LLaVA 1.6 13B needs ~22.9 GB but RTX 5080 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
28.6 tok/s
TTFT
6772 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 | 54.8 tok/s | 1926 ms | 4K |
| Coding | F | Too heavy | 28.6 tok/s | 6772 ms | 4K |
| Agentic Coding | F | Too heavy | 12.2 tok/s | 23077 ms | 4K |
| Reasoning | F | Too heavy | 28.6 tok/s | 8004 ms | 4K |
| RAG | F | Too heavy | 12.2 tok/s | 28846 ms | 4K |
How LLaVA 1.6 13B (13B params) fits at each quantization level on RTX 5080 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 |
Upgrade options
RTX 4000 Ada 20GBBest valueMakes the model fit on the accelerator instead of staying completely out of reach.
Adds memory headroom for longer context windows and future model growth.
~$1,250 MSRP
RTX 3090 24GBBudget pickMakes 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.
~$1,499 MSRP
RTX 4090 24GBNVIDIA upgradeMakes 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.
~$1,599 MSRP
No, LLaVA 1.6 13B requires more memory than RTX 5080 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 5080 Laptop 16GB, LLaVA 1.6 13B achieves approximately 28.6 tokens per second decode speed with a time-to-first-token of 6772ms using Q4_K_M quantization.
For coding workloads, LLaVA 1.6 13B on RTX 5080 Laptop 16GB receives a F grade with 28.6 tok/s and 4K context.
On RTX 5080 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/llava-1.6-13b-on-rtx-5080-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:
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.
