RX 7900 XTX 24GBBudget pick
960 GB/s (+560)
C
Raises estimated decode speed by about 197%.80.9 tok/s decode
Raises estimated decode speed by about 197%.
~$999 MSRP
DeepSeek R1 Distill Qwen 14B needs ~14.5 GB VRAM. MacBook Pro M2 Max 32GB has 23.0 GB. With Q4_K_M quantization, expect ~27 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
27.2 tok/s
TTFT
7126 ms
Safe context
99K
Memory
14.5 GB / 23.0 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 | 27.2 tok/s | 3887 ms | 99K |
| Coding | C | Runs well | 27.2 tok/s | 7126 ms | 99K |
| Agentic Coding | C | Runs well | 27.2 tok/s | 10366 ms | 99K |
| Reasoning | C | Runs well | 27.2 tok/s | 8422 ms | 99K |
| RAG | C | Runs well | 27.2 tok/s | 12957 ms | 99K |
How DeepSeek R1 Distill Qwen 14B (14B params) fits at each quantization level on MacBook Pro M2 Max 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | C46 |
Q3_K_S | 3 | 6.9 GB | Low | C47 |
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 197%.
~$999 MSRP
RTX 3090 24GBBest valueRaises estimated decode speed by about 176%.
~$1,499 MSRP
Yes, MacBook Pro M2 Max 32GB can run DeepSeek R1 Distill Qwen 14B with a C grade (Runs well). Expected decode speed: 27.2 tok/s.
DeepSeek R1 Distill Qwen 14B (14B parameters) requires approximately 14.5 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 M2 Max 32GB, DeepSeek R1 Distill Qwen 14B achieves approximately 27.2 tokens per second decode speed with a time-to-first-token of 7126ms using Q4_K_M quantization.
For coding workloads, DeepSeek R1 Distill Qwen 14B on MacBook Pro M2 Max 32GB receives a C grade with 27.2 tok/s and 99K context.
On MacBook Pro M2 Max 32GB, DeepSeek R1 Distill Qwen 14B can safely use up to 99K 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-m2-max-32gb" 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 |
| C48 |
Q4_K_M | 4 | 8.5 GB | Medium | C48 |
Q5_K_M | 5 | 10.1 GB | High | C49 |
Q6_K | 6 | 11.5 GB | High | C50 |
Q8_0Best for your GPU | 8 | 15.0 GB | Very High | C50 |
F16 | 16 | 28.7 GB | Maximum | F0 |
Not always. MacBook Pro M2 Max 32GB 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.