Yes — with 29.7 GB to spare

Qwen3.8 27B at Q4_K_M fits your RTX 6000 Ada entirely on the GPU at 8K context, at an estimated 37 tokens per second. There is room for its full 256K window.

Fully on GPU 8K context Q4_K_M · 15.6 GB Apache 2.0 Released 14 Aug 2026 New this week Vision

The current default local Qwen: dense 27B, text + image + video, 262K context. Only 16 of its 64 blocks keep a KV cache, so long context is cheap.

What hardware do I need for Qwen3.8 27B? →

The VRAM budget

weights 15.6 GB

Weights 15.6 GB KV cache @ 8K 0.50 GB Runtime overhead 0.6 GB Free 29.7 GB of 46.4 GB

Quantisation ladder

Quant	Weights	Total @ 8K	Max context	Tok/s	Quality	Fit
Q8_0	27.5 GB	28.6 GB	256K	21	−0.1% ppl	Long context
Q6_K	21.2 GB	22.3 GB	256K	27	−0.4% ppl	Long context
Q5_K_M	18.4 GB	19.5 GB	256K	32	−0.8% ppl	Long context
Q4_K_M	15.6 GB	16.7 GB	256K	37	−1.9% ppl	Recommended
Q3_K_M	12.7 GB	13.8 GB	256K	46	−5.4% ppl	Long context
Q2_K	10.8 GB	11.9 GB	256K	54	−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 16 of its 64 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 qwen3.8:27b
$ OLLAMA_CONTEXT_LENGTH=8192 \
    ollama run qwen3.8:27b

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

01Download is 15.6 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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