Hermes 4.3 36B needs ~28.7 GB but Radeon PRO W7700 16GB only has 16.0 GB. Try a smaller quantization or lighter model.
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
12.7 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
3.4 tok/s
TTFT
56976 ms
Safe context
4K
Memory
28.7 GB / 16.0 GB
Offload
40%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 28.7 GB, but this setup only exposes 16.0 GB of usable VRAM.
Add more VRAM headroom
The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 4.0 tok/s | 26461 ms | 4K |
| Coding | F | Too heavy | 3.4 tok/s | 56976 ms | 4K |
| Agentic Coding | F | Too heavy | 2.5 tok/s | 110633 ms | 4K |
| Reasoning | F | Too heavy | 3.4 tok/s | 67336 ms | 4K |
| RAG | F | Too heavy | 2.5 tok/s | 138291 ms | 4K |
How Hermes 4.3 36B (36B params) fits at each quantization level on Radeon PRO W7700 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 14.0 GB | Low | F0 |
Q3_K_S | 3 | 17.6 GB | Low | F0 |
NVFP4 | 4 |
Upgrade options
Makes 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.
~$1,899 MSRP
Makes 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.
~$2,249 MSRP
Makes 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
NVIDIA A100 40GBBiggest leapMakes 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.
~$10,000 MSRP
No, Hermes 4.3 36B requires more memory than Radeon PRO W7700 16GB provides.
Hermes 4.3 36B (36B parameters) requires approximately 28.7 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 PRO W7700 16GB, Hermes 4.3 36B achieves approximately 3.4 tokens per second decode speed with a time-to-first-token of 56976ms using Q4_K_M quantization.
For coding workloads, Hermes 4.3 36B on Radeon PRO W7700 16GB receives a F grade with 3.4 tok/s and 4K context.
On Radeon PRO W7700 16GB, Hermes 4.3 36B can safely use up to 4K 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-pro-w7700-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 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 |
Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
