RTX PRO 6000 Blackwell Workstation Edition 96GBBudget pick1792 GB/s (+992)
B
Raises estimated decode speed by about 226%.40.1 tok/s decode
Raises estimated decode speed by about 226%.
~$9,999 MSRP
DeepSeek LLM 67B needs ~61.4 GB VRAM. Mac Studio M2 Ultra 128GB has 92.2 GB. With Q4_K_M quantization, expect ~12 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
12.3 tok/s
TTFT
15681 ms
Safe context
4K
Memory
61.4 GB / 92.2 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 | 12.3 tok/s | 8553 ms | 4K |
| Coding | B | Runs well | 12.3 tok/s | 15681 ms | 4K |
| Agentic Coding | B | Runs well | 12.3 tok/s | 22808 ms | 4K |
| Reasoning | B | Runs well | 12.3 tok/s | 18532 ms | 4K |
| RAG | B | Runs well | 12.3 tok/s | 28510 ms | 4K |
How DeepSeek LLM 67B (67B params) fits at each quantization level on Mac Studio M2 Ultra 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 |
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 226%.
~$9,999 MSRP
RTX PRO 6000 Blackwell Server Edition 96GBBest valueRaises estimated decode speed by about 190%.
~$9,999 MSRP
Yes, Mac Studio M2 Ultra 128GB can run DeepSeek LLM 67B with a B grade (Runs well). Expected decode speed: 12.3 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 Mac Studio M2 Ultra 128GB, DeepSeek LLM 67B achieves approximately 12.3 tokens per second decode speed with a time-to-first-token of 15681ms using Q4_K_M quantization.
For coding workloads, DeepSeek LLM 67B on Mac Studio M2 Ultra 128GB receives a B grade with 12.3 tok/s and 4K context.
On Mac Studio M2 Ultra 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/deepseek-llm-67b-on-m2-ultra-128gb" 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 |
| 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 |
Not always. Mac Studio M2 Ultra 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.