# 2 years and I have a small AI server | how do I benchmark? Whats useful to know? > Source: > Published: 2026-11-04 20:35:12+00:00 So I forwarded your reply to Muse, this is what Muse replied with: **Q: Can you post the link to the strata fork you’re using?** A: [GitHub - jmnargi/Strata-V100: Strata port for Tesla V100 (sm_70): Qwen3.8-Flash-Next with 262K context and MTP · GitHub](https://github.com/jmnargi/Strata-V100) My install: commit 7de1934, engine 0.1.45.6, CUDA 12.9.1, Qwen3.8-Flash-Next Q2_0, 36G + 27G shards, max-context 262144, KV int8, spec 4, MTP on, port 8081, GPUs [0,1] **Q: Is Resizable BAR on in the BIOS?** A: No, and it’s not available on these cards, checked from the OS, not BIOS guesswork. Commands used: `lspci -s 04:00.0 -v`, `lspci -s 08:00.0 -v`, `nvidia-smi` - No Resizable BAR capability listed in lspci for either card, V100 / Volta doesn’t have consumer ReBAR like a 5060 Ti / Blackwell - Full memory is mapped correctly: GPU 0 Memory at 7000000000 [size=32G], GPU 1 Memory at 6000000000 [size=32G], kernel driver in use: nvidia, both Tesla PG500-216, 32GB - So BAR is not screwy, and not the cause of the memory errors in my OP **Q: Nvidia passthrough funky?** A: Not applicable here. These V100s are on the Proxmox host itself, not VM passthrough, running llama-swap on 8080 and Strata on 8081. No VM restart / reboot-the-host passthrough issue. **My Strata crash, direct:** “prefill: the PLE rows’ upload failed: an illegal memory access was encountered” “output became non-finite (NaN) twice, engine dropped cached state” Layer 0, chunk 0, quantize_scatter, break at about 1,700-2,575 tokens. Small prompts work, 56-69 decode, larger crash in about 0.5s with 0 tokens. **Constraints that shaped what I did / didn’t do:** - No Docker for my product / setup, so I did NOT use Strata’s Docker setup, I built / ran it natively on the host, hand-built first. If that was why I didn’t try a Docker-based bench, that’s the reason, not that I didn’t think of it. - I also didn’t run llm-bench-dot-io, because it always uploads results to their site, and I don’t want private code / results uploaded. - Smoke tests at 128 / 512 tokens don’t help me, Hermes eats about 30k once it’s loaded my large codebase + tooling, so I need 32k / 64k / 128k benchmarks with real code tasks, not small prompts. My llama.cpp numbers, for comparison, same stack, different engine: 356 prefill / 26 decode at 32k, 324 / 28 at 64k, 272 / 25 at 128k. My friend’s 1x 3060 at 60t/s on Q3 is a different quant, context, engine, and prompt, so not directly comparable. I hope this helps :C sometimes it tries to talk in as if replying as me and gets mixed up on those type of details.