RTX PRO 6000 Blackwell Workstation Edition 96GBコスパ重視1792 GB/s (+992)
B
Raises estimated decode speed by about 226%.40.1 tok/sデコード
Raises estimated decode speed by about 226%.
〜$9,999 MSRP
Can DeepSeek LLM 67B run on Mac Studio M2 Ultra 128GB?
YES — Runs Great
B60Good
Estimated from fit model
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.
Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 61.4 GB, 12.3 tok/s, Runs well
61.4 GB required92.2 GB available
67% VRAM used
Fit status
Runs well
Decode
12.3 tok/s
TTFT
15681 ms
Safe context
4K
Memory
61.4 GB / 92.2 GB
Memory breakdown
Weights40.9 GB
KV Cache5.8 GB
Runtime0.9 GB
Headroom13.8 GB
See how fast it feels
See how fast it feelsDeepSeek LLM 67B on Mac Studio M2 Ultra 128GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.
Given information:
- Speed = 60 mph (miles per hour)
- Time = 2.5 hours
Formula: Distance = Speed x Time
Calculation:
Distance = 60 mph x 2.5 h
= 150 miles
The train travels 150 miles.
Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.
Breakdown:
- First half: 1.25 hours at 60 mph = 75 miles
- Stop: 20 minutes (no distance)
- Second half: 1.25 hours at 60 mph = 75 miles
Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph
The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 12.3 tok/s decode · 15.7s TTFT (warm) · 31 tok/s prefill
What limits this setup
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.
Best improvement path
Performance by workload
| 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 |
Quantization options
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 | 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 |
Get started
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 99アップグレードオプション
DeepSeek LLM 67Bを快適に動かすハードウェア
Frequently asked questions
Can Mac Studio M2 Ultra 128GB run DeepSeek LLM 67B?
Yes, Mac Studio M2 Ultra 128GB can run DeepSeek LLM 67B with a B grade (Runs well). Expected decode speed: 12.3 tok/s.
How much VRAM does DeepSeek LLM 67B need?
DeepSeek LLM 67B (67B parameters) requires approximately 61.4 GB of memory with Q4_K_M quantization.
What is the best quantization for DeepSeek LLM 67B?
The recommended quantization for DeepSeek LLM 67B is Q4_K_M, which balances quality and memory efficiency.
What speed will DeepSeek LLM 67B run at on Mac Studio M2 Ultra 128GB?
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.
Can Mac Studio M2 Ultra 128GB run DeepSeek LLM 67B for coding?
For coding workloads, DeepSeek LLM 67B on Mac Studio M2 Ultra 128GB receives a B grade with 12.3 tok/s and 4K context.
What context window can DeepSeek LLM 67B use on Mac Studio M2 Ultra 128GB?
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.
Is unified memory on Mac Studio M2 Ultra 128GB as fast as VRAM for DeepSeek LLM 67B?
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.
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