Nous Hermes 1.0 needs ~19.7 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
11.7 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
7.1 tok/s
TTFT
27270 ms
Safe context
4K
Memory
19.7 GB / 8.0 GB
Offload
60%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 19.7 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 | 10.8 tok/s | 9799 ms | 4K |
| Coding | F | Too heavy | 7.1 tok/s | 27270 ms | 4K |
| Agentic Coding | F | Too heavy | 7.1 tok/s | 39665 ms | 4K |
| Reasoning | F | Too heavy | 7.1 tok/s | 32228 ms | 4K |
| RAG | F | Too heavy | 7.1 tok/s | 49581 ms | 4K |
How Nous Hermes 1.0 (9B params) fits at each quantization level on RTX 2060 Super 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | A74 |
Q3_K_S | 3 | 4.4 GB | Low | A74 |
NVFP4Best for your GPU | 4 | 5.0 GB | Medium | A73 |
Q4_K_M | 4 | 5.5 GB | Medium | F0 |
Q5_K_M | 5 | 6.5 GB | High | F0 |
Q6_K | 6 | 7.4 GB | High | F0 |
Q8_0 | 8 | 9.6 GB | Very High | F0 |
F16 | 16 | 18.5 GB | Maximum | F0 |
升级选项
RTX 4000 Ada 20GB经济之选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.
~$1,250 MSRP
RTX 3090 24GB最佳性价比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.
~$1,499 MSRP
RTX 4090 24GBNVIDIA 升级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.
~$1,599 MSRP
No, Nous Hermes 1.0 requires more memory than RTX 2060 Super 8GB provides.
Nous Hermes 1.0 (9B parameters) requires approximately 19.7 GB of memory with Q4_K_M quantization.
The recommended quantization for Nous Hermes 1.0 is Q4_K_M, which balances quality and memory efficiency.
On RTX 2060 Super 8GB, Nous Hermes 1.0 achieves approximately 7.1 tokens per second decode speed with a time-to-first-token of 27270ms using Q4_K_M quantization.
For coding workloads, Nous Hermes 1.0 on RTX 2060 Super 8GB receives a F grade with 7.1 tok/s and 4K context.
On RTX 2060 Super 8GB, Nous Hermes 1.0 can safely use up to 4K tokens of context. The model's official context limit is 16K, 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/nous-hermes-1.0-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:
