{"slug": "tearing-into-chatgpt-s-container-environment", "title": "Tearing into ChatGPT's Container Environment", "summary": "A Hacker News user discovered that ChatGPT's container environment provides 56 CPU cores and 4GB of RAM, prompting an investigation into the infrastructure. The containers run on gVisor, a sandboxed kernel, and the high core count is due to oversubscription shared among containers. This reveals the scale of compute resources OpenAI allocates for AI workloads.", "body_md": "I'd say the Cloud is now the data center, where, boom, you have these at scale. You have such massive resources available. They're readily available, easy to use. I've always viewed that computing follows the Gas Law. It fills the available space. It always keeps expanding and it's really more a statement where ..... how much computing do you want to do? Well, as much as I can afford. How many more simulations do you do, before you send a chip to the fab? Well, as many as I can get done before I think I've exhausted it, but there are always more tests you can run. How many more analysis of your radiology results do you want, running through the AI algorithms to determine if it's carcinomic or not? Well, as many as you can afford, right? Please give me the best results you can, and the list goes on and on and on.\n\nSo to me, computing has always wanted to fill the available space where the available space is often more limited by economics than anything else. And if I make the unit cost of computing lower, and the ability to reach the data ..... Every time there is a dramatic decrease in that, you open up new opportunities for computing. If we use the AI example, hidden Markov models, convoluted neural nets, et cetera, those ideas were around, all of a sudden that got economical, and Cloud made it economical and all of the sudden Cloud made datasets large enough that I could use learning algorithms that before were infeasible, now became feasible as well. So that combination of compute capacity and datasets, allowed AI to start demonstrating meaningful breakthroughs and now it's sort of like, “Wow, how much computing do you need for AI?” Well, the learning algorithms, it's almost unlimited, right? Really, if you give me another thousand GPUs in my GPU farm, I'll use them all. Many of the hardest problems in computing have always demonstrated this characteristic, whether it's weather prediction, whether it's predictive modeling, whether it's computational fluid dynamics, these are n-complexity algorithms that, boy, you can just keep throwing computing at them.\n\n- Pat Gelsinger in his[2019 Oral History with CHM].\n\nA few months back, while randomly browsing old Hacker News submissions,\nI came across a rather interesting submission from Simon Willison\n[\non ChatGPT Containers](https://simonwillison.net/2026/Jan/26/chatgpt-containers/). What immediately caught my attention was not the post itself but one\nof the\n[comment threads on HN](https://news.ycombinator.com/item?id=46772003):\n\nxnx: How much compute do you get in these containers? Could I have it run whisper on an mp3 it downloads?\n\nsimonw: That might work! You would have to figure out how to get Whisper working in there but I'm sure that's possible with a bit of creativity concerning uploading files and maybe running a build with the available C compiler. It appears to have 4GB of RAM and 56 (!?) CPU cores[https://chatgpt.com/share/6977e1f8-0f94-8006-9973-e9fab6d24418]\n\n**56 LPs?? 4GB of RAM??!** One of the users in the thread, named\ntintor, who appears to be ex-OpenAI (O1 reasoning model and code\ninterpreter), briefly mentioned that\n[the cores are shared with other containers](https://news.ycombinator.com/item?id=46772442). However, even with oversubscription, the number seemed baffling. So,\nI decided to further investigate this. In this blog post, we will dive\ninto what their container environment looks like.\n\nBefore we begin, note that all of my chat prompts and their subsequent containerized executions were performed on ChatGPT's paid plan - ChatGPT Plus. They were running GPT 5.5 Extended Thinking.\n\nLet us start by focusing on ChatGPT's container environment. If we ask\nit to run\n[dmesg](https://man7.org/linux/man-pages/man1/dmesg.1.html)\nand report the output of the command, here is what we get:\n\n```\n[    0.000000] Starting gVisor...\n[    0.541400] Checking naughty and nice process list...\n[    0.648754] Mounting deweydecimalfs...\n[    0.661315] Daemonizing children...\n[    1.145987] Searching for needles in stacks...\n[    1.455202] Verifying that no non-zero bytes made their way into /dev/zero...\n[    1.791613] Creating cloned children...\n[    2.209217] Rewriting operating system in Javascript...\n[    2.593286] Checking naughty and nice process list...\n[    2.602568] Letting the watchdogs out...\n[    2.972217] Creating bureaucratic processes...\n[    3.195269] Setting up VFS...\n[    3.254751] Setting up FUSE...\n[    3.723550] Ready!\n```\n\nThis is strong evidence that ChatGPT uses gVisor as a sandboxing\nenvironment for code execution. To better understand gVisor, I encourage\nchecking out\n[Emma Haruka Iwao's 2019 talk](https://youtu.be/Ur0hbW_K66s?si=QDaudvBSQPsbOpVf)\nand Ye Lin's\n[recent blog post](https://blog.yelinaung.com/posts/gvisor/).\nAndrea and Remzi Arpaci-Dusseau (of OSTEP fame) have also\n[\nco-authored a paper on it](https://www.usenix.org/system/files/hotcloud19-paper-young.pdf). Here is what the\n[gVisor team has to say about their product](https://gvisor.dev/blog/2024/09/23/safe-ride-into-the-dangerzone/):\n\ngVisor is a container security solution. ….. An open source project written in Go, gVisor was released in May 2018 by Google under the Apache 2.0 license. It runs on Linux and integrates with all popular container management software, such as Docker, Podman, or Kubernetes. At its core, gVisor is an application kernel that implements a substantial portion of the Linux system call interface. This means gVisor sits between a container and the Linux kernel and plays both roles: from the container's perspective, gVisor acts as a kernel, but from Linux's perspective, gVisor is just a regular application. That means the container can no longer directly interface with the Linux kernel. This is a massive reduction in attack surface.\n\ngVisor manages to pull the above trick with the help of two components:\n\n- Sentry is the component that runs the containerized application. It intercepts every system call that the application makes and reimplements it in Go. As part of this, it may decide to do one or more system calls to the host Linux kernel. However, it's heavily restricted with a strict seccomp filter (that's why system calls like open, socket, or exec are not allowed).\n- Gofer is a component that runs outside the container and is responsible for filesystem operations. The sentry may make I/O requests to the gofer. The gofer will independently validate them, then perform these I/O operations on the container's behalf (that's how the container can read files from the host filesystem, even though open is not allowed from the sentry).