Yes — Mistral 7B (7B) runs at 62 tok/s on M3 with 16 GB RAM using Q4_K_M quantization via Ollama. First token latency is 0.6s. Mistral AI's classic 7B model, one of the most widely-supported open models.
Want to run Mistral 7B faster — or step up to bigger models? Find your Mac →The LLMCheck index estimates Mistral 7B on M3 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 | 62 tok/s |
| Time to first token | 0.6s |
| Quantization | Q4_K_M |
| Minimum RAM | 16 GB |
| Recommended engine | Ollama |
| Parameters | 7B |
| Benchmark date | 2026-01 |
Q4_K_M 7B Ollama M3
The recommended engine for Mistral 7B on M3 is Ollama. Install Ollama, then pull the model:
Ollama handles quantization automatically — it will download the Q4_K_M variant (~16 GB) and start an interactive chat session.
| Chip | Speed | First Token | Min RAM | Engine |
|---|---|---|---|---|
| M5 Max | 122 tok/s | 0.3s | 64 GB | Ollama |
| M4 Pro | 98 tok/s | 0.4s | 24 GB | MLX |
| M1 | 42 tok/s | 1.2s | 16 GB | Ollama |
To run Mistral 7B on M3 you need:
Mistral 7B needs about 16 GB of unified memory. These current Apple Silicon Macs have the headroom to run it comfortably:
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See how Mistral 7B stacks up against other models on your specific Mac hardware.
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