LLaVA 1.6 13B needs ~22.8 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
11.3 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
3.7 tok/s
TTFT
52487 ms
Safe context
4K
Memory
22.8 GB / 11.5 GB
Offload
50%
Usable shared or unified memory is the main blocker for this model.
Not enough usable memory
The model needs 22.8 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 | 5.0 tok/s | 21134 ms | 4K |
| Coding | F | Too heavy | 3.7 tok/s | 52487 ms | 4K |
| Agentic Coding | F | Too heavy | 3.7 tok/s | 76345 ms | 4K |
| Reasoning | F | Too heavy | 3.7 tok/s | 62030 ms | 4K |
| RAG | F | Too heavy | 3.7 tok/s | 95431 ms | 4K |
How LLaVA 1.6 13B (13B params) fits at each quantization level on MacBook Air M2 16GB (11.5 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.1 GB | Low | A76 |
Q3_K_S | 3 | 6.4 GB | Low | A76 |
NVFP4 | 4 |
Upgrade options
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 132%.
~$799 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$1,099 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 132%.
~$1,099 MSRP
RTX 5090 32GBBiggest leapMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$1,999 MSRP
No, LLaVA 1.6 13B requires more memory than MacBook Air M2 16GB provides.
LLaVA 1.6 13B (13B parameters) requires approximately 22.8 GB of memory with Q4_K_M quantization.
The recommended quantization for LLaVA 1.6 13B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Air M2 16GB, LLaVA 1.6 13B achieves approximately 3.7 tokens per second decode speed with a time-to-first-token of 52487ms using Q4_K_M quantization.
For coding workloads, LLaVA 1.6 13B on MacBook Air M2 16GB receives a F grade with 3.7 tok/s and 4K context.
On MacBook Air M2 16GB, LLaVA 1.6 13B can safely use up to 4K tokens of context. The model's official context limit is 4K, 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/llava-1.6-13b-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:
7.3 GB |
| Medium |
| A75 |
Q4_K_MBest for your GPU | 4 | 7.9 GB | Medium | A75 |
Q5_K_M | 5 | 9.4 GB | High | F0 |
Q6_K | 6 | 10.7 GB | High | F0 |
Q8_0 | 8 | 13.9 GB | Very High | F0 |
F16 | 16 | 26.7 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.
