DeepSeek R1 Distill Qwen 14B needs ~18.0 GB VRAM. Mac mini M4 64GB has 46.1 GB. With Q4_K_M quantization, expect ~10 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
8.9 tok/s
TTFT
21745 ms
Safe context
290K
Memory
18.0 GB / 46.1 GB
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 8.9 tok/s | 11861 ms | 290K |
| Coding | C | Runs well | 10.1 tok/s | 19136 ms | 290K |
| Agentic Coding | C | Runs well | 8.9 tok/s | 31630 ms | 290K |
| Reasoning | C | Runs well | 8.9 tok/s | 25699 ms | 290K |
| RAG | C | Runs well | 8.9 tok/s | 39537 ms | 290K |
How DeepSeek R1 Distill Qwen 14B (14B params) fits at each quantization level on Mac mini M4 64GB (46.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | C42 |
Q3_K_S | 3 | 6.9 GB | Low | C42 |
NVFP4 | 4 |
Copy-paste commands to run DeepSeek R1 Distill Qwen 14B on your machine.
Run
lms load hf-unsloth--deepseek-r1-distill-qwen-14b-gguf && lms server startUpgrade options
Raises estimated decode speed by about 298%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Raises estimated decode speed by about 216%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Raises estimated decode speed by about 633%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Yes, Mac mini M4 64GB can run DeepSeek R1 Distill Qwen 14B with a C grade (Runs well). Expected decode speed: 10.1 tok/s.
DeepSeek R1 Distill Qwen 14B (14B parameters) requires approximately 18.0 GB of memory with Q4_K_M quantization.
The recommended quantization for DeepSeek R1 Distill Qwen 14B is Q4_K_M, which balances quality and memory efficiency.
On Mac mini M4 64GB, DeepSeek R1 Distill Qwen 14B achieves approximately 10.1 tokens per second decode speed with a time-to-first-token of 19136ms using Q4_K_M quantization.
For coding workloads, DeepSeek R1 Distill Qwen 14B on Mac mini M4 64GB receives a C grade with 10.1 tok/s and 290K context.
On Mac mini M4 64GB, DeepSeek R1 Distill Qwen 14B can safely use up to 290K tokens of context. The model's official context limit is —, 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/hf-unsloth--deepseek-r1-distill-qwen-14b-gguf-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:
7.8 GB |
| Medium |
| C43 |
Q4_K_M | 4 | 8.5 GB | Medium | C43 |
Q5_K_M | 5 | 10.1 GB | High | C43 |
Q6_K | 6 | 11.5 GB | High | C44 |
Q8_0 | 8 | 15.0 GB | Very High | C45 |
F16Best for your GPU | 16 | 28.7 GB | Maximum | C48 |
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.