Can I run Llama 3.2 1B on Apple M3 Max (64GB)?
Yes: Llama 3.2 1B runs on Apple M3 Max (64GB) with F16 at roughly 95 tokens/s. Estimated memory 3.2 GiB at 8,192 tokens of context.
With F16 you can push the context to about 131,072 tokens before memory runs out (F16 KV cache).
Memory by quantisation (8,192-token context)
| Quantisation | Memory | Verdict | Est. speed |
|---|---|---|---|
| F16Unquantized reference | 3.2 GiB | Fits | ~95 tok/s |
| Q8_0Near-lossless | 2.2 GiB | Fits | ~164 tok/s |
| Q6_KVery close to F16 | 1.9 GiB | Fits | ~202 tok/s |
| Q5_K_MGreat quality/size | 1.8 GiB | Fits | ~226 tok/s |
| Q4_K_MBest default for most | 1.6 GiB | Fits | ~255 tok/s |
| IQ4_XSSmaller 4-bit (imatrix) | 1.6 GiB | Fits | ~280 tok/s |
| Q3_K_MNoticeable loss | 1.5 GiB | Fits | ~297 tok/s |
| Q2_KLast resort; big loss | 1.4 GiB | Fits | ~330 tok/s |
Apple M3 Max (64GB) offers about 64 GiB of memory (75% usable for a model) at 400 GB/s. Speed is a bandwidth-bound estimate of single-stream generation at 65% efficiency; real results vary by runtime, drivers and settings, so measure on your machine.
How this is calculated
Weights ≈ parameters × bits per weight ÷ 8. KV cache ≈ 2 × 16 layers × 8 KV heads × 64 head dim × 2 bytes per token. Decode speed ≈ efficiency × memory bandwidth ÷ bytes read per token. Learn the maths in VRAM math and memory bandwidth, or try the VRAM calculator.
Llama 3.2 1B on other hardware
Similar models on Apple M3 Max (64GB)
Running slowly or out of memory? See CUDA out of memory and the quantisation lesson.