Can you run Gemma 4 31B on RTX 4090?
Gemma 4 31B Q4_K_M on the NVIDIA GeForce RTX 4090: verdict, VRAM math and estimated speed.
Yes, the NVIDIA GeForce RTX 4090 runs Gemma 4 31B. 20 GB weights at Q4_K_M vs 21.6 GB usable VRAM; ~33 tok/s est. (ModelFit, 2026).
VRAM math and speed are ModelFit engine estimates, not measurements. Commands are registry-verified Ollama tags.
Cite this page: ModelFit, Gemma 4 31B on RTX 4090, https://modelfit.io/can-i-run/gemma4-31b-q4-on-rtx-4090/, updated September 2026, CC BY 4.0.
Last updated: September 24, 2026 · Editor: ModelFit Team
What limits this combo
The four constraints the fit engine checks for Gemma 4 31B on the RTX 4090, in order of what usually breaks first.
Where the RTX 4090 sits for Gemma 4 31B
On the RTX 4090, Gemma 4 31B runs with room to spare (~33 tok/s est.). The cheapest card that also runs it comfortably is the RTX 3090 (24 GB, ~28 tok/s est.).
Same engine as the verdict above: 90% of VRAM usable, KV-cache at fp16, bandwidth-derived tok/s. Verdicts of the other cards are computed for Gemma 4 31B Q4_K_M exactly.
Quant explorer
Switch between the quality-gated builds tracked for this exact combo. Q4_K_M stays the default recommendation; heavier Q6/Q8 builds only appear when their exact Ollama tags are registry-verified.
Why no 2-bit builds: Q1/Q2-class quants can look attractive in a memory table, but their quality loss is large enough that ModelFit excludes them from rankings instead of inflating the catalog with junk options. Usable VRAM: 21.6 GB.
Workload verdicts
Gemma 4 31B on the RTX 4090, graded per use case from the model's registry-verified tuning and the engine's speed estimate for this exact combo.
Grades combine tag-verified tuning (a model not built for the workload caps at C) with the tok/s floor each workload needs to feel usable. A combo that partially offloads caps at C; one that does not fit is D everywhere.
Memory math: weights + context vs budget
Weights take 20 GB. Context costs extra KV-cache on top; this is where long-context sessions break on cards that technically fit the weights.
This configuration exceeds the comfortable VRAM budget by about 2.4 GB. Expect offload pressure or a shorter safe context.
KV-cache figures assume an fp16 cache, the llama.cpp/Ollama default. Standard GQA models use a size-class estimate (8 KV heads x 128 head dim class); hybrid linear-attention models (Qwen3.5/3.6, Qwen3-Next) use the exact per-token cost from their published config, since only their sparse full-attention layers cache KV. A q8_0 KV cache roughly halves either figure. Estimates, not measurements.
See how fast it feels
A deterministic typing simulation for Gemma 4 31B: first token ~1.1s (instant prefill), then ~33 tokens/sec.
ModelFit engine estimate, not a measured benchmark. Real speed varies with prompt length, thermals, runtime, and KV-cache settings.
Gemma 4 31B on RTX 4090: FAQ
Can the NVIDIA GeForce RTX 4090 run Gemma 4 31B?
Yes. Gemma 4 31B (Q4_K_M) loads in about 20 GB and the RTX 4090 offers 21.6 GB of usable VRAM, leaving headroom for context. Expect at roughly 33 tokens/sec (est.).
How much VRAM does Gemma 4 31B need?
About 20 GB for the weights at Q4_K_M, plus KV-cache for context: roughly 4.0 GB extra at 16k tokens. The RTX 4090 budget is 21.6 GB (24 GB x 90%).
What is the best quantization of Gemma 4 31B for the RTX 4090?
Stick with the Q4_K_M build at 20 GB; every heavier quant exceeds the 21.6 GB usable VRAM.
What GPU do I need to run Gemma 4 31B comfortably?
The RTX 4090 already runs Gemma 4 31B comfortably. Larger cards only buy you longer context or a heavier quant.
How much context can Gemma 4 31B use on the RTX 4090?
None worth having: the 20 GB of weights alone exceed the 21.6 GB usable VRAM, so the model only runs with system-RAM offload and short prompts.
Is Gemma 4 31B on the RTX 4090 fast enough for coding agents?
Barely on paper: the engine estimates ~33 tok/s against the ~30 tok/s an agentic loop needs to feel responsive, but Gemma 4 31B is not tuned for tool-calling, which caps its agentic grade; a coding-tuned model is the better pick here.