Nous Hermes 1.0 needs ~20.6 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q4_K_M quantization, expect ~36 tok/s.
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
0.6 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.2 GB host RAM)
Decode
36.1 tok/s
TTFT
5369 ms
Safe context
15K
Memory
20.6 GB / 20.0 GB
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 51.1 tok/s | 2065 ms | 15K |
| Coding | A | Runs with offload (needs ~0.2 GB host RAM) | 36.1 tok/s | 5369 ms | 15K |
| Agentic Coding | F | Too heavy | 13.5 tok/s | 20802 ms | 15K |
| Reasoning | A | Runs with offload (needs ~0.2 GB host RAM) | 36.1 tok/s | 6345 ms | 15K |
| RAG | F | Too heavy | 13.5 tok/s | 26003 ms | 15K |
How Nous Hermes 1.0 (9B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | B66 |
Q3_K_S | 3 | 4.4 GB | Low | B67 |
NVFP4 | 4 | 5.0 GB | Medium | B67 |
Q4_K_M | 4 | 5.5 GB | Medium | B68 |
Q5_K_M | 5 | 6.5 GB | High | B69 |
Q6_K | 6 | 7.4 GB | High | B69 |
Q8_0Best for your GPU | 8 | 9.6 GB | Very High | A71 |
F16 | 16 | 18.5 GB | Maximum | F0 |
Copy-paste commands to run Nous Hermes 1.0 on your machine.
Run
lms load Nous-Hermes-1.0 && lms server startYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen3-Coder 30B A3B Instruct | 30.5B | A | 23.8 tok/s | |
| Qwen 3.5 27B | 27B | A | 10.7 tok/s | |
| Qwen 3.6 27B | 27B | S | 10.1 tok/s | |
| Qwen3-VL 30B A3B Instruct | 30B | A | 25.3 tok/s | |
 Magistral Small 2507 | 24B | S | 20.6 tok/s |
Yes, RTX 4000 Ada 20GB can run Nous Hermes 1.0 with a A grade (Runs with offload (needs ~0.2 GB host RAM)). Expected decode speed: 36.1 tok/s.
Nous Hermes 1.0 (9B parameters) requires approximately 20.6 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 4000 Ada 20GB, Nous Hermes 1.0 achieves approximately 36.1 tokens per second decode speed with a time-to-first-token of 5369ms using Q4_K_M quantization.
For coding workloads, Nous Hermes 1.0 on RTX 4000 Ada 20GB receives a A grade with 36.1 tok/s and 15K context.
On RTX 4000 Ada 20GB, Nous Hermes 1.0 can safely use up to 15K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/nous-hermes-1.0-on-rtx-4000-ada-20gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: