Best overallGood fit
Qwen2.5 72B Instruct
Frontier-adjacent open weights. Needs a workstation, a multi-GPU rig or a large unified-memory Mac.
- Params
- 72.7B
- Quant
- Q6_K
- Memory
- 60 GB
Reference
Best models for MacBook Pro M4 Max (128 GB)
The largest model capacity you can carry in a bag. 128 GB unified memory runs a 70B comfortably; bandwidth caps throughput below a 4090.
29 models fit96 GB usableEstimated
Top picks
Best on MacBook Pro M4 Max (128 GB)
Runner-upGood fit
Llama 3.3 70B Instruct
Delivers close to Llama 3.1 405B quality at a size a dual-GPU workstation or 64 GB Mac can actually hold.
- Params
- 70.6B
- Quant
- Q6_K
- Memory
- 58 GB
ThirdGood fit
Qwen2.5 Coder 32B Instruct
The strongest open code model that still fits one 24 GB card. The reason a lot of people buy a 4090.
- Params
- 32.8B
- Quant
- F16
- Memory
- 64 GB
Full results
29 models
Estimated
| # | Model | Params | Licence | Quantization | Memory | ~tok/s | Fit | Fine-tune |
|---|---|---|---|---|---|---|---|---|
| 1 | Qwen2.5 72B Instruct | 72.7B | Qwen License | Q6_K | 60 GB | 7 | Good fit | Yes |
| 2 | Llama 3.3 70B Instruct | 70.6B | Llama 3.3 Community License | Q6_K | 58 GB | 7 | Good fit | Yes |
| 3 | Qwen2.5 Coder 32B Instruct | 32.8B | Apache 2.0 | F16 | 64 GB | 6 | Good fit | Yes |
| 4 | Mixtral 8x7B Instruct | 46.7B | Apache 2.0 | Q8_0 | 49 GB | 31 | Excellent fit | Yes |
| 5 | Qwen2.5 32B Instruct | 32.8B | Apache 2.0 | F16 | 64 GB | 6 | Good fit | Yes |
| 6 | Gemma 3 27B Instruct | 27.4B | Gemma Terms of Use | F16 | 57 GB | 8 | Excellent fit | Yes |
| 7 | DeepSeek-R1-Distill-Qwen-32B | 32.8B | MIT | F16 | 64 GB | 6 | Good fit | Yes |
| 8 | Phi-4 14B | 14.7B | MIT | F16 | 30 GB | 14 | Excellent fit | Yes |
| 9 | Mistral Small 24B Instruct | 23.6B | Apache 2.0 | F16 | 46 GB | 9 | Excellent fit | Yes |
| 10 | Qwen2.5 14B Instruct | 14.8B | Apache 2.0 | F16 | 30 GB | 14 | Excellent fit | Yes |
| 11 | Qwen3 30B-A3B | 30.5B | Apache 2.0 | F16 | 58 GB | 64 | Good fit | Yes |
| 12 | StarCoder2 15B | 16B | BigCode OpenRAIL-M | F16 | 31 GB | 13 | Excellent fit | Yes |
| 13 | Qwen3 14B | 14.8B | Apache 2.0 | F16 | 30 GB | 14 | Excellent fit | Yes |
| 14 | DeepSeek-R1-Distill-Qwen-14B | 14.8B | MIT | F16 | 30 GB | 14 | Excellent fit | Yes |
| 15 | Mistral Nemo 12B Instruct | 12.2B | Apache 2.0 | F16 | 25 GB | 17 | Excellent fit | Yes |
| 16 | Qwen2.5 Coder 7B Instruct | 7.6B | Apache 2.0 | F16 | 15 GB | 28 | Excellent fit | Yes |
| 17 | Gemma 3 12B Instruct | 12.2B | Gemma Terms of Use | F16 | 26 GB | 17 | Excellent fit | Yes |
| 18 | DeepSeek-Coder-V2-Lite Instruct | 15.7B | DeepSeek License | F16 | 32 GB | 88 | Excellent fit | Yes |
| 19 | Gemma 2 9B Instruct | 9.2B | Gemma Terms of Use | F16 | 20 GB | 23 | Excellent fit | Yes |
| 20 | Qwen2.5 7B Instruct | 7.6B | Apache 2.0 | F16 | 15 GB | 28 | Excellent fit | Yes |
| 21 | Llama 3.1 8B Instruct | 8B | Llama 3.1 Community License | F16 | 16 GB | 26 | Excellent fit | Yes |
| 22 | Qwen3 8B | 8.2B | Apache 2.0 | F16 | 17 GB | 26 | Excellent fit | Yes |
| 23 | Mistral 7B Instruct v0.3 | 7.25B | Apache 2.0 | F16 | 15 GB | 29 | Excellent fit | Yes |
| 24 | Code Llama 7B Instruct | 6.7B | Llama 2 Community License | F16 | 17 GB | 32 | Excellent fit | Yes |
| 25 | Phi-3.5 Mini Instruct | 3.8B | MIT | F16 | 11 GB | 56 | Excellent fit | Yes |
| 26 | Gemma 3 4B Instruct | 4.3B | Gemma Terms of Use | F16 | 9.9 GB | 49 | Excellent fit | Yes |
| 27 | Llama 3.2 3B Instruct | 3.2B | Llama 3.2 Community License | F16 | 7.3 GB | 66 | Excellent fit | Yes |
| 28 | SmolLM2 1.7B Instruct | 1.7B | Apache 2.0 | F16 | 5.2 GB | 124 | Excellent fit | Yes |
| 29 | Llama 3.2 1B Instruct | 1.24B | Llama 3.2 Community License | F16 | 3.0 GB | 170 | Excellent fit | Yes |
How this page is produced
Memory is calculated from each model’s published geometry against this machine’s usable capacity. Where the vendor publishes a memory bandwidth figure, an order-of-magnitude token rate is derived from it and labelled as an estimate.
- · Trains through MLX. CUDA-only paths such as Unsloth are unavailable.
- · 29 of these can also be fine-tuned on this hardware.
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