LLaVA 1.5 7B needs ~14.1 GB but RTX 2060 Super 8GB only has 8.0 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
6.1 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
12.8 tok/s
TTFT
15100 ms
Safe context
4K
Memory
14.1 GB / 8.0 GB
Offload
40%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 14.1 GB, but this setup only exposes 8.0 GB of usable VRAM.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
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 | F | Too heavy | 26.2 tok/s | 4035 ms | 4K |
| Coding | F | Too heavy | 12.8 tok/s | 15100 ms | 4K |
| Agentic Coding | F | Too heavy | 9.1 tok/s | 30851 ms | 4K |
| Reasoning | F | Too heavy | 12.8 tok/s | 17845 ms | 4K |
| RAG | F | Too heavy | 9.1 tok/s | 38563 ms | 4K |
How LLaVA 1.5 7B (7B params) fits at each quantization level on RTX 2060 Super 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | A71 |
Q3_K_S | 3 | 3.4 GB | Low | A72 |
NVFP4 | 4 |
Upgrade options
RTX 5060 Ti 16GBBudget 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.
~$449 MSRP
RTX 4060 Ti 16GBBest valueMakes 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.
~$499 MSRP
RTX 2000 Ada 16GBNVIDIA 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.
~$625 MSRP
No, LLaVA 1.5 7B requires more memory than RTX 2060 Super 8GB provides.
LLaVA 1.5 7B (7B parameters) requires approximately 14.1 GB of memory with Q4_K_M quantization.
The recommended quantization for LLaVA 1.5 7B is Q4_K_M, which balances quality and memory efficiency.
On RTX 2060 Super 8GB, LLaVA 1.5 7B achieves approximately 12.8 tokens per second decode speed with a time-to-first-token of 15100ms using Q4_K_M quantization.
For coding workloads, LLaVA 1.5 7B on RTX 2060 Super 8GB receives a F grade with 12.8 tok/s and 4K context.
On RTX 2060 Super 8GB, LLaVA 1.5 7B 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-1.5-7b-on-rtx-2060-super-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| Medium |
| A71 |
Q4_K_M | 4 | 4.3 GB | Medium | A71 |
Q5_K_MBest for your GPU | 5 | 5.0 GB | High | A71 |
Q6_K | 6 | 5.7 GB | High | F0 |
Q8_0 | 8 | 7.5 GB | Very High | F0 |
F16 | 16 | 14.3 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.
