Yes — with 21.4 GB to spare

Gemma 3 27B at Q4_K_M fits your A100 40 GB entirely on the GPU at 8K context, at an estimated 61 tokens per second. There is room for its full 128K window.

Fully on GPU 8K context Q4_K_M · 15.4 GB Gemma Terms of Use Released Mar 2025 Vision

The 2025 single-GPU generalist with vision. Its Gemma-licence terms are the reason to prefer Gemma 4 now.

What hardware do I need for Gemma 3 27B? →

The VRAM budget

weights 15.4 GB

Weights 15.4 GB KV cache @ 8K 1.03 GB Runtime overhead 0.6 GB Free 21.4 GB of 38.4 GB

Quantisation ladder

Quant	Weights	Total @ 8K	Max context	Tok/s	Quality	Fit
Q8_0	27.1 GB	28.7 GB	128K	35	−0.1% ppl	Long context
Q6_K	20.9 GB	22.6 GB	128K	45	−0.4% ppl	Long context
Q5_K_M	18.1 GB	19.7 GB	128K	52	−0.8% ppl	Long context
Q4_K_M	15.4 GB	17.0 GB	128K	61	−1.9% ppl	Recommended
Q3_K_M	12.5 GB	14.1 GB	128K	75	−5.4% ppl	Long context
Q2_K	10.7 GB	12.3 GB	128K	88	−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, 1 global in 6), which is why its cache barely grows with context.

How to run it

Ollama llama.cpp LM Studio vLLM

terminal

$ ollama pull gemma3:27b
$ OLLAMA_CONTEXT_LENGTH=8192 \
    ollama run gemma3:27b

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

01Download is 15.4 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 128K context on this card.

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