Hermes 4.3 36B needs ~28.0 GB VRAM. Tesla P40 24GB has 24.0 GB. With NVFP4 quantization, expect ~6 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
4.2 tok/s
TTFT
45856 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.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
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) | 5.0 tok/s | 21285 ms | 4K |
| Coding | F | Too heavy | 4.2 tok/s | 45856 ms | 4K |
| Agentic Coding | F | Too heavy | 3.2 tok/s | 89223 ms | 4K |
| Reasoning | F | Too heavy | 4.2 tok/s | 54193 ms | 4K |
| RAG | F | Too heavy | 3.2 tok/s | 111529 ms | 4K |
Inference speed
Estimated decode speed (tokens/sec) for Hermes 4.3 36B at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~55 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 54.7 | Offloads |
2× RX 7900 XTX 24GB | 48 GB | Q4_K_M | 53.5 | Fits |
| 2× RTX 4090 24GB | 48 GB | Q4_K_M | 28.1 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 27.3 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 27.3 | Fits |
| 2× RTX 3090 24GB | 48 GB | Q4_K_M | 25.8 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 25.4 | Fits |
| 4× RTX 3060 12GB | 48 GB | Q4_K_M | 22.7 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 21.1 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 20.0 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 17.2 | Tight |
| RTX 4090 24GB | 24 GB | Q4_K_M | 16.6 | Too big |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 15.3 | Too big |
| RTX 3090 24GB | 24 GB | Q4_K_M | 14.2 | Too big |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 10.9 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 10.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 6.0 | Too big |
| RTX 4070 12GB | 12 GB | Q4_K_M | 2.6 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 4060 8GB | 8 GB | Q4_K_M | 2.0 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How Hermes 4.3 36B (36B params) fits at each quantization level on Tesla P40 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 |
NVFP4 | 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 |
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, Tesla P40 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: 5.5 tok/s.
Hermes 4.3 36B (36B parameters) requires approximately 29.8 GB at Q4_K_M quantization. On Tesla P40 24GB, it fits at NVFP4 using 28.0 GB.
The recommended quantization is Q4_K_M, but on Tesla P40 24GB the best fitting quantization is NVFP4, which uses 28.0 GB.
On Tesla P40 24GB, Hermes 4.3 36B achieves approximately 5.5 tokens per second decode speed with a time-to-first-token of 34962ms using NVFP4 quantization.
For coding workloads, Hermes 4.3 36B on Tesla P40 24GB receives a F grade with 4.2 tok/s and 4K context.
On Tesla P40 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.
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.
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