Hermes 4.3 36B needs ~39.9 GB VRAM. Gaudi 3 128GB has 128.0 GB. With Q4_K_M quantization, expect ~118 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
Fit status
Runs well
Decode
117.9 tok/s
TTFT
1641 ms
Safe context
350K
Memory
39.9 GB / 128.0 GB
The raw memory story may look fine, but the software ecosystem is still a constraint here.
Runtime ecosystem is narrower than CUDA
Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.
Prefer CUDA if you want the path of least resistance
If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 117.9 tok/s | 895 ms | 350K |
| Coding | C | Runs well | 117.9 tok/s | 1641 ms | 350K |
| Agentic Coding | C | Runs well | 117.9 tok/s | 2388 ms | 350K |
| Reasoning | C | Runs well | 117.9 tok/s | 1940 ms | 350K |
| RAG | C | Runs well | 117.9 tok/s | 2985 ms | 350K |
How Hermes 4.3 36B (36B params) fits at each quantization level on Gaudi 3 128GB (128.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 14.0 GB | Low | D38 |
Q3_K_S | 3 | 17.6 GB | Low | D39 |
NVFP4 | 4 |
Copy-paste commands to run Hermes 4.3 36B on your machine.
Run
lms load hf-nousresearch--hermes-4-3-36b-gguf && lms server startYes, Gaudi 3 128GB can run Hermes 4.3 36B with a C grade (Runs well). Expected decode speed: 117.9 tok/s.
Hermes 4.3 36B (36B parameters) requires approximately 39.9 GB of memory with Q4_K_M quantization.
The recommended quantization for Hermes 4.3 36B is Q4_K_M, which balances quality and memory efficiency.
On Gaudi 3 128GB, Hermes 4.3 36B achieves approximately 117.9 tokens per second decode speed with a time-to-first-token of 1641ms using Q4_K_M quantization.
For coding workloads, Hermes 4.3 36B on Gaudi 3 128GB receives a C grade with 117.9 tok/s and 350K context.
On Gaudi 3 128GB, Hermes 4.3 36B can safely use up to 350K tokens of context. 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-gaudi-3-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| Medium |
| D39 |
Q4_K_M | 4 | 22.0 GB | Medium | D39 |
Q5_K_M | 5 | 25.9 GB | High | D40 |
Q6_K | 6 | 29.5 GB | High | C40 |
Q8_0 | 8 | 38.5 GB | Very High | C42 |
F16Best for your GPU | 16 | 73.8 GB | Maximum | C48 |
Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.