\n\nCircling back to the above dmesg output, you might notice that the logs\nappear somewhat nonsensical. Why is that? By default, dmesg is used to\nprint the messages in the kernel ring buffer. The modern interface to\nread this message buffer involves `/dev/kmsg`\n\n. The older\ninterface involved reading from syslog. However, if you ask ChatGPT to\nperform the following command in its container:\n`ls -l /dev/kmsg`\n\n, you will get an\n`\"ls: cannot access '/dev/kmsg': No such file or directory\"`\n\nerror. This is because `/dev/kmsg`\n\nis not present in their\ngVisor sandbox, and the command is likely to fall back to the older\nsyslog method.\n\nSo what happens next? Observe the 103 syscall number\n[\nin gVisor's linux64.go](https://github.com/google/gvisor/blob/master/pkg/sentry/syscalls/linux/linux64.go#L148).\n\n```\n103: syscalls.PartiallySupported(\"syslog\", Syslog, \"Outputs a dummy message for security reasons.\", nil),\n```\n\nThis is Sentry's syscall table that maps gVisor's syscall API to Linux's\ncorresponding syscall numbers. The\n[103 syscall on Linux is syslog](https://chromium.googlesource.com/chromiumos/docs/+/master/constants/syscalls.md), which is getting wired to gVisor's Syslog handler. The code for the\nhandler\n[\nis at sys_syslog.go](https://github.com/google/gvisor/blob/master/pkg/sentry/syscalls/linux/sys_syslog.go#L34), which in turn invokes\n[syslog.go's Log() function](https://github.com/google/gvisor/blob/master/pkg/sentry/kernel/syslog.go#L51), which returns a dummy copy of the syslog.\n\nNext, let us focus on its execution environment and the 56 LP claim. If\nI ask it to run `nproc`\n\nand report the output of the command,\nI get 56. Furthermore, a `cat /proc/cpuinfo`\n\nreturns the\nfollowing:\n\n```\nprocessor       : 0\nvendor_id       : GenuineIntel\ncpu family      : 6\nmodel           : 79\nmodel name      : unknown\nstepping        : unknown\ncpu MHz         : 2294.687\ncache size      : 8192 KB\nphysical id     : 0\nsiblings        : 56\ncore id         : 0\ncpu cores       : 56\napicid          : 0\ninitial apicid  : 0\nfpu             : yes\nfpu_exception   : yes\ncpuid level     : 13\nwp              : yes\nflags           : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 \nclflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm pni pclmulqdq vmx ssse3 fma \ncx16 pcid sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm \n3dnowprefetch fsgsbase bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed adx smap xsaveopt md_clear\nbogomips        : 2294.69\nclflush size    : 64\ncache_alignment : 64\naddress sizes   : 46 bits physical, 48 bits virtual\npower management:\n```\n\nI am only showing the first processor's info. There are 55 processor\ninfos that come after this. However, they all differ only in the\n`processor`\n\n, `core id`\n\n, `apicid`\n\n, and\n`initial apicid`\n\nfields. For the next processor output, these\n4 fields would have the value of 1, and the subsequent processor would\nhave it as 2, and so forth.\n\nThe gVisor code corresponding to this seems to originate from\n[pkg/cpuid/cpuid_amd64.go](https://github.com/google/gvisor/blob/master/pkg/cpuid/cpuid_amd64.go#L312), particularly the `WriteCPUInfoTo`\n\ncall:\n\n```\n// WriteCPUInfoTo is to generate a section of one cpu in /proc/cpuinfo. This is\n// a minimal /proc/cpuinfo, it is missing some fields like \"microcode\" that are\n// not always printed in Linux. Several fields are simply made up.\n```\n\nFrom the code, the `model name`\n\nand `stepping`\n\nare\nhardcoded to \"unknown\". Furthermore, gVisor pretends that all CPUs are\nin the same socket and the `physical id`\n\nis always set to 0.\nThe presence of `fpu`\n\n, `fpu_exception`\n\n, and\n`wp`\n\n(write protect) is also hardcoded to return \"yes\".\nAddress sizes are not read from hardware either; they are predetermined\nto be 46 bits physical, 48 bits virtual. Perhaps the most interesting\none is `cache size`\n\n, which is completely made up at 8192 KB\nwith the comment:\n\n```\n// Pretend the CPU has 8192 KB of cache. Note that real /proc/cpuinfo exposes total L3 cache\n// size on Intel and per-core L2 cache size on AMD (as of Linux 6.1.0), so the value of this\n// field is not really important in practice. Any value that is chosen here will be wrong\n// by an order of magnitude on a significant chunk of x86 machines.\n// 8192 KB is selected because it is a reasonable size that will be effectively usable on\n// lightly loaded machines - most machines have 1-4MB of L3 cache per core.\n```\n\nBut some fields contain real CPUID data. The `vendor id`\n\n,\n`cpu family`\n\n, `model`\n\n, and `flags`\n\nare\nall parsed using `CPUID`\n\n.\n[CPUID](https://en.wikipedia.org/wiki/CPUID) is an\ninstruction that is used to query processor details. Intel concisely\ndescribes this in their\n[Architecture Software Developer Manuals](https://www.intel.com/content/www/us/en/developer/articles/technical/intel-sdm.html):\n\nThis instruction returns the family, model, and (for some processors) a brand string for the processor that executes the instruction. It also indicates the features that are present in the processor and gives information about the processor's caches and TLB. .....\n\nTo obtain processor identification information, a source operand value is placed in the EAX register to select the type of information to be returned. When the CPUID instruction is executed, selected information is returned in the EAX, EBX, ECX, and EDX registers.\n\nTo fetch information from `CPUID`\n\n, WriteCPUInfoTo\n[\ninvokes the following call](https://github.com/google/gvisor/blob/cfb7c0629521099eb14d7bd86e9fbfa47287a640/pkg/cpuid/cpuid_amd64.go#L315):\n\n```\nax, _, _, _ := fs.query(featureInfo)\n```\n\nInside `native_amd64.go`\n\n,\n[the featureInfo constant](https://github.com/google/gvisor/blob/cfb7c0629521099eb14d7bd86e9fbfa47287a640/pkg/cpuid/native_amd64.go#L46)\nis defined as:\n\n```\nfeatureInfo                   cpuidFunction = 0x1               // Returns basic feature bits and processor signature.\n```\n\n`fs.query()`\n\nis a thin wrapper that\n[forwards the call to Native.Query()](https://github.com/google/gvisor/blob/cfb7c0629521099eb14d7bd86e9fbfa47287a640/pkg/cpuid/native_amd64.go#L147):\n\n```\n// Query executes CPUID natively.\n//\n// This implements Function.\n//\n//go:nosplit\nfunc (*Native) Query(in In) Out {\n\tif int(in.Eax) < len(allowedBasicFunctions) && allowedBasicFunctions[in.Eax] {\n\t\treturn native(in)\n\t} else if in.Eax >= uint32(extendedStart) {\n\t\tif l := int(in.Eax - uint32(extendedStart)); l < len(allowedExtendedFunctions) && allowedExtendedFunctions[l] {\n\t\t\treturn native(in)\n\t\t}\n\t}\n\treturn Out{} // All zeros.\n}\n```\n\nIn the above code, `Native.Query()`\n\nchecks whether the\nrequested `CPUID`\n\nis in an allowlist. If allowed, it calls\n`native()`\n\n, whose implementation\n[\nlives in the assembly file native_amd64.s](https://github.com/google/gvisor/blob/cfb7c0629521099eb14d7bd86e9fbfa47287a640/pkg/cpuid/native_amd64.s#L17). In this scenario, the source operand value `0x1`\n\nis put\ninto the `EAX`\n\nregister, and when the\n`CPUID`\n\ninstruction is executed, the selected information is\nreturned in the `EAX`\n\n, `EBX`\n\n, `ECX`\n\n,\nand `EDX`\n\nregisters.