DeepSeek R1 Distill 14B needs ~15.0 GB VRAM. MacBook Pro M3 24GB has 17.3 GB. With Q4_K_M quantization, expect ~8 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
Tight fit
Decode
8.6 tok/s
TTFT
22513 ms
Safe context
29K
Memory
15.0 GB / 17.3 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 | A | Runs well | 8.6 tok/s | 12280 ms | 29K |
| Coding | A | Tight fit | 8.0 tok/s | 24314 ms | 29K |
| Agentic Coding | A | Runs with offload (needs ~0.3 GB host RAM) | 8.0 tok/s | 35102 ms | 29K |
| Reasoning | A | Tight fit | 8.6 tok/s | 26606 ms | 29K |
| RAG | A | Runs with offload (needs ~0.3 GB host RAM) | 8.0 tok/s | 43878 ms |
How DeepSeek R1 Distill 14B (14B params) fits at each quantization level on MacBook Pro M3 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | A73 |
Q3_K_S | 3 | 6.9 GB | Low | A74 |
NVFP4 | 4 |
Copy-paste commands to run DeepSeek R1 Distill 14B on your machine.
Run
ollama run deepseek-r1Your hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Magistral Small 2507 | 24B | B | 3.8 tok/s | |
| Devstral Small 2 24B Instruct | 24B | B | 3.8 tok/s |
Yes, MacBook Pro M3 24GB can run DeepSeek R1 Distill 14B with a A grade (Tight fit). Expected decode speed: 8.0 tok/s.
DeepSeek R1 Distill 14B (14B parameters) requires approximately 15.0 GB of memory with Q4_K_M quantization.
The recommended quantization for DeepSeek R1 Distill 14B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M3 24GB, DeepSeek R1 Distill 14B achieves approximately 8.0 tokens per second decode speed with a time-to-first-token of 24314ms using Q4_K_M quantization.
For coding workloads, DeepSeek R1 Distill 14B on MacBook Pro M3 24GB receives a A grade with 8.0 tok/s and 29K context.
On MacBook Pro M3 24GB, DeepSeek R1 Distill 14B can safely use up to 29K 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-14b-on-m3-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 29K |
7.8 GB |
| Medium |
| A75 |
Q4_K_M | 4 | 8.5 GB | Medium | A76 |
Q5_K_M | 5 | 10.1 GB | High | A75 |
Q6_KBest for your GPU | 6 | 11.5 GB | High | A75 |
Q8_0 | 8 | 15.0 GB | Very High | F0 |
F16 | 16 | 28.7 GB | Maximum | F0 |
 Phi-4-reasoning-plus 14B | 14.7B | S | 8.2 tok/s |
| Devstral Small 1.1 | 24B | B | 3.8 tok/s |
 GPT-OSS 20B | 21B | A | 11.4 tok/s |
Not always. MacBook Pro M3 24GB 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.