{"slug": "deepseek-qwen", "title": "Deepseek? Qwen?", "summary": "A single H200 GPU with 141GB HBM3e cannot comfortably run DeepSeek V4 Flash (284B total, 13B active parameters) due to VRAM constraints, even with 2TB system RAM for offloading. The model requires an 8-GPU H200 node for practical serving, as system RAM aids capacity but not generation speed. Smaller MoE models like Qwen3.6-35B-A3B are more suitable for single-GPU setups.", "body_md": "Well. That ChatGPT conclusion may not be unreasonable. In simple terms, if you mean running that model on a single H200 GPU, the model is probably **too large for the available VRAM**. System RAM can be used as an escape hatch, but you should not expect it to be fast. So the model may run, but it may also be **extremely slow**:\n\nShort answer\n\nIf the machine is literally **1×H200 GPU + 2TB system RAM**, I would not start with **DeepSeek V4 Flash** as the first practical model.\n\nI would treat it as an **advanced experiment**, not as the default recommendation.\n\nThe model itself may be good. The problem is fit. The official model card describes DeepSeek V4 Flash as a **284B total / 13B active MoE model** with **1M context**:\n\n[deepseek-ai/DeepSeek-V4-Flash](https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash)\n\nA single H200 is very strong, but it is still a single GPU with about **141GB HBM3e**:\n\n[NVIDIA H200](https://www.nvidia.com/en-us/data-center/h200/)\n\nSo I would separate the cases like this:\n\n| Hardware interpretation |\nPractical meaning |\n**1×H200 + 2TB system RAM** |\nDeepSeek V4 Flash may be possible with quantization/offload, but I would expect it to be slow or backend-sensitive. |\n**8×H200 node + 2TB system RAM** |\nDeepSeek V4 Flash becomes much more natural as a vLLM/SGLang-style serving target. |\n**1×H200 with GGUF/llama.cpp-style offload** |\nInteresting for experiments, but speed and backend maturity become the main questions. |\n\nThe vLLM recipe for DeepSeek V4 Flash shows an H200 example around an **8-GPU H200 node** with prefill/decode splitting:\n\n[vLLM recipe: DeepSeek V4 Flash](https://recipes.vllm.ai/deepseek-ai/DeepSeek-V4-Flash)\n\nThat does not mean one H200 is useless. It just means that **“the model can run somewhere”** and **“this is a comfortable first model for one H200”** are different statements.\n\n“Runs” is not the same as “runs well”\n\nFor one H200, I would not only ask:\n\nCan the model load?\n\nI would ask:\n\nCan the model give acceptable latency, throughput, context length, stability, and quality for the actual workload?\n\nThose are different questions.\n\nSystem RAM helps with **capacity**. It does not magically turn CPU RAM into HBM. If the runtime constantly has to move weights, experts, or cache data between CPU RAM and GPU memory, generation can become transfer-bound.\n\n| 2TB system RAM helps with… |\nBut it does not automatically solve… |\n| Holding huge quantized weights in memory |\nGPU execution speed |\n| CPU offload experiments |\nCPU-GPU transfer bottlenecks |\n| Trying multiple models or quant levels |\nLow-latency serving |\n| RAG, preprocessing, and evaluation datasets |\nToken generation speed |\n| MoE expert offload experiments |\nBackend maturity issues |\n\nA useful short version is:\n\nSystem RAM helps capacity and experimentation much more than raw generation speed.\n\nDo not confuse MoE active parameters with dense model size\n\nThis is a common trap.\n\nWhen a MoE model says **13B active**, that does **not** mean it has the same memory requirement as a 13B dense model.\n\nFor MoE models:\n\n**active parameters** are closer to per-token compute cost\n**total parameters** are closer to model residency / storage / offload planning\n- non-active experts still need to live somewhere\n- expert placement and routing matter a lot\n- backend support matters a lot\n\n| Model |\nTotal params |\nActive params |\nPractical warning |\n[DeepSeek V4 Flash](https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash) |\n284B |\n13B |\nNot a 13B memory problem. |\n[Qwen3.5-122B-A10B](https://huggingface.co/Qwen/Qwen3.5-122B-A10B) |\n122B |\n10B |\nMore practical, but still not a 10B memory problem. |\n[Qwen3.6-35B-A3B](https://huggingface.co/Qwen/Qwen3.6-35B-A3B) |\n35B |\n3B |\nMuch more natural as a first single-H200 MoE candidate. |\n[MiniMax-M2](https://arxiv.org/abs/2605.26494) |\n229.9B |\n9.8B |\nInteresting, but still a large-MoE/offload/backend experiment. |\n\nThe rule I would use is:\n\nActive parameters are a compute signal, not a complete VRAM estimate.