\n\n```\nTEXT ·native(SB),NOSPLIT|NOFRAME,$0-24\n\tMOVL arg_Eax+0(FP), AX\n\tMOVL arg_Ecx+4(FP), CX\n\tCPUID\n\tMOVL AX, ret_Eax+8(FP)\n\tMOVL BX, ret_Ebx+12(FP)\n\tMOVL CX, ret_Ecx+16(FP)\n\tMOVL DX, ret_Edx+20(FP)\n\tRET\n```\n\nThe important thing to note here is that gVisor is not fabricating this\ndata, unlike the others. It is running the real\n`CPUID`\n\ninstruction. As for its runtime environment, a\nhypothesis can be made that OpenAI's gVisor is running inside a\ncontainerized environment managed by a hypervisor on a cloud\ninfrastructure. Notice how the `hypervisor`\n\nflag is set in\n`/proc/cpuinfo`\n\n. Microsoft describes\n[\nthis approach to hypervisor discovery\n](https://learn.microsoft.com/en-us/virtualization/hyper-v-on-windows/tlfs/feature-discovery)\nin its docs:\n\nBefore using any hypervisor interfaces, software should first determine whether it's running within a virtualized environment. On x64 platforms that conform to this specification, this is done by executing the CPUID instruction with an input (EAX) value of 1. Upon execution, code should check bit 31 of register ECX (the “hypervisor present bit”). If this bit is set, a hypervisor is present. In a non-virtualized environment, the bit will be clear.\n\nIn such a scenario, the hypervisor would intercept\n`CPUID`\n\nand modify its results. Hypervisors \"can\" present a\nfake CPU model - Chips and Cheese\n[wrote a blog post describing this](https://chipsandcheese.com/p/why-you-cant-trust-cpuid)\nback in 2022. Therefore, it is hard to say whether the underlying\nhardware of the host might be Intel-based or not. The CPU family 6 and\nmodel 79 themselves seem to correspond to the\n[Broadwell CPU family](https://openbenchmarking.org/s/Intel+Xeon+E5-2683+v4). It is rather old, being the\n[fifth generation](https://en.wikipedia.org/wiki/Broadwell_(microarchitecture))\nof the Intel Core processor from 2014. A handy way to find out the\narchitecture correlated with a CPU family is by peeking at the constants\nin Linux's source code at\n[linux/arch/x86/include/asm/intel-family.h](https://github.com/torvalds/linux/blob/master/arch/x86/include/asm/intel-family.h)\n- \"`#define INTEL_BROADWELL_X IFM(6, 0x4F)`\n\n\"\n\nThere is another twist to this tale! When I asked it to run\n`/proc/cpuinfo`\n\n- only a few hours apart -\n*\nI managed to get a completely different set of model numbers and\nflags*:\n\n```\n.....\nvendor_id   : GenuineIntel\ncpu family  : 6\nmodel       : 106\n.....\nsiblings    : 56\ncpu cores   : 56\n.....\nflags       : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov \npat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm pni \npclmulqdq vmx ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt \ntsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm \n3dnowprefetch fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid \nrtm avx512f avx512dq rdseed adx smap clwb avx512cd sha_ni avx512bw avx512vl \nxsaveopt xsavec xgetbv1 xsaves avx512vbmi umip avx512_vbmi2 gfni vaes \nvpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid fsrm \narch_capabilities\n.....\n```\n\nThis CPU model is relatively newer, and is based on the\n[Ice Lake](https://en.wikipedia.org/wiki/Ice_Lake_(microprocessor))\n(Sunny Cove) family from 2019. This indicates that there are multiple\nSKUs in the environment being mapped on a need-to basis. There is\nsomething fascinating we can do here. `CPUID`\n\nis an\nunprivileged instruction, not a syscall, and it executes in ring 3\nwithout trapping the kernel. Therefore, we can create and execute a\nsmall C program that invokes the `CPUID`\n\ninstruction and\nparses various processor details out of it.\n\nHere is the program that I created with the help of Claude - the underlying comments and explanations are my own.\n\n```\n#include <stdio.h>\n#include <stdint.h>\n\nstatic inline void cpuid_count(uint32_t leaf, uint32_t subleaf,\n                               uint32_t *eax, uint32_t *ebx,\n                               uint32_t *ecx, uint32_t *edx) {\n   /* \n       Helper to execute a CPUID instruction.\n      \n       See https://wiki.osdev.org/Inline_Assembly for details on __asm__\n       This is inspired by https://wiki.osdev.org/CPUID#Using_CPUID_from_GCC\n\n       In the below code, a, b, c, d are termed as \"constraints\" and they tell where the C variable\n       should be mapped - in this case, to the registers eax, ebx, ecx, and edx.\n       For a complete list, see https://gcc.gnu.org/onlinedocs/gcc/Machine-Constraints.html\n      \n       As mentioned earlier in the blog, CPUID takes no parameters.\n       Instead, source operand value is placed in the EAX register - the \"leaf\".\n       Sometimes, the ECX register is also used to specify a \"subleaf\" for more detailed queries.\n   */\n   __asm__ __volatile__ (\n       \"cpuid\"\n       : \"=a\"(*eax), \"=b\"(*ebx), \"=c\"(*ecx), \"=d\"(*edx)\n       : \"a\"(leaf), \"c\"(subleaf)\n   );\n}\n\nint main(void) {\n   uint32_t eax, ebx, ecx, edx;\n\n   /* Wiki page is a great reference for CPUID information\n       https://en.wikipedia.org/wiki/CPUID\n   */\n\n   /* Max leaf number that the processor supports is in EAX */\n   cpuid_count(0x0, 0, &eax, &ebx, &ecx, &edx);\n   uint32_t max_leaf = eax;\n   printf(\"Max basic CPUID leaf: 0x%x\\n\", max_leaf);\n\n   /* CPU's manufacturer ID string (12 char ASCII) lives in EBX, EDX, ECX (in that order). */\n   char vendor[13];\n   *(uint32_t*)(vendor + 0) = ebx;\n   *(uint32_t*)(vendor + 4) = edx;\n   *(uint32_t*)(vendor + 8) = ecx;\n   vendor[12] = '\\0';\n   printf(\"Vendor: %s\\n\", vendor);\n\n   /* Leaf 0x1:\n       Claude's intention behind drafting the below function was not fully accurate!\n\n       Here's why - Wikipedia tells me:\n           1. EBX[23:16]:\n               \"Maximum number of addressable IDs for logical processors in this physical package\"\n               What this means is - every LP needs a unique number so that system can address it.\n               This number is its APIC ID. By \"addressable\", it means a way to reach the LP by this ID number.\n               So, the Wiki sentence probably equates to the biggest number of APIC IDs the chip has set aside.\n\n               However, the bits for APIC ID seem to be allocated in powers of 2.\n               So, if the chip has 6 LPs, it would need to allocate 8 APIC IDs (0-7) to be able to address them.\n               So, the value in EBX[23:16] would be 8, not 6. The two reserved APIC IDs would just go unused.\n               Therefore, we can estimate an upper bound on the number of LPs, not the exact number.\n          \n           2. EDX[28]: HTT flag\n               This bit used to be about Hyper-Threading.