Mac Studio M2 Ultra 128GB经济之选
128 GB Unified (+32)800 GB/s (+254)
B
Adds memory headroom for longer context windows and future model growth.12.3 tok/s 解码
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Can DeepSeek LLM 67B run on MacBook Pro M4 Max 96GB?
YES — Tight Fit
B58Good
Estimated from fit model
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.
Runtime: llama.cppCapacity: TightBandwidth: MediumStack: 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) — 57.9 GB, 16.0 tok/s, Tight fit
57.9 GB required69.1 GB available
84% VRAM used
Fit status
Tight fit
Decode
16.0 tok/s
TTFT
12114 ms
Safe context
4K
Memory
57.9 GB / 69.1 GB
Memory breakdown
Weights40.9 GB
KV Cache5.8 GB
Runtime0.9 GB
Headroom10.4 GB
See how fast it feels
See how fast it feelsDeepSeek LLM 67B on MacBook Pro M4 Max 96GB
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: 16.0 tok/s decode · 12.1s TTFT (warm) · 40 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 | 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 |
Inference speed
DeepSeek LLM 67B inference speed — tokens per second by GPU & Mac
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.
Quantization options
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 | 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 |
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升级选项
能流畅运行 DeepSeek LLM 67B 的硬件
Mac Studio M1 Ultra 128GB最佳性价比
128 GB Unified (+32)800 GB/s (+254)
B
Adds memory headroom for longer context windows and future model growth.11.7 tok/s 解码
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
MacBook Pro M4 Max 128GBApple 升级
128 GB Unified (+32)
B
Adds memory headroom for longer context windows and future model growth.16 tok/s 解码
Adds memory headroom for longer context windows and future model growth.
~$4,999 MSRP
NVIDIA H100 80GB最大飞跃3350 GB/s (+2804)
B
Raises estimated decode speed by about 368%.74.9 tok/s 解码
Raises estimated decode speed by about 368%.
~$40,000 MSRP
Frequently asked questions
Can MacBook Pro M4 Max 96GB run DeepSeek LLM 67B?
Yes, MacBook Pro M4 Max 96GB can run DeepSeek LLM 67B with a B grade (Tight fit). Expected decode speed: 16.0 tok/s.
How much VRAM does DeepSeek LLM 67B need?
DeepSeek LLM 67B (67B parameters) requires approximately 57.9 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 MacBook Pro M4 Max 96GB?
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.
Can MacBook Pro M4 Max 96GB run DeepSeek LLM 67B for coding?
For coding workloads, DeepSeek LLM 67B on MacBook Pro M4 Max 96GB receives a B grade with 16.0 tok/s and 4K context.
What context window can DeepSeek LLM 67B use on MacBook Pro M4 Max 96GB?
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.
Is unified memory on MacBook Pro M4 Max 96GB as fast as VRAM for DeepSeek LLM 67B?
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.
Embed this result▼
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: