DeepSeek LLM 67B needs ~57.9 GB VRAM. MacBook Pro M4 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~16 tok/s.
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
Fit status
Tight fit
Decode
16.0 tok/s
TTFT
12114 ms
Safe context
4K
Memory
57.9 GB / 69.1 GB
This setup is broadly balanced for this model.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 16.0 tok/s | 6608 ms | 4K |
| Coding | B | Tight fit | 16.0 tok/s | 12114 ms | 4K |
| Agentic Coding | B | Tight fit | 16.0 tok/s | 17621 ms | 4K |
| Reasoning | B | Tight fit | 16.0 tok/s | 14317 ms | 4K |
| RAG | B | Tight fit | 16.0 tok/s | 22026 ms | 4K |
How DeepSeek LLM 67B (67B params) fits at each quantization level on MacBook Pro M4 Max 96GB (69.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 26.1 GB | Low | C55 |
Q3_K_S | 3 | 32.8 GB | Low | B57 |
NVFP4 | 4 |
Copy-paste commands to run DeepSeek LLM 67B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "deepseek-ai/deepseek-llm-67b-chat" \
--hf-file "deepseek-llm-67b-chat-Q4_K_M.gguf" \
-c 4096 -ngl 99Upgrade options
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Adds memory headroom for longer context windows and future model growth.
~$4,999 MSRP
NVIDIA H100 80GBBiggest leapRaises estimated decode speed by about 368%.
~$40,000 MSRP
Yes, MacBook Pro M4 Max 96GB can run DeepSeek LLM 67B with a B grade (Tight fit). Expected decode speed: 16.0 tok/s.
DeepSeek LLM 67B (67B parameters) requires approximately 57.9 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 M4 Max 96GB, DeepSeek LLM 67B achieves approximately 16.0 tokens per second decode speed with a time-to-first-token of 12114ms using Q4_K_M quantization.
For coding workloads, DeepSeek LLM 67B on MacBook Pro M4 Max 96GB receives a B grade with 16.0 tok/s and 4K context.
On MacBook Pro M4 Max 96GB, 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/deepseek-llm-67b-on-m4-max-96gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
37.5 GB |
| Medium |
| B58 |
Q4_K_M | 4 | 40.9 GB | Medium | B58 |
Q5_K_M | 5 | 48.2 GB | High | B58 |
Q6_KBest for your GPU | 6 | 54.9 GB | High | B58 |
Q8_0 | 8 | 71.7 GB | Very High | F0 |
F16 | 16 | 137.4 GB | Maximum | F0 |
Not always. MacBook Pro M4 Max 96GB 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.