\n               See this blog: https://www.os2museum.com/wp/htt-means-hyper-threading-right/\n               It says that: \"As of October 2017, Intel now says: HTT:\n               Max APIC IDs reserved field is Valid. A value of 0 for HTT indicates\n               there is only a single logical processor in the package and software\n               should assume only a single APIC ID is reserved. A value of 1 for HTT\n               indicates the value in CPUID.1.EBX[23:16] (the Maximum number of addressable\n               IDs for logical processors in this package) is valid for the package.\"\n   */\n   cpuid_count(0x1, 0, &eax, &ebx, &ecx, &edx);\n   uint32_t logical_per_pkg = (ebx >> 16) & 0xFF;\n   int has_htt = (edx >> 28) & 1;\n   printf(\"\\nLeaf 0x1:\\n\");\n   printf(\"  Max logical processors per package: %u\\n\", logical_per_pkg);\n   printf(\"  HTT flag: %d\\n\", has_htt);\n\n   /* Leaf 0x4: Deterministic Cache Parameters */\n   printf(\"\\nLeaf 0x4 (Cache Topology):\\n\");\n   for (int i = 0; i < 16; i++) {\n       cpuid_count(0x4, i, &eax, &ebx, &ecx, &edx);\n       /* 5 bits for cache type\n           0 is no more caches, 1 is data cache, 2 is instruction cache,\n           3 is unified cache, 4-31 are reserved.\n       */\n       int cache_type = eax & 0x1F;\n       if (cache_type == 0) break;  /* no more caches */\n       int cache_level = (eax >> 5) & 0x7; /* 3 bits for cache level (1,2,3) */\n       /* Same as the APIC ID explanation from above\n           Wiki says: \"Maximum number of addressable IDs for logical processors sharing this cache, minus 1\"\n           So, after +1, this is the maximum number of addressable APIC IDs that tells us\n           the upper bound on the number of LPs sharing this one cache instance.\n           This is not a guarantee on the exact count either.\n       */\n       int max_threads_sharing = ((eax >> 14) & 0xFFF) + 1;\n       /*\n           Wiki says: \"Maximum number of addressable IDs for processor cores in physical package, minus 1\"\n           So, after +1, this is the maximum number of addressable APIC IDs that tells us\n           the upper bound on the count of \"physical cores\" in the package reported by this cache.\n       */\n       int max_cores_in_pkg = ((eax >> 26) & 0x3F) + 1;\n       int line_size = (ebx & 0xFFF) + 1;\n       /* Hierarchically cache is like: \n           Cache contains \"sets\" - groups of cache lines.\n           Each \"set\" contains \"ways\" - the number of lines that can be stored in that set.\n           Each \"way\" contains a cache line of \"line_size\" bytes.\n           Partitions seem to be usually set to 1 - I do not fully understand them!\n           So, total cache size = ways * line_size * sets * partitions.\n       */\n       int partitions = ((ebx >> 12) & 0x3FF) + 1;\n       int ways = ((ebx >> 22) & 0x3FF) + 1;\n       int sets = ecx + 1;\n       int cache_size = ways * partitions * line_size * sets;\n       const char *types[] = {\"Null\",\"Data\",\"Instruction\",\"Unified\"};\n       printf(\"  L%d %s: %d KB, %d-way, shared by %d threads, \"\n              \"max cores in pkg: %d\\n\",\n              cache_level,\n              cache_type < 4 ? types[cache_type] : \"???\",\n              cache_size / 1024, ways,\n              max_threads_sharing, max_cores_in_pkg);\n   }\n\n   /* Leaf 0xB: Extended Topology Enumeration */\n   printf(\"\\nLeaf 0xB (Extended Topology):\\n\");\n   if (max_leaf >= 0xB) {\n       for (int level = 0; level < 8; level++) {\n           cpuid_count(0xB, level, &eax, &ebx, &ecx, &edx);\n           int level_type = (ecx >> 8) & 0xFF; /* 1 is SMT, 2 is Core */\n           if (level_type == 0) break;  /* 0 is invalid level */\n           /* It helps decode an x2APIC ID into (thread, core, package) coordinates\n           for the LP running the CPUID instruction.\n          \n           NOTE:\n           For a full proof solution - we have to enumerate the whole machine:\n           we can pin to each CPU (using say taskset) - then run CPUID on each to collect its\n           x2APIC ID - then use these shifts to slice the IDs and count distinct\n           cores/packages. I could not fully pull this off during my experimentation.\n           */\n           int shift = eax & 0x1F;\n           /* From Wiki - \"the number of logical processors at the level queried is returned in EBX\"\n               For example, at SMT level, threads per core might be 2,\n               at core level, LPs per package might be 16.\n           */\n           int num_logical = ebx & 0xFFFF;\n           /* x2apic_id is giving x2APIC ID of whichever LP is running the code rn\n               Wiki - \"leaf Bh will return different values in EDX depending on which\n               logical processor the CPUID instruction runs; the value returned in\n               EDX is actually the x2APIC id of the logical processor.\"\n           */\n           uint32_t x2apic_id = edx;\n           const char *level_names[] = {\"Invalid\",\"SMT\",\"Core\"};\n           printf(\"  Level %d: type=%s(%d), logical_processors=%d, \"\n                  \"bit_shift=%d, x2APIC_ID=%u\\n\",\n                  level,\n                  level_type < 3 ? level_names[level_type] : \"Unknown\",\n                  level_type, num_logical, shift, x2apic_id);\n       }\n   } else {\n       printf(\"  Not supported (max leaf < 0xB)\\n\");\n   }\n\n   /* Leaf 0x1F: V2 Extended Topology (newer Intel)\n       Not much useful info for this blog\n   */\n   printf(\"\\nLeaf 0x1F (V2 Extended Topology):\\n\");\n   if (max_leaf >= 0x1F) {\n       for (int level = 0; level < 8; level++) {\n           cpuid_count(0x1F, level, &eax, &ebx, &ecx, &edx);\n           int level_type = (ecx >> 8) & 0xFF;\n           if (level_type == 0) break;\n           int shift = eax & 0x1F;\n           int num_logical = ebx & 0xFFFF;\n           printf(\"  Level %d: type=%d, logical_processors=%d, \"\n                  \"bit_shift=%d\\n\",\n                  level, level_type, num_logical, shift);\n       }\n   } else {\n       printf(\"  Not supported (max leaf < 0x1F)\\n\");\n   }\n\n   /* Leaf 0x40000000: Hypervisor Detection\n       From Wiki:\n       \"When the CPUID instruction is executed under Intel VT-x or AMD-v virtualization,\n       it will be intercepted by the hypervisor, enabling the hypervisor to return CPUID feature\n       flags that differ from those of the underlying hardware.\n       .....\n       For leaf 40000000h, the hypervisor is expected to return the index of the highest supported\n       hypervisor CPUID leaf in EAX, and a 12-character hypervisor ID string in EBX,ECX,EDX (in that order).