text model · Gemma · Windows

Can I run Gemma 4 31B on Nvidia GeForce RTX 4090 (24GB)?

Compatibility verdict VRAM threshold engine

Yes, it runs GPU accelerated ~36 tok/s est.

Yes. Gemma 4 31B runs on Nvidia GeForce RTX 4090 (24GB) at Q4_K_M (~20.5 GB of ~23 GB usable).

Needs ~20.5 GB Device usable ~23 GB

Runs at Q4_K_M using ~20.5 GB of ~23 GB usable.

That figure is at a 4k context and moves about ±15% as context length changes. Nvidia GeForce RTX 4090 (24GB) leaves ~2.5 GB of headroom.

Q4_K_M needed: ~20.5 GB
Usable on device: ~23 GB
Device memory: 24 GB
Best quant: Q4_K_M

Share on X Share on Reddit

Which quant fits

Quant ladder vs ~23 GB usable

Q2_K

~15.9 GB

Q3_K_M

~18.2 GB

Q4_K_M

~20.5 GB

Q5_K_M

~25.5 GB

Q6_K

~29 GB

Q8_0

~34.8 GB

FP16

~63.6 GB

The line marks Nvidia GeForce RTX 4090 (24GB)'s ~23 GB budget; rungs past it are too large.

Running cost · estimate

Power & economics est.

Power draw: ~450 W
Electricity / 1M tokens: ~$0.52

At ~$0.15/kWh and the estimated ~36 tok/s, a million generated tokens costs about $0.52 in electricity. TDP is the peak draw, so this is an upper bound. Assumptions.

Run it

Install commands Windows

Pick your tool. All 3 load the same Q4_K_M weights.

Ollama

$ ollama run gemma4:31b

llama.cpp

$ llama-cli -hf unsloth/gemma-4-31B-it-GGUF:Q4_K_M

LM Studio

$ lms get unsloth/gemma-4-31B-it-GGUF

Model Gemma

Parameters: 32.7B
Q4_K_M size: 18.32 GB
Q8_0 size: 32.64 GB
Context: 256k
Ollama tag: gemma4:31b

Full Gemma 4 31B requirements →

Device Windows

Memory: 24 GB vram
Usable for weights: ~23 GB
Power draw: ~450 W
Best runtime: vLLM (Linux) / Ollama (CUDA)

Best models for Nvidia GeForce RTX 4090 (24GB) →

You could also run

Run Gemma 4 31B on other hardware

Nvidia GeForce RTX 3090 (24GB)AMD Radeon RX 7900 XTX (24GB)Nvidia GeForce RTX 5090 (32GB)32GB RAM Laptop (CPU/iGPU only)Apple M5 (32GB)Apple M4 Pro (48GB)

FAQ

Can Nvidia GeForce RTX 4090 (24GB) run Gemma 4 31B?

Yes. Gemma 4 31B runs on Nvidia GeForce RTX 4090 (24GB) at Q4_K_M (~20.5 GB of ~23 GB usable).

How much memory does Gemma 4 31B need?

Nvidia GeForce RTX 4090 (24GB) has room to spare. At Q4_K_M the weights are ~18.32 GB; with KV cache and runtime overhead, budget ~20.5 GB at a 4k context.

What is the best tool to run Gemma 4 31B on Windows?

LM Studio for a simple setup; Ollama (CUDA) for the most speed. AMD GPUs run via Vulkan/ROCm at roughly half CUDA throughput. NVIDIA is the smooth path on Windows.

Embed this

A live badge for your model card or README, updated as the data is.

Markdown

[![Gemma 4 31B on Nvidia GeForce RTX 4090 (24GB)](https://localmodel.run/badge/gemma-4-31b/nvidia-rtx-4090-24gb.svg)](https://localmodel.run/can-i-run/gemma-4-31b/nvidia-rtx-4090-24gb)

Sources

Weights are measured from GGUF files; KV cache and overhead are computed, so totals can vary ~15% with context and runtime. Any tok/s is a bandwidth estimate. See methodology.