Can an RTX 3090 run Gemma 4 31B Instruct?

In practice

Gemma 4 31B Instruct sits comfortably on RTX 3090. At Q4_K_M it takes 21.31 GB of the 23.2 GB available, which leaves 1.89 GB spare, enough for the 8k window we assumed and for a window of up to about 8k tokens before memory runs out. Generation runs at about 33.0 tokens per second, well past the speed anyone reads at, so a 500-token answer takes about 15 seconds.

Of the memory in use, 82% is the weights and 15% is the attention cache. With this much room the question is quality, not fit. The next step up, Q5_K_M, needs 24.44 GB and does not fit, so Q4_K_M is where we would stay.

Reading a 4,000-token prompt, roughly a long email or a few pages of text, takes about 5 seconds before the first word appears. That part is limited by compute and not by bandwidth, so it follows a different spec from the writing speed.

The numbers

Gemma 4 31B Instruct has 31.27 billion parameters across 60 layers. The GeForce RTX 3090 has 24 GB of VRAM at 936 GB/s, of which about 23.2 GB is left for a model once the operating system has taken its share.

6 of the 9 levels fit entirely. Levels marked degraded are listed for completeness, not as advice. Rows marked partial or experts in RAM run with part of the model in system RAM, at the speed shown.

How long a conversation it holds

Memory rises with the length of the conversation because the attention cache keeps a key and a value for every token. At Q4_K_M, the longest window that still fits on this device is 8k tokens. Setting the window to what you need and no more is the cheapest way to buy back memory.

The settings we would use

llama-server -m <model>.gguf -ngl all -c 8192 -fa on

In Ollama, set OLLAMA_CONTEXT_LENGTH to the window you want, run ollama ps while the model is loaded and check that the processor column reads 100% GPU. If it shows a split on a model that fits, the Ollama guide shows how to fix it. The llama.cpp flags guide explains each option above.

What else an RTX 3090 runs well

For comparison, these are the other popular models that fit this device at a sound quantization, best pairings first.

The same model on nearby hardware

Memory decides whether Gemma 4 31B Instruct loads and bandwidth decides how quickly it answers. Here is what a step down, a card with the same memory and a step up do with it.

Wait for the first word593 ms

Then writes at33 tok/s

Whole answer5.8 s

YouWhy does my model use more memory when the conversation gets longer?

Model

Simulated from our estimate at a 512-token question, not a recording. Assumes nothing else is competing for the GPU.

Questions people ask

Keep going

Everything the GeForce RTX 3090 runs, the full breakdown for Gemma 4 31B Instruct, how the numbers are made in the methodology, or a different pairing in the calculator.