\n       \"\n   */\n   printf(\"\\nLeaf 0x40000000 (Hypervisor ID):\\n\");\n   cpuid_count(0x40000000, 0, &eax, &ebx, &ecx, &edx);\n   if (eax != 0 || ebx != 0 || ecx != 0 || edx != 0) {\n       char hv_id[13];\n       *(uint32_t*)(hv_id+0) = ebx;\n       *(uint32_t*)(hv_id+4) = ecx;\n       *(uint32_t*)(hv_id+8) = edx;\n       hv_id[12] = '\\0';\n       printf(\"  Hypervisor ID: %s\\n\", hv_id);\n       printf(\"  Max hypervisor leaf: 0x%x\\n\", eax);\n   } else {\n       printf(\"  All zeros (blocked by gVisor allowlist or \"\n              \"no hypervisor leaf)\\n\");\n   }\n\n   return 0;\n}\n```\n\nOn executing this once in the presence of CPU family 6 and model 106, the ChatGPT container provided the following output:\n\n```\nMax basic CPUID leaf: 0x1b\nVendor: GenuineIntel\n\nLeaf 0x1:\n  Max logical processors per package: 32\n  HTT flag: 1\n\nLeaf 0x4 (Cache Topology):\n  L1 Data: 48 KB, 12-way, shared by 2 threads, max cores in pkg: 16\n  L1 Instruction: 32 KB, 8-way, shared by 2 threads, max cores in pkg: 16\n  L2 Unified: 1280 KB, 20-way, shared by 2 threads, max cores in pkg: 16\n  L3 Unified: 49152 KB, 12-way, shared by 32 threads, max cores in pkg: 16\n\nLeaf 0xB (Extended Topology):\n  Level 0: type=SMT(1), logical_processors=2, bit_shift=1, x2APIC_ID=38\n  Level 1: type=Core(2), logical_processors=32, bit_shift=5, x2APIC_ID=38\n\nLeaf 0x1F (V2 Extended Topology):\n  Not supported (max leaf < 0x1F)\n\nLeaf 0x40000000 (Hypervisor ID):\n  Hypervisor ID: Microsoft Hv\n  Max hypervisor leaf: 0x4000000b\n```\n\n*There is a tremendous amount of information to unpack here!*\n\"`Microsoft Hv`\n\n\" is the Hyper-V signature. This strongly\nindicates that OpenAI is running their container environment on Azure.\nBased on\n[\nthis SPEC CPU result](https://spec.org/cpu2017/results/res2021q2/cpu2017-20210608-26983.html), the cache geometry itself seems to match the Ice Lake (Sunny Cove)\nfamily - *Intel Xeon Gold 6338*. The above\n`CPUID`\n\noutput shows us 32 LPs and 2 threads per core, which\nmeans 16 physical cores per package. It is important to stress that this\n`CPUID`\n\nprogram only ran on one LP; therefore, it only\nreported the topology of its own package (32 LPs). We cannot directly\ntell the total number of LPs in the host, or tell how many packages\nexist from the above output.\n\nOne might also wonder, for this cache geometry, Intel Xeon Gold 6338 is supposed to have 32 cores per socket / 64 threads, so why are we only seeing 32 LPs? I believe the answer possibly lies with the container host allocation. The container host was handed 32 of those threads (16 cores worth). Therefore, it is not getting a whole socket, but a slice, because that is the quota the hypervisor carved out for the VM. In this case, the hypervisor does not seem to resize the cache geometry (I am not sure what the industry practice is) - which is why we see the real chip's 48 MB L3, 1.25 MB L2, and 48 KB L1d.\n\nNote that the gVisor codepath to determine the number of cores for\n`/proc/cpuinfo`\n\n[comes from tasks.go](https://github.com/google/gvisor/blob/cfb7c0629521099eb14d7bd86e9fbfa47287a640/pkg/sentry/fsimpl/proc/tasks.go#L284):\n\n``` js\nfunc cpuInfoData(k *kernel.Kernel) string {\n\tfeatures := k.FeatureSet()\n\tvar buf bytes.Buffer\n\tfor i, max := uint(0), k.ApplicationCores(); i < max; i++ {\n\t\tfeatures.WriteCPUInfoTo(i, max, &buf)\n\t}\n\treturn buf.String()\n}\n```\n\nThis in turn seems to\n[rely on loader.go's](https://github.com/google/gvisor/blob/cfb7c0629521099eb14d7bd86e9fbfa47287a640/runsc/boot/loader.go#L157)\n`ApplicationCores`\n\ninitialization:\n\n```\n\t// applicationCores is the number of CPU cores gVisor reports to user\n\t// applications.\n\tapplicationCores int\n....\n\tl.root = containerInfo{\n\t\tcid:                args.ID,\n\t\tcontainerName:      containerName,\n\t\tconf:               args.Conf,\n\t\tspec:               args.Spec,\n\t\tgoferMountConfs:    args.GoferMountConfs,\n\t\tnvidiaHostSettings: args.NvidiaHostSettings,\n\t\tapplicationCores:   args.NumCPU,\n\t}\n```\n\nThe NumCPU itself\n[is initialized as](https://github.com/google/gvisor/blob/cfb7c0629521099eb14d7bd86e9fbfa47287a640/runsc/boot/loader.go#L529):\n\n```\n\tif args.NumCPU == 0 {\n\t\targs.NumCPU = runtime.NumCPU()\n\t}\n```\n\nGiven all that we have seen so far, there are two plausible explanations for the original 56 LP finding:\n\n`loader.go`\n\nabove) to the gVisor sandbox.\n`runtime.NumCPU()`\n\n- which would show\n`nproc=56`\n\nwhen gVisor is running inside a container host\nthat has 56 LPs in its cpuset, for instance, with two NUMA nodes. And\ngiven that the CPUID program only ran on one LP, it only reported the\ntopology of its own package; the topology could consist of a package\nthat contributes 32 LPs and another that contributes 24 LPs.\nAcross multiple re-runs of the above C code, I have not stumbled upon a 24 LP package - so I cannot say that it appears to be the case. To quantify how many actual cores of compute are available, we can ask ChatGPT to run a simple CPU parallelism benchmark as follows:\n\n```\n#include <stdio.h>\n#include <stdlib.h>\n#include <string.h>\n#include <pthread.h>\n#include <time.h>\n#include <unistd.h>\n\n#define ITERATIONS 500000000L\n\nstatic void *burn(void *arg) {\n   (void)arg;\n   volatile long x = 0;\n   for (long i = 0; i < ITERATIONS; i++) {\n       x += i;\n   }\n   return NULL;\n}\n\nstatic double now(void) {\n   struct timespec ts;\n   clock_gettime(CLOCK_MONOTONIC, &ts);\n   return ts.tv_sec + ts.tv_nsec * 1e-9;\n}\n\nint main(void) {\n   /*\n       Reads from /sys/devices/system/cpu/online\n       See: https://github.com/bminor/glibc/blob/master/sysdeps/unix/sysv/linux/getsysstats.c#L214\n       Falls back to /proc/stat and sched_getaffinity\n   */\n   int max_cores = sysconf(_SC_NPROCESSORS_ONLN);\n\n   /* Single-threaded busy loop for baseline time */\n   double t0 = now();\n   burn(NULL);\n   double single = now() - t0;\n   printf(\"Cores reported: %d\\n\", max_cores);\n   printf(\"Single-thread:  %.2fs\\n\\n\", single);\n\n   /* Spawn across 1, 2, 4, ..., max_cores */\n   int counts[32];\n   int n = 0;\n   for (int c = 1; c <= max_cores; c *= 2)\n       counts[n++] = c;\n   if (counts[n-1] != max_cores)\n       counts[n++] = max_cores;\n\n   for (int t = 0; t < n; t++) {\n       int nthreads = counts[t];\n       pthread_t *tids = malloc(nthreads * sizeof(pthread_t));\n\n       double t0 = now();\n       for (int i = 0; i < nthreads; i++)\n           pthread_create(&tids[i], NULL, burn, NULL);\n       for (int i = 0; i < nthreads; i++)\n           pthread_join(tids[i], NULL);\n       double wall = now() - t0;\n\n       /*\n       Calculates the speedup -\n       if N threads each doing the same amount of work finished in the same time as single thread,\n       then the parallelism is N. If it is much less than N, then we can say that we have much less\n       real concurrncy than what the OS is reporting.\n       */\n       double effective = (single * nthreads) / wall;\n       printf(\"%2d threads: wall=%7.2fs  effective_parallelism=%.1f\\n\",\n              nthreads, wall, effective);\n\n       free(tids);\n   }\n\n   return 0;\n}\n```\n\nIn the above code, we start by running a single-threaded busy loop (with 500M iterations) to establish a baseline time. Next, we spawn the same busy loop workload across 1, 2, 4, 8, … up to max threads. If N threads are each doing the same amount of work and finish in the same time as a single thread, then the parallelism is N. If it is much less than N, then we can say that we have less real concurrency than what the OS is reporting.