Hermes 4.3 36B needs ~28.0 GB VRAM. RTX 5090 Laptop 24GB has 24.0 GB. With NVFP4 quantization, expect ~21 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
5.8 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
16.3 tok/s
TTFT
11861 ms
Safe context
4K
Memory
29.8 GB / 24.0 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 2.9 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | D | Very compromised (needs ~2.9 GB host RAM) | 19.1 tok/s | 5543 ms | 4K |
| Coding | F | Too heavy | 16.3 tok/s | 11861 ms | 4K |
| Agentic Coding | F | Too heavy | 12.3 tok/s | 22802 ms | 4K |
| Reasoning | F | Too heavy | 16.3 tok/s | 14017 ms | 4K |
| RAG | F | Too heavy | 12.3 tok/s | 28502 ms | 4K |
How Hermes 4.3 36B (36B params) fits at each quantization level on RTX 5090 Laptop 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 14.0 GB | Low | C50 |
Q3_K_SBest for your GPU | 3 | 17.6 GB | Low | C49 |
Copy-paste commands to run Hermes 4.3 36B on your machine.
Run
lms load hf-nousresearch--hermes-4-3-36b-gguf && lms server startUpgrade options
RTX 5090 32GBBudget 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.
~$1,999 MSRP
RTX PRO 4500 Blackwell 32GBBest 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.
~$2,499 MSRP
RTX 5000 Ada 32GBNVIDIA 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.
~$4,000 MSRP
Yes, RTX 5090 Laptop 24GB can run Hermes 4.3 36B at NVFP4 quantization (Very compromised (needs ~2.9 GB host RAM)). The recommended Q4_K_M requires 29.8 GB which exceeds available memory, but at NVFP4 it needs only 28.0 GB. Expected decode speed: 21.3 tok/s.
Hermes 4.3 36B (36B parameters) requires approximately 29.8 GB at Q4_K_M quantization. On RTX 5090 Laptop 24GB, it fits at NVFP4 using 28.0 GB.
The recommended quantization is Q4_K_M, but on RTX 5090 Laptop 24GB the best fitting quantization is NVFP4, which uses 28.0 GB.
On RTX 5090 Laptop 24GB, Hermes 4.3 36B achieves approximately 21.3 tokens per second decode speed with a time-to-first-token of 9095ms using NVFP4 quantization.
For coding workloads, Hermes 4.3 36B on RTX 5090 Laptop 24GB receives a F grade with 16.3 tok/s and 4K context.
On RTX 5090 Laptop 24GB, Hermes 4.3 36B can safely use up to 4K tokens of context at NVFP4 quantization. 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-nousresearch--hermes-4-3-36b-gguf-on-rtx-5090-laptop-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 4 |
20.2 GB |
| Medium |
| F0 |
Q4_K_M | 4 | 22.0 GB | Medium | F0 |
Q5_K_M | 5 | 25.9 GB | High | F0 |
Q6_K | 6 | 29.5 GB | High | F0 |
Q8_0 | 8 | 38.5 GB | Very High | F0 |
F16 | 16 | 73.8 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.