Yes — Gemma 4 31B (31B) runs at 8 tok/s on M6 with 32 GB RAM using QAT quantization via LM Studio. Google's flagship 31B dense model, Arena AI #3 among open models.
Want to run Gemma 4 31B faster — or step up to bigger models? Find your Mac →The LLMCheck index estimates Gemma 4 31B on M6 using our published methodology: Q4_K_M quantization, memory-bandwidth scaling, and cross-referenced third-party benchmarks where available. Figures are transparent estimates — own this config? Submit a real benchmark →
| Metric | Value |
|---|---|
| Tokens per second | 8 tok/s |
| Time to first token | — |
| Quantization | QAT |
| Minimum RAM | 32 GB |
| Recommended engine | LM Studio |
| Parameters | 31B |
| Benchmark date | 2026-10 |
QAT 31B LM Studio M6
The recommended engine for Gemma 4 31B on M6 is LM Studio. Install Ollama, then pull the model:
Ollama handles quantization automatically — it will download the QAT variant (~32 GB) and start an interactive chat session.
| Chip | Speed | First Token | Min RAM | Engine |
|---|---|---|---|---|
| M5 Max | 26 tok/s | — | 128 GB | MLX |
| M4 Pro | 12 tok/s | — | 24 GB | Ollama |
To run Gemma 4 31B on M6 you need:
Gemma 4 31B needs about 32 GB of unified memory. These current Apple Silicon Macs have the headroom to run it comfortably:
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See how Gemma 4 31B stacks up against other models on your specific Mac hardware.
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