MacBook Pro M3 Max 128GBBudget pick
128 GB Unified (+32)
B
Adds memory headroom for longer context windows and future model growth.6.4 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
DeepSeek LLM 67B needs ~57.9 GB VRAM. MacBook Pro M2 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~6 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
6.2 tok/s
TTFT
31361 ms
Safe context
4K
Memory
57.9 GB / 69.1 GB
The model fits in shared memory, but shared-memory bandwidth is now the real limiter.
Fit does not mean dedicated-VRAM speed
Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.
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.
Prioritize bandwidth, not only capacity
If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 6.2 tok/s | 17106 ms | 4K |
| Coding | B | Tight fit | 6.2 tok/s | 31361 ms | 4K |
| Agentic Coding | B | Tight fit | 6.2 tok/s | 45616 ms | 4K |
| Reasoning | B | Tight fit | 6.2 tok/s | 37063 ms | 4K |
| RAG | B | Tight fit | 6.2 tok/s | 57020 ms | 4K |
How DeepSeek LLM 67B (67B params) fits at each quantization level on MacBook Pro M2 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.
~$2,499 MSRP
Raises estimated decode speed by about 98%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Raises estimated decode speed by about 89%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
NVIDIA H100 80GBBiggest leapRaises estimated decode speed by about 1108%.
Moves the workload away from shared memory into dedicated accelerator memory.
~$40,000 MSRP
Yes, MacBook Pro M2 Max 96GB can run DeepSeek LLM 67B with a B grade (Tight fit). Expected decode speed: 6.2 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 M2 Max 96GB, DeepSeek LLM 67B achieves approximately 6.2 tokens per second decode speed with a time-to-first-token of 31361ms using Q4_K_M quantization.
For coding workloads, DeepSeek LLM 67B on MacBook Pro M2 Max 96GB receives a B grade with 6.2 tok/s and 4K context.
On MacBook Pro M2 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-m2-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 |
Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
Not always. MacBook Pro M2 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.