\n\nChatGPT compiled the code as\n`cc -O2 -pthread /tmp/cpu_burn.c -o /tmp/cpu_burn`\n\nand\nproduced the following output:\n\n```\nCores reported: 56\nSingle-thread: 0.92s\n\n1 threads: wall= 0.92s effective_parallelism=1.0\n2 threads: wall= 0.92s effective_parallelism=2.0\n4 threads: wall= 0.94s effective_parallelism=3.9\n8 threads: wall= 2.02s effective_parallelism=3.6\n16 threads: wall= 3.85s effective_parallelism=3.8\n32 threads: wall= 8.25s effective_parallelism=3.6\n56 threads: wall= 14.82s effective_parallelism=3.5\n```\n\nNote that I ran the above code across multiple days and observed similar\nresults. From the output attached above, the pattern is clear. For up to\n4 threads, the wall time stays flat at around 0.92-0.94 seconds,\nindicative of true parallel execution. However, at 8 threads and beyond,\nthe wall time jumps to 2.02+ seconds. This suggests that the system only\nhas about ~4 actual cores available, and beyond that point, the system\nis contending for resources. This seems to suggest that there might be a\nhard CPU quota in place. As a point of comparison, here is the output\nfrom my MacBook Pro running an\n[M2 Pro chip](https://en.wikipedia.org/wiki/Apple_M2):\n\n```\n[8:16:39][hello-world] cc -O2 -pthread testing2.c  -o testing2\n[8:17:06][hello-world] ./testing2\nCores reported: 12\nSingle-thread:  0.17s\n\n 1 threads: wall=   0.15s  effective_parallelism=1.1\n 2 threads: wall=   0.15s  effective_parallelism=2.2\n 4 threads: wall=   0.17s  effective_parallelism=4.0\n 8 threads: wall=   0.18s  effective_parallelism=7.4\n12 threads: wall=   0.26s  effective_parallelism=7.7\n[8:17:10][hello-world]\n```\n\nThe 12-core M2 Pro CPU has 8 performance cores and 4 efficiency cores. Therefore, up to 8 threads, the scaling is nearly perfect, which indicates that all the threads land on the fast performance cores. Then at 12 threads, the wall time jumps to 0.26 seconds and the speedup remains at 7.7 < 8 - indicating that the efficiency cores become highly active at that point.\n\n*Let us now shift our focus to the 4 GB RAM argument.* To answer\nthis, we start by probing its environment. Asking ChatGPT to execute\n`free -h`\n\ngets us the following output:\n\n```\ntotal used free shared buff/cache available\nMem: 4.0Gi 703Mi 3.3Gi 0B 255Mi 3.3Gi\nSwap: 0B 0B 0B\n```\n\nMoreover, `cat /proc/meminfo`\n\nshows the following:\n\n```\nMemTotal: 4194304 kB\nMemFree: 3475112 kB\nMemAvailable: 3475112 kB\nBuffers: 0 kB\nCached: 260188 kB\nSwapCache: 0 kB\nActive: 589084 kB\nInactive: 130096 kB\nActive(anon): 458992 kB\nInactive(anon): 0 kB\nActive(file): 130092 kB\nInactive(file): 130096 kB\nUnevictable: 0 kB\nMlocked: 0 kB\nSwapTotal: 0 kB\nSwapFree: 0 kB\nDirty: 0 kB\nWriteback: 0 kB\nAnonPages: 458992 kB\nMapped: 260188 kB\nShmem: 0 kB\n```\n\nThe headline is that the memory limit is exactly 4 GiB\n(4194304/1024/1024)! To understand gVisor's memory model better, I found\n[their Resource Model guide](https://gvisor.dev/docs/architecture_guide/resources/)\nto be a good start:\n\nThe host is able to manage physical memory using regular means (e.g. tracking working sets, reclaiming and swapping under pressure). The Sentry lazily populates host mappings for applications, and allow the host to demand-page those regions, which is critical for the functioning of those mechanisms.\n\nThe Sentry implements its own memory management, including demand-paging and a Sentry internal page cache for files that cannot be used natively. A single memfd backs all application memory. .....\n\nTherefore, memory usage statistics within the sandbox (e.g. via proc) are approximations. The Sentry maintains an internal breakdown of memory usage, and can collect accurate information but only through a relatively expensive API call. .....\n\nSentry's memory management documentation describes the working in\ngreater detail. It has this to say as to\n[\nwhy gVisor needs memfd at all](https://github.com/google/gvisor/blob/master/pkg/sentry/mm/README.md):\n\nWhen the application first incurs a page fault on ..... address, the host kernel delivers information about the page fault to the sentry in a platform-dependent manner, and the sentry handles the fault ..... The sentry allocates memory to store the contents of the required part of the file, and copies file contents to the allocated memory. However, since the sentry is implemented atop a host kernel, it does not configure mappings to physical memory directly. Instead, mappable \"memory\" in the sentry is represented by a host file descriptor and offset, since ..... this is the memory mapping primitive provided by the host kernel. In general, memory is allocated from a temporary host file using the pgalloc package.\n\nIntuitively, say an application wants to read a mapped file for the\nfirst time. To do this, it may need to get the file's contents from\ndisk. However, disk IO can be slow, and many processes might be mapping\nthe same file. For this, the Linux kernel maintains a filemap (also\nknown as a\n[page cache](https://bjohnson.lmu.build/cmsi387web/week07.html)), which stores physical pages in memory holding disk contents. Now, if\na new process wants to map a file that is already mapped, it can reuse\nthe same physical pages. To do this, the kernel creates a\n[\nPage Table Entry\n](https://docs.kernel.org/mm/page_tables.html)\n(PTE) in the new process' page tables pointing to the already mapped\npage frame. Unfortunately, gVisor cannot configure mappings to physical\nmemory directly because it is not a kernel; it is a mere regular program\nrunning in userspace.\n\nTo mitigate this limitation, Sentry creates and manages a memory mapped\nfile (`memfd`\n\n). This ties back to what the Resource Model\nguide mentioned - \"A single memfd backs all application memory\". When an\napplication running in a gVisor environment needs memory, the Sentry\nallocates an offset from this `memfd`\n\nand asks the host to\nmap it into the application's address space using `mmap`\n\n. The\nhost then handles allocating physical memory and installing page table\nentries.\n\nSo how does this connect to the 4 GiB memory number? My understanding is\nthat the container runtime sets the memory limit - something like \"`docker run`\n\n\" with\n[ --memory=4g](https://docs.docker.com/engine/containers/resource_constraints/). During sandbox creation, gVisor's\n\n`runsc`\n\nreads this limit\nand passes it as a\n`--total-memory`\n\nargument\n\n```\n\tf.Uint64Var(&b.totalMem, \"total-memory\", 0, \"sets the initial amount of total memory to report back to the container\")\n```\n\nThis value then flows into\n[runsc/boot/loader.go](https://github.com/google/gvisor/blob/cfb7c0629521099eb14d7bd86e9fbfa47287a640/runsc/boot/loader.go#L665), where both the minimum and maximum memory are pinned to the same\nnumber:\n\n```\nif args.TotalMem > 0 {\n\t\t// Adjust the total memory returned by the Sentry so that applications that\n\t\t// use /proc/meminfo can make allocations based on this limit.\n\t\tusage.MinimumTotalMemoryBytes = args.TotalMem\n\t\tusage.MaximumTotalMemoryBytes = args.TotalMem\n\t\tlog.Infof(\"Setting total memory to %.2f GB\", float64(args.TotalMem)/(1<<30))\n\t}\n```\n\nThis is the ceiling. Before allocating from `memfd`\n\n, gVisor's\nmemory allocator checks against this limit to deal with potential memory\npressure. The entire `/proc/meminfo`\n\noutput itself is\nconstructed by a `Generate()`\n\ncall in\n[pkg/sentry/fsimpl/proc/tasks_files.go](https://github.com/google/gvisor/blob/cfb7c0629521099eb14d7bd86e9fbfa47287a640/pkg/sentry/fsimpl/proc/tasks_files.go#L272):\n\n```\nfunc (*meminfoData) Generate(ctx context.Context, buf *bytes.Buffer) error {\n\tmf := kernel.KernelFromContext(ctx).MemoryFile()\n....