Can DeepSeek R1 Distill 32B run on MacBook Pro M1 Max 64GB?

YES — Runs Great

A75Great

○Estimated from fit model

DeepSeek R1 Distill 32B needs ~31.2 GB VRAM. MacBook Pro M1 Max 64GB has 46.1 GB. With Q4_K_M quantization, expect ~12 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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) — 31.2 GB, 12.2 tok/s, Runs well

31.2 GB required46.1 GB available

68% VRAM used

Fit status

Runs well

Decode

12.2 tok/s

TTFT

15905 ms

Safe context

33K

Memory

31.2 GB / 46.1 GB

Memory breakdown

Weights19.5 GB

KV Cache3.9 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsDeepSeek R1 Distill 32B on MacBook Pro M1 Max 64GB
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: 12.2 tok/s decode · 15.9s TTFT (warm) · 30 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	A	Runs well	12.2 tok/s	8676 ms	33K
Coding	A	Runs well	12.2 tok/s	15905 ms	33K
Agentic Coding	A	Runs well	12.2 tok/s	23135 ms	33K
Reasoning	A	Runs well	12.2 tok/s	18797 ms	33K
RAG	A	Runs well	12.2 tok/s	28919 ms	33K

Quantization options

How DeepSeek R1 Distill 32B (32B params) fits at each quantization level on MacBook Pro M1 Max 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	B69
Q3_K_S	3	15.7 GB	Low	A71
NVFP4	4	17.9 GB	Medium	A71
Q4_K_M	4	19.5 GB	Medium	A72
Q5_K_M	5	23.0 GB	High	A73
Q6_K	6	26.2 GB	High	A74
Q8_0Best for your GPU	8	34.2 GB	Very High	A73
F16	16	65.6 GB	Maximum	F0

Get started

Copy-paste commands to run DeepSeek R1 Distill 32B on your machine.

Run

ollama run deepseek-r1:32b

Your hardware

More models your MacBook Pro M1 Max 64GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3.6 35B A3B	35B	S	30.8 tok/s
👁 Alibaba Qwen 3.5 35B A3B	35B	S	33.4 tok/s
👁 Moonshot AI Kimi Linear 48B A3B	48B	A	7.5 tok/s
👁 IBM Granite Code 34B	34B	A	11.5 tok/s
👁 Cohere Command R 35B	35B	A	11.2 tok/s

Frequently asked questions

See all results for MacBook Pro M1 Max 64GB See all hardware for DeepSeek R1 Distill 32B

URL: https://willitrunai.com/can-run/deepseek-r1-distill-32b-on-m1-max-64gb

⇱ DeepSeek R1 Distill 32B on MacBook Pro M1 Max 64GB? YES