GoogleGemma 4 31B on Apple M3 (16GB Unified)
M3 (16GB Unified) cannot run Gemma 4 31B. 16 GB VRAM is insufficient at any quantization level.
Model Size
30.7B
Device VRAM
16 GB
Bandwidth
100 GB/s
Quantization
Q3_K_M
Performance by Quantization
OwnRig currently has one published compatibility entry for Gemma 4 31B on Apple M3 (16GB Unified) at Q3_K_M. This pairing has limitations — check the rating and notes below.
| Quantization | Speed | TTFT | Fits in VRAM | Rating | Confidence |
|---|---|---|---|---|---|
| Q3_K_M | – | – | ✗ Offload | Not viable | estimated |
Notes
Q3_K_M
Model exceeds m3-16gb effective VRAM capacity.
About Gemma 4 31B
Gemma 4 31B (30.7B) is a chat, coding, reasoning, multi-purpose model. Google's flagship open-weight model. Dense 30.7B parameters with 256K context. Benchmarks: 89.2% AIME 2026, 85.2% MMLU Pro, 84.3% GPQA Diamond, 80.0% LiveCodeBench v6, 86.4% agentic tool use. Supports text, image, and video input. Fits on a single RTX 4090 at Q4 or dual 16 GB GPUs. Direct successor to Gemma 3 27B with substantially better reasoning. Apache 2.0 licensed.
View all Gemma 4 31B hardware options →About Apple M3 (16GB Unified)
Apple M3 (16GB Unified) has 16 GB at 100 GB/s. Available in MacBook Air 13" (2024), MacBook Air 15" (2024).
See all models Apple M3 (16GB Unified) can run →Estimate method: Estimated: model exceeds m3-16gb VRAM capacity. Reference hardware source: github.com (2026-04-18)
Performance varies by driver version, inference engine, quantization method, context length, and system configuration. Figures shown are estimates based on community benchmarks and may not reflect your exact setup. Product names are trademarks of their respective owners. OwnRig is independent and not affiliated with any hardware or AI model provider.