SOLAR 10.7B v1.0 needs ~9.6 GB but RTX 2060 6GB only has 6.0 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
3.6 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
7.7 tok/s
TTFT
25280 ms
Safe context
4K
Memory
9.6 GB / 6.0 GB
Offload
40%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 9.6 GB, but this setup only exposes 6.0 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 | 8.9 tok/s | 11885 ms | 4K |
| Coding | F | Too heavy | 7.7 tok/s | 25280 ms | 4K |
| Agentic Coding | F | Too heavy | 5.8 tok/s | 48173 ms | 4K |
| Reasoning | F | Too heavy | 7.7 tok/s | 29877 ms | 4K |
| RAG | F | Too heavy | 5.8 tok/s | 60217 ms | 4K |
How SOLAR 10.7B v1.0 (10.699999809265137B params) fits at each quantization level on RTX 2060 6GB (6.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.2 GB | Low | F0 |
Q3_K_S | 3 | 5.2 GB | Low | F0 |
NVFP4 | 4 |
Upgrade options
RTX 3060 12GBBudget 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.
~$329 MSRP
RTX 5060 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.
~$449 MSRP
RTX 4060 Ti 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.
~$499 MSRP
No, SOLAR 10.7B v1.0 requires more memory than RTX 2060 6GB provides.
SOLAR 10.7B v1.0 (10.699999809265137B parameters) requires approximately 9.6 GB of memory with Q4_K_M quantization.
The recommended quantization for SOLAR 10.7B v1.0 is Q4_K_M, which balances quality and memory efficiency.
On RTX 2060 6GB, SOLAR 10.7B v1.0 achieves approximately 7.7 tokens per second decode speed with a time-to-first-token of 25280ms using Q4_K_M quantization.
For coding workloads, SOLAR 10.7B v1.0 on RTX 2060 6GB receives a F grade with 7.7 tok/s and 4K context.
On RTX 2060 6GB, SOLAR 10.7B v1.0 can safely use up to 4K tokens of context. The model's official context limit is —, 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/hf-mradermacher--solar-10-7b-v1-0-gguf-on-rtx-2060-6gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| Medium |
| F0 |
Q4_K_M | 4 | 6.5 GB | Medium | F0 |
Q5_K_M | 5 | 7.7 GB | High | F0 |
Q6_K | 6 | 8.8 GB | High | F0 |
Q8_0 | 8 | 11.4 GB | Very High | F0 |
F16 | 16 | 21.9 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.
