Yes — with 119.2 GB to spare

Gemma 4 31B at Q4_K_M fits your H200 SXM entirely on the GPU at 8K context, at an estimated 165 tokens per second. There is room for its full 256K window.

Fully on GPU 8K context Q4_K_M · 17.6 GB Apache 2.0 Released 2 Apr 2026 Vision

The dense flagship: strongest maths of the 24–32 GB class (89% AIME), clean prose, vision. Q4 is a tight 24 GB fit.

What hardware do I need for Gemma 4 31B? →

The VRAM budget

weights 17.6 GB

Weights 17.6 GB KV cache @ 8K 2.03 GB Runtime overhead 0.6 GB Free 119.2 GB of 139.4 GB

Quantisation ladder

Quant	Weights	Total @ 8K	Max context	Tok/s	Quality	Fit
Q8_0	31.0 GB	33.6 GB	256K	94	−0.1% ppl	Long context
Q6_K	23.9 GB	26.5 GB	256K	122	−0.4% ppl	Long context
Q5_K_M	20.7 GB	23.3 GB	256K	141	−0.8% ppl	Long context
Q4_K_M	17.6 GB	20.2 GB	256K	165	−1.9% ppl	Recommended
Q3_K_M	14.2 GB	16.9 GB	256K	204	−5.4% ppl	Long context
Q2_K	12.2 GB	14.8 GB	256K	238	−15% ppl	Long context

Quality is the published perplexity delta against f16 weights. Max context assumes an f16 KV cache; q8_0 roughly doubles it. This model interleaves sliding-window layers (1024 tokens, 10 of 60 layers global), which is why its cache barely grows with context.

How to run it

Ollama llama.cpp LM Studio vLLM

terminal

$ ollama pull gemma4:31b
$ OLLAMA_CONTEXT_LENGTH=8192 \
    ollama run gemma4:31b

The default. One binary, a model registry, an OpenAI-compatible port. More on Ollama.

01Download is 17.6 GB. Keep it on an SSD — a first load off a spinning disk takes minutes.

02Close anything else holding VRAM. A browser with hardware acceleration can sit on 1–2 GB.

03There is room to go to the model's full 256K context on this card.

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