\n\nFor memory planning, also check:\n\n| Factor |\nWhy it matters |\n**Total parameters** |\nDetermines how much weight data must live somewhere. |\n**Quantization format** |\nChanges memory footprint, speed, and quality. |\n**KV cache** |\nCan dominate memory use at long context. |\n**Context length** |\n8K, 64K, 128K, and 1M are very different deployment problems. |\n**Batch / concurrency** |\nServing one user and serving many users are different. |\n**Backend support** |\nNew models can have missing operators, special attention, or immature kernels. |\n**Offload behavior** |\nCPU RAM can save capacity, but transfer can kill speed. |\n\nUse 4-bit estimates, but treat them as a lower bound\n\nFor current local OSS LLM use, I would usually size models assuming **good 4-bit weight quantization** first.\n\nThat is more realistic than assuming BF16/FP16 for every local deployment.\n\nBut a 4-bit sizing table is still only a first-pass estimate. It is not a guarantee.\n\nUseful reference:\n\n[Hugging Face GGUF docs](https://huggingface.co/docs/hub/gguf)\n\nA good warning is:\n\nThe table below assumes good 4-bit weight quantization and moderate context length. It does not fully include KV cache, batching, CUDA/workspace overhead, backend buffers, or long-context serving costs.\n\n| Model scale |\n1×H200 practicality, assuming good 4-bit weights |\nComment |\n**7B–14B dense** |\nVery easy |\nFast, but probably too small if you want to exploit an H200. |\n**24B–40B dense/MoE** |\nExcellent first target |\nGood quality/speed range; practical baseline. |\n**70B dense** |\nVery realistic |\nNatural use of a large single GPU. |\n**100B–130B dense/MoE** |\nUpper practical range |\nWorth testing; KV cache and context length matter. |\n**200B–300B total MoE** |\nAdvanced / experimental |\nPossible in some setups, but do not assume it will be fast. |\n**400B+ total MoE** |\nUsually not a first single-H200 target |\nMay run with heavy offload, but “usable” depends heavily on backend and tolerance for low tokens/sec. |\n**1T-class MoE** |\nWatchlist / joke / special case |\nInteresting, but not where I would start on one H200. |\n\nFor this setup, I would probably test in this order:\n\n| Order |\nSize range |\nGoal |\n| 1 |\n**24B–40B** |\nFast baseline with modern models. |\n| 2 |\n**70B** |\nStrong large-single-GPU baseline. |\n| 3 |\n**100B–130B** |\nUpper practical range. |\n| 4 |\n**200B+ MoE** |\nOnly after baseline latency/quality is known. |\n\nQuantization is practical, but not magic\n\n4-bit quantization is often the practical default for large local models. But it still trades off memory, speed, and quality.\n\nThe quality loss is often small enough to be acceptable for large models, especially with good formats. But it is not literally zero.\n\nIt can matter more for:\n\n- math\n- code\n- strict JSON/tool calling\n- long reasoning chains\n- small models\n- difficult instruction following\n- tasks where small logit differences matter\n\nSpeed is also not automatic. Quantization can speed things up by reducing memory bandwidth and allowing the model to fit on GPU. But some formats require dequantization or special kernels, and performance depends on backend implementation.\n\n| Quant level |\nPractical meaning |\n**Q8 / FP8 / 8-bit** |\nQuality-oriented if memory allows. |\n**Q6 / Q5** |\nGood quality/capacity balance. |\n**Q4** |\nPractical default for many large local models. |\n**Q3** |\nSometimes acceptable for large models; test quality. |\n**Q2 / ~2-bit** |\nEmergency or experiment zone. |\n**IQ1 / ~1.5–1.8 bpw** |\nFunny but real; not a normal first recommendation. |\n**BitNet b1.58-style models** |\nSeparate low-bit-native architecture/training direction, not ordinary post-training quantization. |\n\nAs a small quantization joke: yes, 1-bit and 2-bit quants exist. If the alternative is “the model does not fit at all,” 1.5–2 bit can sometimes be useful. But I would not use those as the normal recommendation. I would size the machine around good 4-bit weights first.\n\nLong context changes the memory math\n\nModel weights are only one part of VRAM use.\n\nLong context can make **KV cache** a major memory consumer.