DeepSeek R1 Distill 32B needs ~31.2 GB VRAM. Mac mini M4 64GB has 46.1 GB. With Q4_K_M quantization, expect ~4 tok/s.
Operating mode
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
Fit status
Runs well
Decode
8.6 tok/s
TTFT
22500 ms
Safe context
33K
Memory
31.2 GB / 46.1 GB
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.
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 8.6 tok/s | 12273 ms | 33K |
| Coding | A | Runs well | 4.4 tok/s | 43739 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 |
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 |
Copy-paste commands to run DeepSeek R1 Distill 32B on your machine.
Run
ollama run deepseek-r1:32bYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen 3.6 35B A3B | 35B | S | 12.1 tok/s | |
|
Yes, Mac mini M4 64GB can run DeepSeek R1 Distill 32B with a A grade (Runs well). Expected decode speed: 4.4 tok/s.
DeepSeek R1 Distill 32B (32B parameters) requires approximately 31.2 GB of memory with Q4_K_M quantization.
The recommended quantization for DeepSeek R1 Distill 32B is Q4_K_M, which balances quality and memory efficiency.
On Mac mini M4 64GB, DeepSeek R1 Distill 32B achieves approximately 4.4 tokens per second decode speed with a time-to-first-token of 43739ms using Q4_K_M quantization.
For coding workloads, DeepSeek R1 Distill 32B on Mac mini M4 64GB receives a A grade with 4.4 tok/s and 33K context.
On Mac mini M4 64GB, DeepSeek R1 Distill 32B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/deepseek-r1-distill-32b-on-m4-mini-64gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
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 |
| 35B |
| S |
| 13.1 tok/s |
 Kimi Linear 48B A3B | 48B | A | 5.3 tok/s |
 Granite Code 34B | 34B | A | 8.1 tok/s |
 Command R 35B | 35B | A | 7.9 tok/s |
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.
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.