Can the Apple M1 (16GB) run Llama 3.1 8B?
Yes
QuantQ8_0Near-lossless
Memory10.2 GBof 10.7 GB
Speed~5tokens/s
Yes. The Apple M1 (16GB) can run Llama 3.1 8B fully in unified memory at Q8_0 (near-lossless), using about 10.2 GB of the 10.7 GB available, at roughly 5 tokens/second.
Try other context lengths in the calculator →Memory needed by quantization
At 8,192 tokens of context. macOS lets the GPU use about 10.7 GB of the 16 GB unified memory by default.
| Quant | Quality | Memory | Fits? | Speed |
|---|---|---|---|---|
| FP16 | Full quality | 17.7 GB | Too big | — |
| Q8_0 | Near-lossless | 10.2 GB | Fits | ~5 tok/s |
| Q6_K | Excellent | 8.3 GB | Fits | ~6 tok/s |
| Q5_K_M | Very good | 7.4 GB | Fits | ~7 tok/s |
| Q4_K_M | Good, most popular | 6.5 GB | Fits | ~8 tok/s |
| Q3_K_M | Noticeably degraded | 5.6 GB | Fits | ~10 tok/s |
| Q2_K | Heavily degraded | 4.7 GB | Fits | ~14 tok/s |
How to run Llama 3.1 8B on the Apple M1 (16GB)
With Ollama installed, one command downloads and starts it:
ollama run llama3.1:8bOllama's default download is Q4_K_M (about 6.5 GB here). You have room for Q8_0, which is near-lossless: pick that GGUF in LM Studio or from Hugging Face.
GPUs that run Llama 3.1 8B comfortably
- NVIDIA RTX 4060 (8 GB)
- NVIDIA RTX 3070 (8 GB)
- NVIDIA RTX 3080 10GB
- NVIDIA RTX 3060 12GB
- NVIDIA RTX 4070 (12 GB)
Models that run well on the Apple M1 (16GB)
Run it in the cloud
Don't want to buy hardware? Run Llama 3.1 8B on a rented GPU today.
Rent a cloud GPU by the hour. No upfront cost, cancel any time.