\n\tsnapshot, totalUsage := usage.MemoryAccounting.Copy()\n\ttotalSize := usage.TotalMemory(mf.TotalSize(), totalUsage)\n\tanon := snapshot.Anonymous + snapshot.Tmpfs\n\tfile := snapshot.PageCache + snapshot.Mapped\n\t// We don't actually have active/inactive LRUs, so just make up numbers.\n\tactiveFile := (file / 2) &^ (hostarch.PageSize - 1)\n\tinactiveFile := file - activeFile\n\n\tfmt.Fprintf(buf, \"MemTotal:       %8d kB\\n\", totalSize/1024)\n\tmemFree := totalSize - totalUsage\n\tif memFree > totalSize {\n\t\t// Underflow.\n\t\tmemFree = 0\n\t}\n\tdirty, writeback := usage.DirtyMemoryAccounting.Copy()\n\t// We use MemFree as MemAvailable because we don't swap.\n\t// TODO(rahat): When reclaim is implemented the value of MemAvailable\n\t// should change.\n\tfmt.Fprintf(buf, \"MemFree:        %8d kB\\n\", memFree/1024)\n\tfmt.Fprintf(buf, \"MemAvailable:   %8d kB\\n\", memFree/1024)\n\tfmt.Fprintf(buf, \"Buffers:               0 kB\\n\") // memory usage by block devices\n\tfmt.Fprintf(buf, \"Cached:         %8d kB\\n\", (file+snapshot.Tmpfs)/1024)\n\t// Emulate a system with no swap, which disables inactivation of anon pages.\n\tfmt.Fprintf(buf, \"SwapCache:             0 kB\\n\")\n....\n```\n\nNotice the `TotalMemory()`\n\ncall used to fetch the\n`totalSize`\n\n! Since `loader.go`\n\npinned both\n`MinimumTotalMemoryBytes`\n\nand\n`MaximumTotalMemoryBytes`\n\nto the same value, the\n`TotalMemory()`\n\ncall returns exactly that value.\n\n*So, can we push the ChatGPT container to its memory limit?* Yes,\nwe certainly can! Here is a simple benchmarking program I worked on with\nClaude for this:\n\n```\n#include <stdio.h>\n#include <stdlib.h>\n#include <string.h>\n\n#define CHUNK_SIZE (64 * 1024 * 1024)\n\nint main(void) {\n    size_t total = 0;\n    int chunk_num = 0;\n\n    while (1) {\n        void *p = malloc(CHUNK_SIZE);\n        if (!p) {\n            printf(\"malloc failed after %zu MB\\n\", total / (1024 * 1024));\n            break;\n        }\n        memset(p, 0xAA, CHUNK_SIZE);\n        total += CHUNK_SIZE;\n        chunk_num++;\n        printf(\"Allocated chunk %d: %zu MB total\\n\", chunk_num, total / (1024 * 1024));\n    }\n\n    return 0;\n}\n```\n\nIn the above code, we keep requesting a chunk of 64 MiB memory in an\ninfinite loop. Then, we invoke `memset`\n\n, filling every byte\nof the chunk with `0xAA`\n\n. This forces the pages to be\nfaulted, which triggers demand paging, and real physical memory gets\ncommitted to it. When we ask ChatGPT to compile and run this code in\ntheir container environment, we see:\n\n```\ngcc /mnt/data/stress_mem.c -o /mnt/data/stress_mem\n\nAllocated chunk 1: 64 MB total\nAllocated chunk 2: 128 MB total\nAllocated chunk 3: 192 MB total\nAllocated chunk 4: 256 MB total\n.....\nAllocated chunk 54: 3456 MB total\nAllocated chunk 55: 3520 MB total\nwaiting on pid 2115: waiting on PID 2115 in sandbox \"15106c3c82eff48f6b3632901190e00fbfdf20999be8c65c06fba1a74007f5de\": \nurpc method \"containerManager.WaitPID\" failed: EOF\n.....\nfailed with status 128.\n```\n\n*The process died at 3520 MB!* What's fascinating is that the\nsandbox environment crashed and the entire file system was wiped too,\n[likely due to an OOM kill](https://www.digitalocean.com/community/questions/getting-the-following-error-waiting-on-pid-xxx-in-sandbox-xxxxx-sandbox-urpc-method-containermanager-waitpid-failed-eof). ChatGPT validates this in its thinking\nmessages:\n\nbash -lc echo alive; ls -l /mnt/data/stress_mem.runlog /mnt/data/stress_mem 2>&1 || true; tail -n 10 /mnt/data/stress_mem.runlog 2>/dev/null | cat -vet\n\nalive\n\nls: cannot access '/mnt/data/stress_mem.runlog': No such file or directory\n\nls: cannot access '/mnt/data/stress_mem': No such file or directory\n\nInvestigating potential container crash and command failure: The tool failed with an EOF error, possibly due to the container crashing or restarting. I'll check if the file exists and inspect the error status (128). The stress process might've caused the container to be killed. I need to determine whether the container survived and try some commands to investigate further.\n\nInvestigating container reset and process failure: It seems the container reset, causing file loss. The process output was captured until an EOF occurred after chunk 55 .....\n\nIt was funny to see ChatGPT's own reasoning get confused about this; it performed a re-run only to see this exact issue pop up again. This concretely validates that the 4 GB limit is real and enforceable.\n\nSo, in summary, ChatGPT containers have a pretty nifty compute capacity\nfor a $20 price point - ~4 effective LPs and 4 GB total RAM (~3.5 GB\navailable after sandbox overhead). One could make a strawman argument\nhere saying that a 2 LP and 4 GB RAM VPS\n[on Linode costs $0.07/hr](https://www.akamai.com/cloud/pricing#tabs-270b40e919-item-8532f0ffbe-tab). However, an important point to note is that their container\nenvironment design philosophy seems to mimic a \"*bursty-traffic*\"\npattern. They expect a significant delay between two successive\ncontainer runs per user per session - as it is likely that the user will\npause and evaluate the previous runtime response. This can be seconds or\neven a few minutes. It helps them reallocate their compute capacity\nelsewhere, reducing the stress on their system. Not to mention the fact\nthat according to their own report \"[How People Use ChatGPT](https://cdn.openai.com/pdf/a253471f-8260-40c6-a2cc-aa93fe9f142e/economic-research-chatgpt-usage-paper.pdf)\" from September 2025 - only 5-8% of their users use ChatGPT for\ntechnical help (3% mathematical calculation, 0.4% data analysis, and\n4.2% programming). This includes people who use it for common use cases\nsuch as code writing, technical information, and getting debugging\nsteps. OpenAI's free non-login account does not have a container\nenvironment; however, Simon Willison did mention that\n[\ntheir free sign-in account\n](https://news.ycombinator.com/item?id=46772159)\nbriefly showed the container instance working.\n\nPurely out of fun, I performed some napkin math to calculate the compute\nrequired to support their \"paid\" users - however, I would strongly take\nthese numbers with a grain of salt.\n*Wild wild west territory ahead*!\n\n*\nAs of June 6, 2026, ChatGPT has\naround 50 million paid subscribers. OpenAI's paper reports that ChatGPT saw 18B messages/week from 700M\nweekly users in July 2025. That would be 18B/700M/7days or 3.67\nmessages per weekly active user per day. With around 50 million paying\nusers, a technical help share of 5-8%, and assuming that 20% of those\ntechnical help messages require a fresh container start, the math for\ncontainer starts per day would be 3.67 * (5 to 8)/100 * 50 * 20/100 or\n1.84M to 2.93M container starts per day. Assuming a 5-minute average\ncontainer lifetime, that implies about (1.84M to 2.93M)/86400 * 5 * 60\nor 6,388 to 10,173 concurrent container environments. Even if the\ntechnical users are 2x as active, the estimate rises to about ~20k\nconcurrent container environments.