MacBook Pro M4 32GBBudget pick
32 GB Unified (+14)
C
Adds memory headroom for longer context windows and future model growth.8.9 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$799 MSRP
DeepSeek R1 Distill Qwen 14B needs ~13.0 GB VRAM. MacBook Pro M3 Pro 18GB has 13.0 GB. With Q4_K_M quantization, expect ~13 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 with offload (needs ~0 GB host RAM)
Decode
12.6 tok/s
TTFT
15319 ms
Safe context
15K
Memory
13.0 GB / 13.0 GB
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 12.8 tok/s | 8236 ms | 15K |
| Coding | C | Runs with offload | 12.6 tok/s | 15319 ms | 15K |
| Agentic Coding | D | Very compromised (needs ~1 GB host RAM) | 10.5 tok/s | 26744 ms | 15K |
| Reasoning | C | Runs with offload (needs ~0 GB host RAM) | 12.6 tok/s | 18104 ms | 15K |
| RAG | D | Very compromised (needs ~1 GB host RAM) | 10.5 tok/s | 33431 ms |
How DeepSeek R1 Distill Qwen 14B (14B params) fits at each quantization level on MacBook Pro M3 Pro 18GB (13.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | C52 |
Q3_K_S | 3 | 6.9 GB | Low | C52 |
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
Adds memory headroom for longer context windows and future model growth.
~$799 MSRP
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
RTX 5080 Laptop 16GBBiggest leapRaises estimated decode speed by about 499%.
Adds memory headroom for longer context windows and future model growth.
Yes, MacBook Pro M3 Pro 18GB can run DeepSeek R1 Distill Qwen 14B with a C grade (Runs with offload). Expected decode speed: 12.6 tok/s.
DeepSeek R1 Distill Qwen 14B (14B parameters) requires approximately 13.0 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 MacBook Pro M3 Pro 18GB, DeepSeek R1 Distill Qwen 14B achieves approximately 12.6 tokens per second decode speed with a time-to-first-token of 15319ms using Q4_K_M quantization.
For coding workloads, DeepSeek R1 Distill Qwen 14B on MacBook Pro M3 Pro 18GB receives a C grade with 12.6 tok/s and 15K context.
On MacBook Pro M3 Pro 18GB, DeepSeek R1 Distill Qwen 14B can safely use up to 15K 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-m3-pro-18gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 15K |
7.8 GB |
| Medium |
| C52 |
Q4_K_MBest for your GPU | 4 | 8.5 GB | Medium | C52 |
Q5_K_M | 5 | 10.1 GB | High | F0 |
Q6_K | 6 | 11.5 GB | High | F0 |
Q8_0 | 8 | 15.0 GB | Very High | F0 |
F16 | 16 | 28.7 GB | Maximum | F0 |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Not always. MacBook Pro M3 Pro 18GB 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.