MacBook Pro M3 Max 128GBコスパ重視
128 GB Unified (+32)
B
Adds memory headroom for longer context windows and future model growth.6.4 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$2,499 MSRP
Can DeepSeek LLM 67B run on MacBook Pro M2 Max 96GB?
YES — Tight Fit
B55Good
Estimated from fit model
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.
Runtime: llama.cppCapacity: TightBandwidth: LowStack: StandardBottleneck: Memory bandwidth
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, 6.2 tok/s, Tight fit
57.9 GB required69.1 GB available
84% VRAM used
Fit status
Tight fit
Decode
6.2 tok/s
TTFT
31361 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 M2 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: 6.2 tok/s decode · 31.4s TTFT (warm) · 15 tok/s prefill
What limits this setup
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.
Best improvement path
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.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 5.7 tok/s | 18603 ms | 4K |
| Coding | B | Tight fit | 5.7 tok/s | 34105 ms | 4K |
| Agentic Coding | B | Tight fit | 5.7 tok/s | 49608 ms | 4K |
| Reasoning | B | Tight fit | 5.7 tok/s | 40306 ms | 4K |
| RAG | B | Tight fit | 5.7 tok/s | 62010 ms | 4K |
Quantization options
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 | 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
Mac Studio M2 Ultra 128GBコスパ最良
128 GB Unified (+32)800 GB/s (+400)
B
Raises estimated decode speed by about 98%.12.3 tok/sデコード
Raises estimated decode speed by about 98%.
Adds memory headroom for longer context windows and future model growth.
〜$3,999 MSRP
Mac Studio M1 Ultra 128GBAppleアップグレード
128 GB Unified (+32)800 GB/s (+400)
B
Raises estimated decode speed by about 89%.11.7 tok/sデコード
Raises estimated decode speed by about 89%.
Adds memory headroom for longer context windows and future model growth.
〜$3,999 MSRP
NVIDIA H100 80GB最大の飛躍3350 GB/s (+2950)
B
Raises estimated decode speed by about 1108%.74.9 tok/sデコード
Raises estimated decode speed by about 1108%.
Moves the workload away from shared memory into dedicated accelerator memory.
〜$40,000 MSRP
Frequently asked questions
Can MacBook Pro M2 Max 96GB run DeepSeek LLM 67B?
Yes, MacBook Pro M2 Max 96GB can run DeepSeek LLM 67B with a B grade (Tight fit). Expected decode speed: 5.7 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 M2 Max 96GB?
On MacBook Pro M2 Max 96GB, DeepSeek LLM 67B achieves approximately 5.7 tokens per second decode speed with a time-to-first-token of 34105ms using Q4_K_M quantization.
Can MacBook Pro M2 Max 96GB run DeepSeek LLM 67B for coding?
For coding workloads, DeepSeek LLM 67B on MacBook Pro M2 Max 96GB receives a B grade with 5.7 tok/s and 4K context.
What context window can DeepSeek LLM 67B use on MacBook Pro M2 Max 96GB?
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.
What should I upgrade first if DeepSeek LLM 67B feels slow on MacBook Pro M2 Max 96GB?
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.
Is unified memory on MacBook Pro M2 Max 96GB as fast as VRAM for DeepSeek LLM 67B?
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.
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-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: