Yes — with 6.4 GB to spare

Gemma 4 26B-A4B at Q4_K_M fits your GeForce RTX 4090 entirely on the GPU at 8K context, at an estimated 110 tokens per second. Past 171K the KV cache pushes it over — quantise the cache to q8_0, or step down a quantisation, to go longer.

Fully on GPU 8K context Q4_K_M · 14.9 GB Apache 2.0 Released 2 Apr 2026 Vision

Mixture of experts with 3.8B active. Slower to think than Qwen3.6 35B-A3B, faster to answer, and it sees images.

What hardware do I need for Gemma 4 26B-A4B? →

The VRAM budget

weights 14.9 GB

Weights 14.9 GB KV cache @ 8K 0.51 GB Runtime overhead 0.6 GB Free 6.4 GB of 22.4 GB

Quantisation ladder

Quant	Weights	Total @ 8K	Max context	Tok/s	Quality	Fit
Q8_0	26.2 GB	27.3 GB	—	~16	−0.1% ppl	4.9 GB over
Q6_K	20.2 GB	21.3 GB	34K	81	−0.4% ppl	Long context
Q5_K_M	17.5 GB	18.6 GB	105K	94	−0.8% ppl	Long context
Q4_K_M	14.9 GB	16.0 GB	171K	110	−1.9% ppl	Recommended
Q3_K_M	12.1 GB	13.2 GB	244K	136	−5.4% ppl	Long context
Q2_K	10.3 GB	11.4 GB	256K	158	−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, 5 of 30 layers global), which is why its cache barely grows with context.

How to run it

Ollama llama.cpp LM Studio vLLM

terminal

$ ollama pull gemma4:26b
$ OLLAMA_CONTEXT_LENGTH=8192 \
    ollama run gemma4:26b

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

01Download is 14.9 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 171K context on this card.

See all models for this rig Compare with another model