\n\nA model that fits at 8K context may not be comfortable at 64K, 128K, or 1M context. This is especially important for models that advertise very long context.\n\nFor DeepSeek V4 Flash, **“supports 1M context”** and **“I can serve 1M context comfortably on one H200”** are very different statements.\n\nvLLM has documentation on quantized KV cache:\n\n[vLLM Quantized KV Cache](https://docs.vllm.ai/en/latest/features/quantization/quantized_kvcache/)\n\nThat page is useful because it highlights the point: KV cache is important enough that people quantize it separately.\n\nWhen comparing models, I would track:\n\n| Metric |\nWhy |\n**VRAM used** |\nShows whether the model actually fits with your settings. |\n**CPU RAM used** |\nShows how much offload/caching is happening. |\n**Time to first token** |\nImportant for UX and serving latency. |\n**Generation tok/s** |\nImportant for actual output speed. |\n**Prompt tok/s** |\nImportant for long-context workloads. |\n**Max context tested** |\nPrevents misleading “it fits at 8K” conclusions. |\n\nBackend maturity matters, especially for new models\n\nA model can have valid weights and still be annoying to run.\n\nThis happens often with very new models.\n\n| Possible issue |\nWhat to check |\n| New operators / attention patterns |\nvLLM, SGLang, Transformers, llama.cpp support |\n| Multimodal processors |\nWhether the backend supports the exact processor path |\n| Special chat template |\nModel card and tokenizer config |\n| Special response format |\nExample: GPT-OSS Harmony format |\n| GGUF still in progress |\nllama.cpp discussions / model repo notes |\n| Missing repo files or metadata |\nHF Files and community discussions |\n| Backend lag |\nRecent issues, PRs, and real user reports |\n\nThis is why older models can be attractive. They may be less exciting, but the runtime path is usually safer.\n\nHow I would search for OSS LLMs today\n\nI would not choose a model by asking only “what is the best model?”\n\nI would use leaderboards and community attention to build a shortlist, then reject candidates that do not fit the runtime.\n\nUseful discovery links:\n\nMy search process would be:\n\n| Step |\nCheck |\n| 1 |\nFind active model families from HF, leaderboards, and community discussion. |\n| 2 |\nOpen the exact model card, not just a leaderboard row. |\n| 3 |\nCheck total params, active params, context length, and license. |\n| 4 |\nCheck whether the repo has the files you actually need. |\n| 5 |\nCheck vLLM / SGLang / GGUF / llama.cpp support. |\n| 6 |\nCheck recent issues and discussions. |\n| 7 |\nRun your own small benchmark. |\n\nLeaderboards are useful, but they are not the final answer. A high-ranking model can still be a bad fit if it is painful to run on your hardware.\n\nPractical candidate families I would investigate on one H200\n\nI would not present this as a definitive ranking. The open-model landscape changes too quickly, and backend support matters a lot.\n\nBut if I had **1×H200 + 2TB RAM**, these are the kinds of model families I would personally investigate first.\n\nFirst practical tests\n\n| Candidate |\nWhy I would look at it |\n[Gemma 4 26B-A4B / 31B](https://deepmind.google/models/gemma/gemma-4/) |\nNewer, strong, and still in a practical size range. Check backend support because newer architecture features can matter. |\n[Qwen3.6-35B-A3B](https://huggingface.co/Qwen/Qwen3.6-35B-A3B) |\nVery attractive size for one H200: 35B total / 3B active, with vLLM/SGLang/KTransformers compatibility noted on the model card. |\n[Qwen2.5-Coder-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct) |\nOlder, safer coding baseline; likely easier to run than very new models. |\n[Mistral Small 3.2 24B](https://huggingface.co/mistralai/Mistral-Small-3.2-24B-Instruct-2506) |\nPractical 24B-class baseline; good first comparison point. |\n[DeepSeek-R1-Distill-Qwen-32B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B) |\nUseful if reasoning is important and you want a 32B-class baseline. |\n\nStrong larger tests\n\n| Candidate |\nWhy I would look at it |\n[Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct) |\nOlder but strong and safe; good baseline for a large single GPU. |\n[GPT-OSS-120B](https://huggingface.co/openai/gpt-oss-120b) |\nVery interesting for one H200 because it is documented as fitting into