Can DeepSeek R1 Distill 32B run on Mac mini M4 64GB?
YES — Runs Great
A74Great▲Estimated — low-sample bucket· few comparable runs
DeepSeek R1 Distill 32B needs ~31.2 GB VRAM. Mac mini M4 64GB has 46.1 GB. With Q4_K_M quantization, expect ~9 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: Very 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) — 31.2 GB, 8.6 tok/s, Runs well
31.2 GB required46.1 GB available
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 Mac mini M4 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: 8.6 tok/s decode · 22.5s TTFT (warm) · 22 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 | 8.6 tok/s | 12273 ms | 33K |
| Coding | A | Runs well | 8.6 tok/s | 22500 ms | 33K |
| Agentic Coding | A | Runs well | 8.6 tok/s | 32727 ms | 33K |
| Reasoning | A | Runs well | 8.6 tok/s | 26590 ms | 33K |
| RAG | A | Runs well | 8.6 tok/s | 40908 ms | 33K |
Quantization options
How DeepSeek R1 Distill 32B (32B params) fits at each quantization level on Mac mini M4 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 |
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 Mac mini M4 64GB can run
Frequently asked questions
Q8_0Best for your GPU | 8 | 34.2 GB | Very High | A73 |
Qwen 3.5 35B A3B
👁 IBM
Granite Code 34B | 34B | A | 8.1 tok/s |
Not always. Mac mini M4 64GB 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.
