Gemma 4 31B
Gemma 4 31B loads in 20 GB at Q4_K_M, comfortably from 36 GB of memory, Macs included, starting at the MacBook Pro M5 Pro 48GB. Below is the memory math we ran for it, the smallest card that fits, and the fastest machine we track.
How much memory does Gemma 4 31B need?
Gemma 4 31B is a 31B dense model built for quality, coding, multimodal. The weights alone take 20 GB at Q4_K_M; add context and the OS, and it belongs in the 36 GB memory class. Every number below comes from the ModelFit engine: speeds are estimates, labeled est., because we do not benchmark hardware ourselves.
KV = fp16 estimate (q8_0 cache roughly halves it). "Comfortable" = weights + KV within the engine's tiered budget (~70-85% of memory).
Which machines run Gemma 4 31B?
Verdicts come from the same engine that powers our fit checker: memory capacity first, bandwidth for speed. How we compute these numbers · Hugging Face model card
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Frequently asked questions
How much memory does Gemma 4 31B need?
20 GB for the Q4_K_M weights, plus ~4.0 GB of KV-cache at 16k context, about 24.0 GB total. Comfortable from 36 GB of VRAM or unified memory.
Does Gemma 4 31B run on a Mac?
Yes, from the MacBook Pro M5 Pro 48GB (~13 tok/s est.). Unified memory means the RAM budget is the only limit.
What is the smallest GPU for Gemma 4 31B?
The NVIDIA GeForce RTX 3090 (24 GB VRAM) is the smallest tracked card that runs Gemma 4 31B comfortably, ~28 tok/s est.
Which Gemma 4 31B quant should I pick?
The tracked Q4_K_M build at 20 GB is the one we recommend for most machines. Every published build, ranked by real GGUF file size rather than a formula, is on the quant comparison page linked below.
Cite this page
ModelFit: Gemma 4 31B, specs, memory math and hardware verdicts. https://modelfit.io/models/gemma4-31b/ (dataset updated 2026-10-13, CC BY 4.0).