MacBook Pro M3 Max 128GBBudget pick
128 GB Unified (+32)
B
Adds memory headroom for longer context windows and future model growth.6.4 tok/s decode
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
Share:
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 | 6.2 tok/s | 17106 ms | 4K |
| Coding | B | Tight fit | 6.2 tok/s | 31361 ms | 4K |
| Agentic Coding | B | Tight fit | 6.2 tok/s | 45616 ms | 4K |
| Reasoning | B | Tight fit | 6.2 tok/s | 37063 ms | 4K |
| RAG | B | Tight fit | 6.2 tok/s | 57020 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 |
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 99Upgrade options
Hardware that runs DeepSeek LLM 67B well
Mac Studio M2 Ultra 128GBBest value
128 GB Unified (+32)800 GB/s (+400)
B
Raises estimated decode speed by about 98%.12.3 tok/s decode
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 upgrade
128 GB Unified (+32)800 GB/s (+400)
B
Raises estimated decode speed by about 89%.11.7 tok/s decode
Raises estimated decode speed by about 89%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
3350 GB/s (+2950)
B
Raises estimated decode speed by about 1108%.74.9 tok/s decode
Raises estimated decode speed by about 1108%.
Moves the workload away from shared memory into dedicated accelerator memory.
~$40,000 MSRP