Can an M1 Pro 16GB Mac run DeepSeek-R1-Distill-Llama-70B?
M1 Pro · 16 GB unified memory · 200 GB/s · MacBook Pro 14" (M1 Pro), MacBook Pro 16" (M1 Pro)
DeepSeek-R1-Distill-Llama-70B needs about 45.6 GiB but an M1 Pro 16GB Mac has only ~13.0 GiB available for a model — you are 32.6 GiB short. macOS will swap to SSD and generation will crawl.
Where the memory goes
Three things occupy memory when a model is loaded: the weights themselves, the KV cache that holds the conversation, and the runtime's own working buffers. Only the first is fixed — the KV cache grows with every token in your context.
| Quantisation | Weights | Total | Tok/s | Fits? |
|---|---|---|---|---|
| FP16 | 131.5 GiB | 141.4 GiB | 1 | No |
| Q8_0 | 69.9 GiB | 76.7 GiB | 2 | No |
| Q6_K | 54.2 GiB | 60.3 GiB | 3 | No |
| Q5_K_M | 46.8 GiB | 52.5 GiB | 3 | No |
| Q4_K_M | 40.3 GiB | 45.6 GiB | 4 | No |
| Q3_K_M | 32.1 GiB | 37.0 GiB | 5 | No |
| Q2_K | 24.7 GiB | 29.2 GiB | 6 | No |
The default. Best quality-per-gigabyte for most people.
How long a context fits
The KV cache is often what breaks a setup that looked fine at load time. Quantising it to 8-bit roughly halves its footprint with little measurable quality cost.
| Context | KV cache | Total | FP16 KV | Q8 KV |
|---|---|---|---|---|
| 2K | 0.6 GiB | 43.5 GiB | No | No |
| 4K | 1.3 GiB | 44.2 GiB | No | No |
| 8K | 2.5 GiB | 45.6 GiB | No | No |
| 16K | 5.0 GiB | 48.4 GiB | No | No |
| 32K | 10.0 GiB | 54.0 GiB | No | No |
| 128K | 40.0 GiB | 87.6 GiB | No | No |
What to run instead on an M1 Pro 16GB Mac
These are the largest models that fit comfortably on this machine at Q4_K_M with an 8K context.
| Model | Size | Total | Tok/s | |
|---|---|---|---|---|
| Qwen3 14B | 14.8B | 10.9 GiB | 18 | Yes, comfortably |
| Gemma 2 9B | 9.24B | 9.0 GiB | 28 | Yes, comfortably |
| Qwen3 8B | 8.2B | 6.8 GiB | 32 | Yes, comfortably |
| Llama 3.1 8B | 8.03B | 6.6 GiB | 33 | Yes, comfortably |
| DeepSeek-R1-Distill-Qwen-7B | 7.62B | 5.8 GiB | 34 | Yes, comfortably |
| Qwen2.5 7B | 7.62B | 5.8 GiB | 34 | Yes, comfortably |
Macs that run DeepSeek-R1-Distill-Llama-70B comfortably
The smallest configuration that handles it is the M1 Max 64GB (MacBook Pro 16" (M1 Max)).
| Mac | Memory | Bandwidth | Tok/s |
|---|---|---|---|
| M1 Max 64GB | 64 GB | 400 GB/s | 7 |
| M2 Ultra 64GB | 64 GB | 800 GB/s | 15 |
| M4 Pro 64GB | 64 GB | 273 GB/s | 5 |
| M4 Max 64GB | 64 GB | 546 GB/s | 10 |
| M2 Max 96GB | 96 GB | 400 GB/s | 7 |
| M3 Ultra 96GB | 96 GB | 819 GB/s | 15 |
| M3 Max 128GB | 128 GB | 400 GB/s | 7 |
| M4 Max 128GB | 128 GB | 546 GB/s | 10 |
| M2 Ultra 192GB | 192 GB | 800 GB/s | 15 |
| M3 Ultra 256GB | 256 GB | 819 GB/s | 15 |
| M3 Ultra 512GB | 512 GB | 819 GB/s | 15 |
Common questions
- Can an M1 Pro 16GB Mac run DeepSeek-R1-Distill-Llama-70B?
- No. At Q4_K_M it needs about 45.6 GiB, and an M1 Pro 16GB Mac has roughly 13.0 GiB available for a model after macOS takes its share.
- How fast is DeepSeek-R1-Distill-Llama-70B on an Mac?
- Around 4 tokens per second. Token generation on Apple Silicon is limited by memory bandwidth, and this machine has 200 GB/s.
- How much memory does DeepSeek-R1-Distill-Llama-70B need?
- 40.3 GiB for the weights at Q4_K_M, plus 2.5 GiB for an 8K-token KV cache and about 2.8 GiB of runtime overhead — 45.6 GiB in total.