\n*\n\nThe advent of local tool-calling and agentic CLI tools like Claude Code might have prevented the use of web-based containers from exploding too.\n\nAnd that's it folks! Truth be told, the source material I have used for this blog is only about half of what I wanted to cover. There's a lot more - particularly on ChatGPT's internal container process architecture itself and its comparison with Claude. Maybe that's something for a future blog post! Nevertheless, I would like to end this blog with a little treat - I ran some miscellaneous performance benchmarks on the ChatGPT container (CPU family 6 and model 106).\n\n*openssl speed -evp aes-256-gcm*\n\n```\nDoing AES-256-GCM ops for 3s on 16 size blocks: 7850484 AES-256-GCM ops in 2.76s\nDoing AES-256-GCM ops for 3s on 64 size blocks: 7425164 AES-256-GCM ops in 2.76s\nDoing AES-256-GCM ops for 3s on 256 size blocks: 6474438 AES-256-GCM ops in 2.74s\nDoing AES-256-GCM ops for 3s on 1024 size blocks: 4320136 AES-256-GCM ops in 2.77s\nDoing AES-256-GCM ops for 3s on 8192 size blocks: 1105169 AES-256-GCM ops in 2.78s\nDoing AES-256-GCM ops for 3s on 16384 size blocks: 621248 AES-256-GCM ops in 2.79s\nversion: 3.5.5\nbuilt on: Fri Apr  3 10:05:32 2026 UTC\noptions: bn(64,64)\ncompiler: gcc -fPIC -pthread -m64 -Wa,--noexecstack -Wall -fzero-call-used-regs=used-gpr -Wa,--noexecstack -g -O2 -Werror=implicit-function-declaration -ffile-prefix-map=/build/reproducible-path/openssl-3.5.5=. -fstack-protector-strong -fstack-clash-protection -Wformat -Werror=format-security -fcf-protection -DOPENSSL_USE_NODELETE -DL_ENDIAN -DOPENSSL_PIC -DOPENSSL_BUILDING_OPENSSL -DZLIB -DZSTD -DNDEBUG -Wdate-time -D_FORTIFY_SOURCE=2\nCPUINFO: OPENSSL_ia32cap=0xfeda32235f8bffff:0x00000000d09f2fb9:0x0000000000000400:0x0000000000000000:0x0000000000000000\nThe 'numbers' are in 1000s of bytes per second processed.\ntype             16 bytes     64 bytes    256 bytes   1024 bytes   8192 bytes  16384 bytes\nAES-256-GCM      45510.05k   172177.72k   604911.00k  1597046.67k  3256670.66k  3648217.65k\n```\n\n*openssl speed -evp sha256*\n\n```\nDoing sha256 ops for 3s on 16 size blocks: 6709147 sha256 ops in 2.78s\nDoing sha256 ops for 3s on 64 size blocks: 4220561 sha256 ops in 2.78s\nDoing sha256 ops for 3s on 256 size blocks: 2170586 sha256 ops in 2.78s\nDoing sha256 ops for 3s on 1024 size blocks: 753920 sha256 ops in 2.78s\nDoing sha256 ops for 3s on 8192 size blocks: 106457 sha256 ops in 2.78s\nDoing sha256 ops for 3s on 16384 size blocks: 54091 sha256 ops in 2.78s\nversion: 3.5.5\nbuilt on: Fri Apr  3 10:05:32 2026 UTC\noptions: bn(64,64)\ncompiler: gcc -fPIC -pthread -m64 -Wa,--noexecstack -Wall -fzero-call-used-regs=used-gpr -Wa,--noexecstack -g -O2 -Werror=implicit-function-declaration -ffile-prefix-map=/build/reproducible-path/openssl-3.5.5=. -fstack-protector-strong -fstack-clash-protection -Wformat -Werror=format-security -fcf-protection -DOPENSSL_USE_NODELETE -DL_ENDIAN -DOPENSSL_PIC -DOPENSSL_BUILDING_OPENSSL -DZLIB -DZSTD -DNDEBUG -Wdate-time -D_FORTIFY_SOURCE=2\nCPUINFO: OPENSSL_ia32cap=0xfeda32235f8bffff:0x00000000d09f2fb9:0x0000000000000400:0x0000000000000000:0x0000000000000000\nThe 'numbers' are in 1000s of bytes per second processed.\ntype             16 bytes     64 bytes    256 bytes   1024 bytes   8192 bytes  16384 bytes\nsha256           38613.80k    97163.99k   199881.30k   277702.91k   313703.51k   318786.67k\n```\n\n*openssl speed rsa2048* - this one kept timing out! ChatGPT\noptimized the timeout duration and got the following output:\n\n```\nDoing 2048 bits private rsa sign ops for 10s: 10786 2048 bits private RSA sign ops in 9.25s\nDoing 2048 bits public rsa verify ops for 10s: 369602 2048 bits public RSA verify ops in 9.24s\nDoing 2048 bits public rsa encrypt ops for 10s: 192752 2048 bits public RSA encrypt ops in 9.25s\nDoing 2048 bits private rsa decrypt ops for 10s: 10452 2048 bits private RSA decrypt ops in 9.24s\nDoing rsa2048 keygen ops for 10s: 130 rsa2048 KEM keygen ops in 9.33s\nDoing rsa2048 encaps ops for 10s: 196362 rsa2048 KEM encaps ops in 9.41s\nDoing rsa2048 decaps ops for 10s: 10547 rsa2048 KEM decaps ops in 9.24s\nDoing rsa2048 keygen ops for 10s: 121 rsa2048 signature keygen ops in 9.43s\nDoing rsa2048 signs ops for 10s: 10879 rsa2048 signature sign ops in 9.25s\nDoing rsa2048 verify ops for 10s: 374018 rsa2048 signature verify ops in 9.26s\nversion: 3.5.5\nbuilt on: Fri Apr  3 10:05:32 2026 UTC\noptions: bn(64,64)\ncompiler: gcc -fPIC -pthread -m64 -Wa,--noexecstack -Wall -fzero-call-used-regs=used-gpr -Wa,--noexecstack -g -O2 -Werror=implicit-function-declaration -ffile-prefix-map=/build/reproducible-path/openssl-3.5.5=. -fstack-protector-strong -fstack-clash-protection -Wformat -Werror=format-security -fcf-protection -DOPENSSL_USE_NODELETE -DL_ENDIAN -DOPENSSL_PIC -DOPENSSL_BUILDING_OPENSSL -DZLIB -DZSTD -DNDEBUG -Wdate-time -D_FORTIFY_SOURCE=2\nCPUINFO: OPENSSL_ia32cap=0xfeda32235f8bffff:0x00000000d09f2fb9:0x0000000000000400:0x0000000000000000:0x0000000000000000\n                   sign    verify    encrypt   decrypt   sign/s verify/s  encr./s  decr./s\nrsa  2048 bits 0.000858s 0.000025s 0.000048s 0.000884s   1166.1  40000.2  20838.1   1131.2\n                               keygen    encaps    decaps keygens/s  encaps/s  decaps/s\n                    rsa2048 0.071769s 0.000048s 0.000876s      13.9   20867.4    1141.5\n                               keygen     signs    verify keygens/s    sign/s  verify/s\n                    rsa2048 0.077934s 0.000850s 0.000025s      12.8    1176.1   40390.7\n```\n\n*dd if=/dev/zero of=/mnt/data/ddtest bs=1024k count=1024\nconv=fdatasync*\n- See -\n[What does the conv-fdatasync do in dd command in linux?](https://stackoverflow.com/questions/68150702/what-does-the-conv-fdatasync-do-in-dd-command-in-linux)\n\n```\n1024+0 records in\n1024+0 records out\n1073741824 bytes (1.1 GB, 1.0 GiB) copied, 3.01803 s, 356 MB/s\n```\n\n*dd if=/mnt/data/ddtest of=/dev/null bs=1024k count=1024*\n\n```\n1024+0 records in\n1024+0 records out\n1073741824 bytes (1.1 GB, 1.0 GiB) copied, 0.331456 s, 3.2 GB/s\n```\n\n", "url": "https://wpnews.pro/news/tearing-into-chatgpt-s-container-environment", "canonical_source": "https://pncnmnp.github.io/blogs/chatgpt-containers.html", "published_at": "2026-06-13 12:02:45+00:00", "updated_at": "2026-06-13 12:19:36.810871+00:00", "lang": "en", "topics": ["ai-infrastructure", "large-language-models", "artificial-intelligence", "developer-tools"], "entities": ["ChatGPT", "OpenAI", "Simon Willison", "Hacker News", "gVisor", "Whisper"], "alternates": {"html": "https://wpnews.pro/news/tearing-into-chatgpt-s-container-environment", "markdown": "https://wpnews.pro/news/tearing-into-chatgpt-s-container-environment.md", "text": "https://wpnews.pro/news/tearing-into-chatgpt-s-container-environment.txt", "jsonld": "https://wpnews.pro/news/tearing-into-chatgpt-s-container-environment.jsonld"}}