DeepSeek LLM 67B needs ~51.0 GB but MacBook Pro M1 Max 32GB only has 23.0 GB. Try a smaller quantization or lighter model.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
28.0 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
2.6 tok/s
TTFT
73493 ms
Safe context
4K
Memory
51.0 GB / 23.0 GB
Offload
50%
Usable shared or unified memory is the main blocker for this model.
Not enough usable memory
The model needs 51.0 GB, but this setup only exposes 23.0 GB of usable shared or unified memory.
Move to a larger memory pool
A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 2.6 tok/s | 40087 ms | 4K |
| Coding | F | Too heavy | 2.6 tok/s | 73493 ms | 4K |
| Agentic Coding | F | Too heavy | 2.6 tok/s | 106899 ms | 4K |
| Reasoning | F | Too heavy | 2.6 tok/s | 86855 ms | 4K |
| RAG | F | Too heavy | 2.6 tok/s | 133623 ms | 4K |
How DeepSeek LLM 67B (67B params) fits at each quantization level on MacBook Pro M1 Max 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 26.1 GB | Low | F0 |
Q3_K_S | 3 | 32.8 GB | Low | F0 |
NVFP4 | 4 | 37.5 GB | Medium | F0 |
Q4_K_M | 4 | 40.9 GB | Medium | F0 |
Q5_K_M | 5 | 48.2 GB | High | F0 |
Q6_K | 6 | 54.9 GB | High | F0 |
Q8_0 | 8 | 71.7 GB | Very High | F0 |
F16 | 16 | 137.4 GB | Maximum | F0 |
Opções de upgrade
Makes the model fit on the accelerator instead of staying completely out of reach.
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 200%.
~$1,599 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$2,499 MSRP
NVIDIA H100 80GBMaior saltoMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$40,000 MSRP
No, DeepSeek LLM 67B requires more memory than MacBook Pro M1 Max 32GB provides.
DeepSeek LLM 67B (67B parameters) requires approximately 51.0 GB of memory with Q4_K_M quantization.
The recommended quantization for DeepSeek LLM 67B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M1 Max 32GB, DeepSeek LLM 67B achieves approximately 2.6 tokens per second decode speed with a time-to-first-token of 73493ms using Q4_K_M quantization.
For coding workloads, DeepSeek LLM 67B on MacBook Pro M1 Max 32GB receives a F grade with 2.6 tok/s and 4K context.
On MacBook Pro M1 Max 32GB, DeepSeek LLM 67B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.
Move to a larger memory pool. A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.
Not always. MacBook Pro M1 Max 32GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.
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<iframe src="https://willitrunai.com/embed/deepseek-llm-67b-on-m1-max-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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