Yes — Llama 3.1 8B (8B) runs at 138 tok/s on M5 Max with 128 GB RAM using Q4_K_M quantization via MLX. First token latency is 0.3s. Meta's widely-adopted 8B Llama 3.1 model with great ecosystem support.
Want to run Llama 3.1 8B faster — or step up to bigger models? Find your Mac →The LLMCheck index estimates Llama 3.1 8B on M5 Max 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 | 138 tok/s |
| Time to first token | 0.3s |
| Quantization | Q4_K_M |
| Minimum RAM | 128 GB |
| Recommended engine | MLX |
| Parameters | 8B |
| Benchmark date | 2026-03 |
Q4_K_M 8B MLX M5 Max
The recommended engine for Llama 3.1 8B on M5 Max is MLX. Install with pip and pull the model:
Alternatively, you can use Ollama for a simpler setup:
| Chip | Speed | First Token | Min RAM | Engine |
|---|---|---|---|---|
| M4 | 75 tok/s | 0.6s | 16 GB | Ollama |
| M3 Pro | 62 tok/s | 0.7s | 18 GB | Ollama |
| M2 | 48 tok/s | 0.8s | 8 GB | Ollama |
| M1 | 40 tok/s | 1.1s | 16 GB | Ollama |
To run Llama 3.1 8B on M5 Max you need:
Llama 3.1 8B needs about 128 GB of unified memory. These current Apple Silicon Macs have the headroom to run it comfortably:
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See how Llama 3.1 8B stacks up against other models on your specific Mac hardware.
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