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Gemma 4 31B: VRAM requirements and GPUs that run it

Google's largest open dense model. Top-tier writing and vision; 24 GB class at Q4. At Q4 with 8K context it needs about 25 GB, so you want a GPU with at least 32 GB of VRAM or a Mac with 32 GB of unified memory.

Can your PC run Gemma 4 31B?

Pick your graphics card or Mac. Gemma 4 31B is pinned at the top of the list.

Models 0 of 0 run

● Runs in your browserMemory = weights × 1.1 + KV cache. Usable = 90% VRAM, 72% on Mac.
31Bparameters
25 GBat Q4, 8K context
1/40GPUs run it fully
~53tok/s, fastest card

Memory needed by quantization

QuantWeightsTotal at 8KTotal at 32K
Q4_K_M20 GB25 GB36 GB
Q5_K_M24 GB29 GB40 GB
Q8_036 GB41 GB52 GB
FP1668 GB73 GB84 GB

Totals include about 10% runtime overhead and the KV cache, plus around 0.6 GB for the vision encoder. Longer conversations cost real memory: going from 8K to 32K context adds about 12 GB at any quantization. Most people should download Q4_K_M; Q5 or Q8 only make sense if you have spare memory. Read which quantization to pick.

Which GPUs can run Gemma 4 31B?

1 of the 40 cards we track run it fully in VRAM at 8K context. The cheapest entry point is a 32 GB card such as the RTX 5090, and the fastest is the RTX 5090 at about 53 tokens per second. 39 more can run it with part of the model in system RAM, which is noticeably slower.

GPUVRAMStatusQuantSpeed
RTX 509032 GBRuns wellQ5_K_M~53 tok/s
RTX 409024 GBOffloadQ4_K_M~4.9 tok/s
RTX 3090 / 3090 Ti24 GBOffloadQ4_K_M~4.6 tok/s
RX 7900 XTX24 GBOffloadQ4_K_M~4.7 tok/s
RX 7900 XT20 GBOffloadQ4_K_M~3.6 tok/s
RTX 508016 GBOffloadQ4_K_M~3.8 tok/s
RTX 5070 Ti16 GBOffloadQ4_K_M~3.6 tok/s
RTX 5060 Ti16 GBOffloadQ4_K_M~2.2 tok/s
RTX 4080 / 4080 Super16 GBOffloadQ4_K_M~3.1 tok/s
RTX 4070 Ti Super16 GBOffloadQ4_K_M~2.9 tok/s
RTX 4060 Ti16 GBOffloadQ4_K_M~1.7 tok/s
RTX 4090 Laptop16 GBOffloadQ4_K_M~2.6 tok/s
RX 9070 XT16 GBOffloadQ4_K_M~2.8 tok/s
RX 7800 XT16 GBOffloadQ4_K_M~2.7 tok/s
RX 6800 XT16 GBOffloadQ4_K_M~2.4 tok/s
Arc A77016 GBOffloadQ4_K_M~2.5 tok/s
RTX 507012 GBOffloadQ4_K_M~2.6 tok/s
RTX 4070 / 4070 Super12 GBOffloadQ4_K_M~2.3 tok/s
RTX 306012 GBOffloadQ4_K_M~1.9 tok/s
RTX 4080 Laptop12 GBOffloadQ4_K_M~2.1 tok/s
RX 7700 XT12 GBOffloadQ4_K_M~2.1 tok/s
RX 6700 XT12 GBOffloadQ4_K_M~2.0 tok/s
Arc B58012 GBOffloadQ4_K_M~2.1 tok/s
GTX 1080 Ti11 GBOffloadQ4_K_M~2.2 tok/s
RTX 308010 GBOffloadQ4_K_M~2.7 tok/s
RTX 5060 Ti8 GBOffloadQ4_K_M~2.1 tok/s
RTX 50608 GBOffloadQ4_K_M~2.1 tok/s
RTX 4060 Ti8 GBOffloadQ4_K_M~1.8 tok/s
RTX 40608 GBOffloadQ4_K_M~1.8 tok/s
RTX 3070 / 3070 Ti8 GBOffloadQ4_K_M~2.1 tok/s
RTX 3060 Ti8 GBOffloadQ4_K_M~2.1 tok/s
RTX 30508 GBOffloadQ4_K_M~1.7 tok/s
RTX 4070 Laptop8 GBOffloadQ4_K_M~1.8 tok/s
RTX 4060 Laptop8 GBOffloadQ4_K_M~1.8 tok/s
RX 76008 GBOffloadQ4_K_M~1.8 tok/s
Arc A7508 GBOffloadQ4_K_M~2.2 tok/s
GTX 1660 / 1660 Super6 GBOffloadQ3_K_M~2.3 tok/s
RTX 4050 Laptop6 GBOffloadQ3_K_M~2.1 tok/s
RTX 3060 Laptop6 GBOffloadQ3_K_M~2.3 tok/s
GTX 16504 GBOffloadQ3_K_M~2.1 tok/s

Running it on a Mac

Apple Silicon Macs can give a model about 72% of their unified memory. Gemma 4 31B fits from 32 GB (Apple M4, about 5.2 tokens per second). With a faster chip like the Apple M4 Max (36 GB) expect around 19 tokens per second. Compare the options in our Mac vs NVIDIA guide.

Similar size models: gpt-oss 20B, Codestral 22B, Mistral Small 3 24B, Devstral Small 24B, Gemma 3 27B.

Compare Gemma 4 31B

Gemma 4 31B vs Qwen3.6 27BSide by side for local LLMsGemma 3 27B vs Gemma 4 31BSide by side for local LLMsCompare with any modelPick the other one yourself

Questions

How much VRAM does Gemma 4 31B need?

About 25 GB at Q4_K_M with 8K context, 41 GB at Q8_0, and 73 GB at full FP16. In practice that means a card with at least 32 GB of VRAM for Q4.

Can Gemma 4 31B run on an 8 GB GPU?

Only partly. It needs about 25 GB at Q4, so the rest spills into system RAM and speed drops sharply.

Can I run Gemma 4 31B on a Mac?

Yes. The smallest configuration that runs it fully is Apple M4 with 32 GB of unified memory, at about 5.2 tokens per second. More memory bandwidth (Pro, Max, Ultra chips) means faster replies.

Estimates based on model size, architecture and memory bandwidth. Real numbers vary with software, drivers and context length.

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