DeepSeek LLM 67B needs ~61.4 GB VRAM. MacBook Pro M3 Max 128GB has 92.2 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
Runs well
Decode
6.4 tok/s
TTFT
30316 ms
Safe context
4K
Memory
61.4 GB / 92.2 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 | 6.4 tok/s | 16536 ms | 4K |
| Coding | B | Runs well | 6.4 tok/s | 30316 ms | 4K |
| Agentic Coding | B | Runs well | 6.4 tok/s | 44096 ms | 4K |
| Reasoning | B | Runs well | 6.4 tok/s | 35828 ms | 4K |
| RAG | B | Runs well | 6.4 tok/s | 55120 ms | 4K |
Inference speed
Estimated decode speed (tokens/sec) for DeepSeek LLM 67B at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is 2× RX 7900 XTX 24GB at ~20 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
2× RX 7900 XTX 24GB | 48 GB | Q4_K_M | 19.5 | Heavy offload |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 16.0 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 14.8 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 12.4 | Heavy offload |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 12.3 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 11.7 | Fits |
 2× RTX 4090 24GB | 48 GB | Q4_K_M | 10.3 | Heavy offload |
| 2× RTX 3090 24GB | 48 GB | Q4_K_M | 9.4 | Heavy offload |
| 4× RTX 3060 12GB | 48 GB | Q4_K_M | 8.3 | Heavy offload |
| RTX 5090 32GB | 32 GB | Q4_K_M | 6.2 | Too big |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 5.8 | Too big |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 5.0 | Heavy offload |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 4.5 | Heavy offload |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 2.9 | Too big |
| RTX 4090 24GB | 24 GB | Q4_K_M | 2.0 | Too big |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 2.0 | Too big |
| RTX 3090 24GB | 24 GB | Q4_K_M | 2.0 | Too big |
| RTX 4070 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 4060 8GB | 8 GB | Q4_K_M | 2.0 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How DeepSeek LLM 67B (67B params) fits at each quantization level on MacBook Pro M3 Max 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 26.1 GB | Low | C52 |
Q3_K_S | 3 | 32.8 GB | Low | C54 |
NVFP4 | 4 | 37.5 GB | Medium | C55 |
Q4_K_M | 4 | 40.9 GB | Medium | B55 |
Q5_K_M | 5 | 48.2 GB | High | B57 |
Q6_K | 6 | 54.9 GB | High | B58 |
Q8_0Best for your GPU | 8 | 71.7 GB | Very High | B58 |
F16 | 16 | 137.4 GB | Maximum | F0 |
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
RTX PRO 6000 Blackwell Workstation Edition 96GBBudget pickRaises estimated decode speed by about 527%.
Moves the workload away from shared memory into dedicated accelerator memory.
~$9,999 MSRP
RTX PRO 6000 Blackwell Server Edition 96GBBest valueRaises estimated decode speed by about 458%.
Moves the workload away from shared memory into dedicated accelerator memory.
~$9,999 MSRP
Yes, MacBook Pro M3 Max 128GB can run DeepSeek LLM 67B with a B grade (Runs well). Expected decode speed: 6.4 tok/s.
DeepSeek LLM 67B (67B parameters) requires approximately 61.4 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 M3 Max 128GB, DeepSeek LLM 67B achieves approximately 6.4 tokens per second decode speed with a time-to-first-token of 30316ms using Q4_K_M quantization.
For coding workloads, DeepSeek LLM 67B on MacBook Pro M3 Max 128GB receives a B grade with 6.4 tok/s and 4K context.
On MacBook Pro M3 Max 128GB, 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.
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 M3 Max 128GB 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-m3-max-128gb" 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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