LLaVA 1.6 13B needs ~23.6 GB but MacBook Pro M4 Pro 24GB only has 17.3 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
6.3 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
15.2 tok/s
TTFT
12758 ms
Safe context
4K
Memory
23.6 GB / 17.3 GB
Offload
30%
Usable shared or unified memory is the main blocker for this model.
Not enough usable memory
The model needs 23.6 GB, but this setup only exposes 17.3 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 | A | Runs with offload (needs ~0.1 GB host RAM) | 22.6 tok/s | 4681 ms | 4K |
| Coding | F | Too heavy | 15.2 tok/s | 12758 ms | 4K |
| Agentic Coding | F | Too heavy | 10.5 tok/s | 26826 ms | 4K |
| Reasoning | F | Too heavy | 15.2 tok/s | 15077 ms | 4K |
| RAG | F | Too heavy | 10.5 tok/s | 33533 ms | 4K |
How LLaVA 1.6 13B (13B params) fits at each quantization level on MacBook Pro M4 Pro 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.1 GB | Low | A71 |
Q3_K_S | 3 | 6.4 GB | Low | A73 |
NVFP4 | 4 |
Upgrade options
Makes the model fit on the accelerator instead of staying completely out of reach.
Adds memory headroom for longer context windows and future model growth.
~$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.
Adds memory headroom for longer context windows and future model growth.
~$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 Pro M4 Pro 24GB provides.
LLaVA 1.6 13B (13B parameters) requires approximately 23.6 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 Pro M4 Pro 24GB, LLaVA 1.6 13B achieves approximately 15.2 tokens per second decode speed with a time-to-first-token of 12758ms using Q4_K_M quantization.
For coding workloads, LLaVA 1.6 13B on MacBook Pro M4 Pro 24GB receives a F grade with 15.2 tok/s and 4K context.
On MacBook Pro M4 Pro 24GB, 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-m4-pro-24gb" 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 |
| A73 |
Q4_K_M | 4 | 7.9 GB | Medium | A74 |
Q5_K_M | 5 | 9.4 GB | High | A74 |
Q6_KBest for your GPU | 6 | 10.7 GB | High | A74 |
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 Pro M4 Pro 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.