a single 80GB-class GPU. Make sure to use the required Harmony format. |\n[Qwen3.5-122B-A10B](https://huggingface.co/Qwen/Qwen3.5-122B-A10B) |\nLarger modern MoE candidate; still more realistic than 200B–300B+ total MoE as a first large experiment. |\n[Mistral Medium 3.5 128B](https://huggingface.co/mistralai/Mistral-Medium-3.5-128B) |\nDense 128B with long-context ambitions; interesting upper-range test for one H200 with quantization. |\n| Llama 70B-class baselines |\nUseful because Llama-compatible tooling is mature, especially for GGUF/llama.cpp-style workflows. |\n\nAdvanced / only after smaller baselines\n\n| Candidate |\nWhy I would be careful |\n[DeepSeek V4 Flash](https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash) |\nInteresting model, but 284B total params makes it an offload/backend experiment on one H200. |\n[Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507) |\nLarge MoE; worth testing only after you know your latency/quality baseline. |\n[MiniMax-M2](https://arxiv.org/abs/2605.26494) |\n229.9B total / 9.8B active; interesting agentic model, but still a large-MoE deployment experiment. |\n[Llama 4 Scout](https://huggingface.co/blog/llama4-release) |\nPotentially interesting, but check exact backend support and memory behavior. |\n\nI am intentionally not listing every exciting new frontier MoE model here. For example, GLM-5-class models may be interesting, but they are too large to be good “first practical candidates” for a single H200. I would rather list models that I would realistically test first.\n\nHalf-joke / watchlist\n\n| Item |\nWhy it is not my first practical target |\n| Kimi K2 / Kimi V2-class giant MoE models |\nExciting, but I would not make a 1T-class MoE my first practical single-H200 target. |\n| 1-bit / 2-bit quants |\nReal, funny, and sometimes useful, but I would treat them as emergency or experiment options. |\n\nUseful local inference references\n\nBuild a tiny internal eval set\n\nPublic leaderboards are for shortlisting. For deployment, I would also make a small private eval set from real internal tasks. Even **20–50 carefully chosen cases** can be useful; [promptfoo](https://www.promptfoo.dev/) and [LangSmith Evaluation](https://docs.langchain.com/langsmith/evaluation) are good references.\n\n| Category |\nExample |\nScore |\n| Summarization |\nmemo / meeting note |\nfactuality, omissions, action items |\n| Extraction |\nemails / tickets / PDFs |\nexact match, JSON schema |\n| RAG QA |\ninternal docs |\nfaithfulness, citations |\n| Long context |\nlargest realistic bundle |\naccuracy, latency, memory |\n| Coding / JSON |\nscript or API payload |\ntests, schema, business rules |\n| Regression |\nprevious failures |\npass/fail + note |\n\nRecord the same basics for every model: **backend, quant, context, VRAM, CPU RAM, tok/s, quality, failure mode**.\n\nBottom line\n\nI would not say DeepSeek V4 Flash is a bad model.\n\nI would say:\n\nDeepSeek V4 Flash is probably too large to be the first practical target for one H200 if you care about speed and ease of deployment.\n\nIf this is **1×H200 + 2TB RAM**, I would start with models around:\n\n| First |\nThen |\nLater |\n| Gemma 4 26B-A4B / 31B |\nQwen2.5-72B |\nDeepSeek V4 Flash |\n| Qwen3.6-35B-A3B |\nGPT-OSS-120B |\nQwen3-235B-A22B |\n| Qwen2.5-Coder-32B |\nQwen3.5-122B-A10B |\nMiniMax-M2 |\n| Mistral Small 3.2 24B |\nMistral Medium 3.5 128B |\nother large MoE models |\n\nThe main lesson is:\n\nDo not choose open LLMs by leaderboard rank or active parameter count alone. Choose them by matching model architecture, total size, quantization, context length, KV cache, backend support, and hardware reality.", "url": "https://wpnews.pro/news/deepseek-qwen", "canonical_source": "https://discuss.huggingface.co/t/deepseek-qwen/176657#post_5", "published_at": "2026-06-25 10:01:19+00:00", "updated_at": "2026-06-25 10:23:31.466082+00:00", "lang": "en", "topics": ["large-language-models", "ai-infrastructure", "ai-chips", "machine-learning"], "entities": ["DeepSeek", "Qwen", "NVIDIA H200", "vLLM", "Hugging Face", "DeepSeek V4 Flash", "Qwen3.5-122B-A10B", "Qwen3.6-35B-A3B"], "alternates": {"html": "https://wpnews.pro/news/deepseek-qwen", "markdown": "https://wpnews.pro/news/deepseek-qwen.md", "text": "https://wpnews.pro/news/deepseek-qwen.txt", "jsonld": "https://wpnews.pro/news/deepseek-qwen.jsonld"}}