Yes — with 77.2 GB to spare

Qwen3.5 0.8B at Q4_K_M fits your H100 PCIe entirely on the GPU at 8K context, at an estimated 2475 tokens per second. There is room for its full 256K window.

Fully on GPU 8K context Q4_K_M · 0.5 GB Apache 2.0 Released 28 Feb 2026

Draft model for speculative decoding, or a classifier that fits in 1 GB.

What hardware do I need for Qwen3.5 0.8B? →

The VRAM budget

weights 0.5 GB

Weights 0.5 GB KV cache @ 8K 0.09 GB Runtime overhead 0.6 GB Free 77.2 GB of 78.4 GB

Quantisation ladder

Quant	Weights	Total @ 8K	Max context	Tok/s	Quality	Fit
F16	1.6 GB	2.3 GB	256K	747	Reference	Long context
Q8_0	0.9 GB	1.6 GB	256K	1406	−0.1% ppl	Long context
Q6_K	0.7 GB	1.4 GB	256K	1822	−0.4% ppl	Long context
Q5_K_M	0.6 GB	1.3 GB	256K	2108	−0.8% ppl	Long context
Q4_K_M	0.5 GB	1.2 GB	256K	2475	−1.9% ppl	Recommended
Q3_K_M	0.4 GB	1.1 GB	256K	3057	−5.4% ppl	Long context
Q2_K	0.3 GB	1.0 GB	256K	3568	−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. Only 6 of its 24 blocks keep a per-token KV cache; the rest are linear-attention, Mamba or convolution blocks with a fixed-size state.

How to run it

Ollama llama.cpp LM Studio vLLM

terminal

$ ollama pull qwen3.5:0.8b
$ OLLAMA_CONTEXT_LENGTH=8192 \
    ollama run qwen3.5:0.8b

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

01Download is 0.5 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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