DeepSeek V4 Pro needs ~866.5 GB but MacBook Air M2 16GB only has 11.5 GB. Try a smaller quantization or lighter model.
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
855.0 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
2.0 tok/s
TTFT
96800 ms
Safe context
4K
Memory
866.5 GB / 11.5 GB
Offload
100%
Usable shared or unified memory is the main blocker for this model.
Not enough usable memory
The model needs 866.5 GB, but this setup only exposes 11.5 GB of usable shared or unified memory.
Move to a larger memory pool
A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 2.0 tok/s | 52800 ms | 4K |
| Coding | F | Too heavy | 2.0 tok/s | 96800 ms | 4K |
| Agentic Coding | F | Too heavy | 2.0 tok/s | 140800 ms | 4K |
| Reasoning | F | Too heavy | 2.0 tok/s | 114400 ms | 4K |
| RAG | F | Too heavy | 2.0 tok/s | 176000 ms | 4K |
How DeepSeek V4 Pro (1600B params) fits at each quantization level on MacBook Air M2 16GB (11.5 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 624.0 GB | Low | F0 |
Q3_K_S | 3 | 784.0 GB | Low | F0 |
NVFP4 | 4 |
No, DeepSeek V4 Pro requires more memory than MacBook Air M2 16GB provides.
DeepSeek V4 Pro (1600B parameters) requires approximately 866.5 GB of memory with NVFP4 quantization.
The recommended quantization for DeepSeek V4 Pro is NVFP4, which balances quality and memory efficiency.
On MacBook Air M2 16GB, DeepSeek V4 Pro achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using NVFP4 quantization.
For coding workloads, DeepSeek V4 Pro on MacBook Air M2 16GB receives a F grade with 2.0 tok/s and 4K context.
On MacBook Air M2 16GB, DeepSeek V4 Pro can safely use up to 4K tokens of context. The model's official context limit is 1.0M, 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-v4-pro-on-m2-air-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| Medium |
| F0 |
Q4_K_M | 4 | 976.0 GB | Medium | F0 |
Q5_K_M | 5 | 1152.0 GB | High | F0 |
Q6_K | 6 | 1312.0 GB | High | F0 |
Q8_0 | 8 | 1712.0 GB | Very High | F0 |
F16 | 16 | 3280.0 GB | Maximum | F0 |
Move to a larger memory pool. A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.
Not always. MacBook Air M2 16GB 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.
