Nous Hermes 1.0 needs ~20.1 GB but RTX 4070 Super 12GB only has 12.0 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
8.1 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
17.9 tok/s
TTFT
10812 ms
Safe context
5K
Memory
20.1 GB / 12.0 GB
Offload
40%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 20.1 GB, but this setup only exposes 12.0 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 | B | Very compromised (needs ~0.8 GB host RAM) | 38.4 tok/s | 2753 ms | 5K |
| Coding | F | Too heavy | 17.9 tok/s | 10812 ms | 5K |
| Agentic Coding | F | Too heavy | 10.6 tok/s | 26558 ms | 5K |
| Reasoning | F | Too heavy | 17.9 tok/s | 12778 ms | 5K |
| RAG | F | Too heavy | 10.6 tok/s | 33197 ms | 5K |
How Nous Hermes 1.0 (9B params) fits at each quantization level on RTX 4070 Super 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | A71 |
Q3_K_S | 3 | 4.4 GB | Low | A72 |
NVFP4 | 4 | 5.0 GB | Medium | A73 |
Q4_K_M | 4 | 5.5 GB | Medium | A73 |
Q5_K_M | 5 | 6.5 GB | High | A73 |
Q6_KBest for your GPU | 6 | 7.4 GB | High | A72 |
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 4070 Super 12GB provides.
Nous Hermes 1.0 (9B parameters) requires approximately 20.1 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 4070 Super 12GB, Nous Hermes 1.0 achieves approximately 17.9 tokens per second decode speed with a time-to-first-token of 10812ms using Q4_K_M quantization.
For coding workloads, Nous Hermes 1.0 on RTX 4070 Super 12GB receives a F grade with 17.9 tok/s and 5K context.
On RTX 4070 Super 12GB, Nous Hermes 1.0 can safely use up to 5K 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-4070-super-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
