LLaVA 1.6 13B needs ~22.6 GB but Radeon PRO W7700 16GB only has 16.0 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
6.6 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
15.5 tok/s
TTFT
12504 ms
Safe context
4K
Memory
22.6 GB / 16.0 GB
Offload
30%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 22.6 GB, but this setup only exposes 16.0 GB of usable VRAM.
Add more VRAM headroom
The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs with offload (needs ~0.3 GB host RAM) | 30.0 tok/s | 3520 ms | 4K |
| Coding | F | Too heavy | 15.5 tok/s | 12504 ms | 4K |
| Agentic Coding | F | Too heavy | 6.4 tok/s | 43807 ms | 4K |
| Reasoning | F | Too heavy | 15.5 tok/s | 14778 ms | 4K |
| RAG | F | Too heavy | 6.4 tok/s | 54758 ms | 4K |
How LLaVA 1.6 13B (13B params) fits at each quantization level on Radeon PRO W7700 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.1 GB | Low | A72 |
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.
Raises estimated decode speed by about 117%.
~$899 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.
~$999 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.
~$1,899 MSRP
No, LLaVA 1.6 13B requires more memory than Radeon PRO W7700 16GB provides.
LLaVA 1.6 13B (13B parameters) requires approximately 22.6 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 Radeon PRO W7700 16GB, LLaVA 1.6 13B achieves approximately 15.5 tokens per second decode speed with a time-to-first-token of 12504ms using Q4_K_M quantization.
For coding workloads, LLaVA 1.6 13B on Radeon PRO W7700 16GB receives a F grade with 15.5 tok/s and 4K context.
On Radeon PRO W7700 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-radeon-pro-w7700-16gb" 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 |
| A74 |
Q4_K_M | 4 | 7.9 GB | Medium | A75 |
Q5_K_M | 5 | 9.4 GB | High | A75 |
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 |
Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
