LLaVA 1.6 13B needs ~22.9 GB but RTX 5000 Ada Laptop 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
6.9 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
18.6 tok/s
TTFT
10390 ms
Safe context
4K
Memory
22.9 GB / 16.0 GB
Offload
30%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 22.9 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 ~0.4 GB host RAM) | 35.7 tok/s | 2955 ms | 4K |
| Coding | F | Too heavy | 18.6 tok/s | 10390 ms | 4K |
| Agentic Coding | F | Too heavy | 8.0 tok/s | 35405 ms | 4K |
| Reasoning | F | Too heavy | 18.6 tok/s | 12280 ms | 4K |
| RAG | F | Too heavy | 8.0 tok/s | 44257 ms | 4K |
How LLaVA 1.6 13B (13B params) fits at each quantization level on RTX 5000 Ada Laptop 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 | 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 |
Opciones de mejora
RTX 4000 Ada 20GBMejor relación calidad-precioHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$1,250 MSRP
RTX 3090 24GBOpción económicaHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
~$1,499 MSRP
RTX 4090 24GBMejora NVIDIAHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
~$1,599 MSRP
No, LLaVA 1.6 13B requires more memory than RTX 5000 Ada Laptop 16GB provides.
LLaVA 1.6 13B (13B parameters) requires approximately 22.9 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 RTX 5000 Ada Laptop 16GB, LLaVA 1.6 13B achieves approximately 18.6 tokens per second decode speed with a time-to-first-token of 10390ms using Q4_K_M quantization.
For coding workloads, LLaVA 1.6 13B on RTX 5000 Ada Laptop 16GB receives a F grade with 18.6 tok/s and 4K context.
On RTX 5000 Ada Laptop 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.
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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/llava-1.6-13b-on-rtx-5000-ada-laptop-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
