Yes — with 72.3 GB to spare

Ornith 1.5 9B at Q4_K_M fits your H100 PCIe entirely on the GPU at 8K context, at an estimated 229 tokens per second. There is room for its full 256K window.

Fully on GPU 8K context Q4_K_M · 5.3 GB MIT Released 19 Aug 2026 New this week

The small Ornith: a coding-agent reasoning build on the Qwen3.5 9B architecture. Same VRAM as its base, thinks before every answer.

What hardware do I need for Ornith 1.5 9B? →

The VRAM budget

weights 5.3 GB

Weights 5.3 GB KV cache @ 8K 0.25 GB Runtime overhead 0.6 GB Free 72.3 GB of 78.4 GB

Quantisation ladder

Quant	Weights	Total @ 8K	Max context	Tok/s	Quality	Fit
F16	17.5 GB	18.4 GB	256K	69	Reference	Long context
Q8_0	9.3 GB	10.2 GB	256K	130	−0.1% ppl	Long context
Q6_K	7.2 GB	8.0 GB	256K	168	−0.4% ppl	Long context
Q5_K_M	6.2 GB	7.1 GB	256K	195	−0.8% ppl	Long context
Q4_K_M	5.3 GB	6.1 GB	256K	229	−1.9% ppl	Recommended
Q3_K_M	4.3 GB	5.1 GB	256K	283	−5.4% ppl	Long context
Q2_K	3.7 GB	4.5 GB	256K	330	−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 8 of its 32 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 ornith-1.5:9b
$ OLLAMA_CONTEXT_LENGTH=8192 \
    ollama run ornith-1.5:9b

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

01Download is 5.3 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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