Can DeepSeek LLM 67B run on Mac Studio M3 Ultra 256GB?

Q: Is unified memory on Mac Studio M3 Ultra 256GB as fast as VRAM for DeepSeek LLM 67B?

Not always. Mac Studio M3 Ultra 256GB 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.

YES — Runs Great

C54Usable

Estimated from fit model

DeepSeek LLM 67B needs ~75.2 GB VRAM. Mac Studio M3 Ultra 256GB has 184.3 GB. With Q4_K_M quantization, expect ~14 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 75.2 GB, 14.8 tok/s, Runs well

75.2 GB required184.3 GB available

41% VRAM used

Fit status

Runs well

Decode

14.8 tok/s

TTFT

13064 ms

Safe context

Memory

75.2 GB / 184.3 GB

Memory breakdown

Weights40.9 GB

KV Cache5.8 GB

Runtime0.9 GB

Headroom27.6 GB

See how fast it feels

See how fast it feelsDeepSeek LLM 67B on Mac Studio M3 Ultra 256GB
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: 14.8 tok/s decode · 13.1s TTFT (warm) · 37 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	C	Runs well	14.8 tok/s	7126 ms	4K
Coding	C	Runs well	13.6 tok/s	14207 ms	4K
Agentic Coding	B	Runs well	14.8 tok/s	19003 ms	4K
Reasoning	C	Runs well	14.8 tok/s	15440 ms	4K
RAG	B	Runs well	14.8 tok/s	23753 ms	4K

Quantization options

How DeepSeek LLM 67B (67B params) fits at each quantization level on Mac Studio M3 Ultra 256GB (184.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	26.1 GB	Low	C48
Q3_K_S	3	32.8 GB	Low	C49
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 99

Upgrade options

Hardware that runs DeepSeek LLM 67B well

AMD Instinct MI350X 288GBBudget pick

288 GB VRAM (+32)8000 GB/s (+7181)

Raises estimated decode speed by about 950%.155.4 tok/s decode

Raises estimated decode speed by about 950%.

Adds memory headroom for longer context windows and future model growth.

~$8,000 MSRP

AMD Instinct MI300X 192GBBest value

5300 GB/s (+4481)

Raises estimated decode speed by about 643%.110 tok/s decode

Raises estimated decode speed by about 643%.

~$15,000 MSRP

Frequently asked questions

Can Mac Studio M3 Ultra 256GB run DeepSeek LLM 67B?

Yes, Mac Studio M3 Ultra 256GB can run DeepSeek LLM 67B with a C grade (Runs well). Expected decode speed: 13.6 tok/s.

How much VRAM does DeepSeek LLM 67B need?

DeepSeek LLM 67B (67B parameters) requires approximately 75.2 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 M3 Ultra 256GB?

On Mac Studio M3 Ultra 256GB, DeepSeek LLM 67B achieves approximately 13.6 tokens per second decode speed with a time-to-first-token of 14207ms using Q4_K_M quantization.

Can Mac Studio M3 Ultra 256GB run DeepSeek LLM 67B for coding?

For coding workloads, DeepSeek LLM 67B on Mac Studio M3 Ultra 256GB receives a C grade with 13.6 tok/s and 4K context.

What context window can DeepSeek LLM 67B use on Mac Studio M3 Ultra 256GB?

On Mac Studio M3 Ultra 256GB, 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 M3 Ultra 256GB as fast as VRAM for DeepSeek LLM 67B?

See all results for Mac Studio M3 Ultra 256GB See all hardware for DeepSeek LLM 67B

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