Nous Hermes 1.0 needs ~18.5 GB VRAM. RTX 4060 Ti 16GB has 16.0 GB. With Q2_K quantization, expect ~28 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
4.5 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
17.1 tok/s
TTFT
11354 ms
Safe context
10K
Memory
20.5 GB / 16.0 GB
Offload
20%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
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.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 0.5 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Tight fit | 38.3 tok/s | 2758 ms | 10K |
| Coding | F | Too heavy | 17.1 tok/s | 11354 ms | 10K |
| Agentic Coding | F | Too heavy | 6.4 tok/s | 44156 ms | 10K |
| Reasoning | F | Too heavy | 17.1 tok/s | 13418 ms | 10K |
| RAG | F | Too heavy | 6.4 tok/s | 55195 ms | 10K |
How Nous Hermes 1.0 (9B params) fits at each quantization level on RTX 4060 Ti 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | B68 |
Q3_K_S | 3 | 4.4 GB | Low | B69 |
NVFP4 | 4 |
Copy-paste commands to run Nous Hermes 1.0 on your machine.
Run
lms load Nous-Hermes-1.0 && lms server startUpgrade options
RTX 4000 Ada 20GBBudget 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.
~$1,250 MSRP
RTX 3090 24GBBest 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.
~$1,499 MSRP
RTX 4090 24GBNVIDIA 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.
~$1,599 MSRP
Yes, RTX 4060 Ti 16GB can run Nous Hermes 1.0 at Q2_K quantization (Very compromised (needs ~0.5 GB host RAM)). The recommended Q4_K_M requires 20.5 GB which exceeds available memory, but at Q2_K it needs only 18.5 GB. Expected decode speed: 28.1 tok/s.
Nous Hermes 1.0 (9B parameters) requires approximately 20.5 GB at Q4_K_M quantization. On RTX 4060 Ti 16GB, it fits at Q2_K using 18.5 GB.
The recommended quantization is Q4_K_M, but on RTX 4060 Ti 16GB the best fitting quantization is Q2_K, which uses 18.5 GB.
On RTX 4060 Ti 16GB, Nous Hermes 1.0 achieves approximately 28.1 tokens per second decode speed with a time-to-first-token of 6896ms using Q2_K quantization.
For coding workloads, Nous Hermes 1.0 on RTX 4060 Ti 16GB receives a F grade with 17.1 tok/s and 10K context.
On RTX 4060 Ti 16GB, Nous Hermes 1.0 can safely use up to 13K tokens of context at Q2_K quantization. The model's official context limit is 16K, 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/nous-hermes-1.0-on-rtx-4060-ti-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
5.0 GB |
| Medium |
| B69 |
Q4_K_M | 4 | 5.5 GB | Medium | B70 |
Q5_K_M | 5 | 6.5 GB | High | A71 |
Q6_K | 6 | 7.4 GB | High | A72 |
Q8_0Best for your GPU | 8 | 9.6 GB | Very High | A72 |
F16 | 16 | 18.5 GB | Maximum | F0 |
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.