Hermes 4.3 36B needs ~30.3 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q4_K_M quantization, expect ~17 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
Tight fit
Decode
17.2 tok/s
TTFT
11259 ms
Safe context
23K
Memory
30.3 GB / 32.0 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 | C | Tight fit | 17.2 tok/s | 6141 ms | 23K |
| Coding | C | Tight fit | 17.2 tok/s | 11259 ms | 23K |
| Agentic Coding | D | Runs with offload (needs ~1.6 GB host RAM) | 11.3 tok/s | 24964 ms | 23K |
| Reasoning | C | Tight fit | 17.2 tok/s | 13306 ms | 23K |
| RAG | D | Runs with offload (needs ~1.6 GB host RAM) | 11.3 tok/s | 31205 ms |
How Hermes 4.3 36B (36B params) fits at each quantization level on Radeon AI PRO R9700 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 14.0 GB | Low | C48 |
Q3_K_S | 3 | 17.6 GB | Low | C49 |
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 startUpgrade options
Raises estimated decode speed by about 35%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Raises estimated decode speed by about 35%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Raises estimated decode speed by about 195%.
Adds memory headroom for longer context windows and future model growth.
~$10,000 MSRP
Yes, Radeon AI PRO R9700 32GB can run Hermes 4.3 36B with a C grade (Tight fit). Expected decode speed: 17.2 tok/s.
Hermes 4.3 36B (36B parameters) requires approximately 30.3 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 Radeon AI PRO R9700 32GB, Hermes 4.3 36B achieves approximately 17.2 tokens per second decode speed with a time-to-first-token of 11259ms using Q4_K_M quantization.
For coding workloads, Hermes 4.3 36B on Radeon AI PRO R9700 32GB receives a C grade with 17.2 tok/s and 23K context.
On Radeon AI PRO R9700 32GB, Hermes 4.3 36B can safely use up to 23K 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-radeon-ai-pro-r9700-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 23K |
20.2 GB |
| Medium |
| C49 |
Q4_K_MBest for your GPU | 4 | 22.0 GB | Medium | C49 |
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 |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.