Hermes 4.3 36B needs ~29.0 GB but RTX 4070 Ti Super 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
13.0 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
5.3 tok/s
TTFT
36815 ms
Safe context
4K
Memory
29.0 GB / 16.0 GB
Offload
40%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 29.0 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 | 6.2 tok/s | 17127 ms | 4K |
| Coding | F | Too heavy | 5.3 tok/s | 36815 ms | 4K |
| Agentic Coding | F | Too heavy | 4.0 tok/s | 71285 ms | 4K |
| Reasoning | F | Too heavy | 5.3 tok/s | 43508 ms | 4K |
| RAG | F | Too heavy | 4.0 tok/s | 89106 ms | 4K |
How Hermes 4.3 36B (36B params) fits at each quantization level on RTX 4070 Ti Super 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
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
No, Hermes 4.3 36B requires more memory than RTX 4070 Ti Super 16GB provides.
Hermes 4.3 36B (36B parameters) requires approximately 29.0 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 RTX 4070 Ti Super 16GB, Hermes 4.3 36B achieves approximately 5.3 tokens per second decode speed with a time-to-first-token of 36815ms using Q4_K_M quantization.
For coding workloads, Hermes 4.3 36B on RTX 4070 Ti Super 16GB receives a F grade with 5.3 tok/s and 4K context.
On RTX 4070 Ti Super 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-rtx-4070-ti-super-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.
