Can the RTX 3090 run Gemma 4 31B?

Just. Gemma 4 31B at Q4_K_M needs about 20.8 GB of the RTX 3090's 24 GB, running at an estimated 29 tok/s. Keep context short and close other GPU apps.

VRAM
24 GB
Needs at Q4
20.8 GB
Headroom
3.2 GB
Speed (Q4_K_M)
~29 tok/s

Every quantization

Gemma 4 31B (32.7B) on 24 GB

QuantWeightsNeedsFitEst. speed
Q4_K_M19 GB20.8 GBTight~29 tok/s
Q5_K_M23 GB24.8 GBToo big -
Q8_035 GB36.8 GBToo big -
FP1665 GB66.8 GBToo big -

Estimates. Needs = weights + 1.8 GB for KV cache and runtime at modest context. Speed is single-stream decode scaled from the RTX 3090's 936 GB/s bandwidth (XiongjieDai/GPU-Benchmarks-on-LLM-Inference). Long contexts need more memory and run slower.

Run it

Copy, paste, go

llama-server -hf google/gemma-4-31B-it-qat-q4_0-gguf -ngl 99

Ollama pulls a Q4 build by default. Pick a larger quant only if the table above says it fits. Full Gemma 4 31B details →

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Gemma 4 31B on other hardware

Also runs on the RTX 3090

Frequently asked

Quick answers

Can the NVIDIA RTX 3090 run Gemma 4 31B?

Just. Gemma 4 31B at Q4_K_M needs about 20.8 GB of the RTX 3090's 24 GB, running at an estimated 29 tok/s. Keep context short and close other GPU apps.

How much VRAM does Gemma 4 31B need?

About 19 GB of weights at Q4_K_M, 35 GB at Q8_0 and 65 GB at FP16. Add roughly 1.8 GB for the KV cache and runtime at modest context lengths.

How fast is Gemma 4 31B on the RTX 3090?

Roughly 29 tokens per second at Q4_K_M (single user, llama.cpp-style decode). This is an estimate from the RTX 3090's 936 GB/s memory bandwidth.