DeepSeek R1 Distill Qwen 14B needs ~24.9 GB VRAM. MacBook Pro M3 Max 128GB has 92.2 GB. With Q4_K_M quantization, expect ~28 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
28.1 tok/s
TTFT
6889 ms
Safe context
672K
Memory
24.9 GB / 92.2 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 | 28.1 tok/s | 3758 ms | 672K |
| Coding | C | Runs well | 28.1 tok/s | 6889 ms | 672K |
| Agentic Coding | C | Runs well | 28.1 tok/s | 10020 ms | 672K |
| Reasoning | C | Runs well | 28.1 tok/s | 8141 ms | 672K |
| RAG | C | Runs well | 28.1 tok/s | 12525 ms | 672K |
How DeepSeek R1 Distill Qwen 14B (14B params) fits at each quantization level on MacBook Pro M3 Max 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | D39 |
Q3_K_S | 3 | 6.9 GB | Low | D39 |
NVFP4 | 4 | 7.8 GB | Medium | D40 |
Q4_K_M | 4 | 8.5 GB | Medium | D40 |
Q5_K_M | 5 | 10.1 GB | High | D40 |
Q6_K | 6 | 11.5 GB | High | D40 |
Q8_0 | 8 | 15.0 GB | Very High | C40 |
F16Best for your GPU | 16 | 28.7 GB | Maximum | C42 |
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-Optionen
Raises estimated decode speed by about 132%.
Adds memory headroom for longer context windows and future model growth.
ca. $6,999 MSRP
Raises estimated decode speed by about 598%.
Adds memory headroom for longer context windows and future model growth.
ca. $8,000 MSRP
Yes, MacBook Pro M3 Max 128GB can run DeepSeek R1 Distill Qwen 14B with a C grade (Runs well). Expected decode speed: 28.1 tok/s.
DeepSeek R1 Distill Qwen 14B (14B parameters) requires approximately 24.9 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 Max 128GB, DeepSeek R1 Distill Qwen 14B achieves approximately 28.1 tokens per second decode speed with a time-to-first-token of 6889ms using Q4_K_M quantization.
For coding workloads, DeepSeek R1 Distill Qwen 14B on MacBook Pro M3 Max 128GB receives a C grade with 28.1 tok/s and 672K context.
On MacBook Pro M3 Max 128GB, DeepSeek R1 Distill Qwen 14B can safely use up to 672K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Not always. MacBook Pro M3 Max 128GB 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.
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-max-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: