RX 7600 XT 16GBBudget pick
16 GB VRAM (+8)288 GB/s (+64)
C
Raises estimated decode speed by about 52%.34.2 tok/s decode
Raises estimated decode speed by about 52%.
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
Hermes 2 Pro Llama 3 8B needs ~7.5 GB VRAM. RX 6600 8GB has 8.0 GB. With Q4_K_M quantization, expect ~23 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
22.5 tok/s
TTFT
8608 ms
Safe context
24K
Memory
7.5 GB / 8.0 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 | 22.5 tok/s | 4695 ms | 24K |
| Coding | C | Tight fit | 22.5 tok/s | 8608 ms | 24K |
| Agentic Coding | D | Runs with offload (needs ~0.3 GB host RAM) | 15.0 tok/s | 18755 ms | 24K |
| Reasoning | C | Tight fit | 22.5 tok/s | 10173 ms | 24K |
| RAG | D | Runs with offload (needs ~0.3 GB host RAM) | 15.0 tok/s | 23444 ms |
How Hermes 2 Pro Llama 3 8B (8B params) fits at each quantization level on RX 6600 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C54 |
Q3_K_S | 3 | 3.9 GB | Low | C53 |
NVFP4 | 4 |
Copy-paste commands to run Hermes 2 Pro Llama 3 8B on your machine.
Run
lms load hf-nousresearch--hermes-2-pro-llama-3-8b-gguf && lms server startUpgrade options
Raises estimated decode speed by about 52%.
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
Raises estimated decode speed by about 84%.
Adds memory headroom for longer context windows and future model growth.
~$349 MSRP
Raises estimated decode speed by about 136%.
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
Yes, RX 6600 8GB can run Hermes 2 Pro Llama 3 8B with a C grade (Tight fit). Expected decode speed: 22.5 tok/s.
Hermes 2 Pro Llama 3 8B (8B parameters) requires approximately 7.5 GB of memory with Q4_K_M quantization.
The recommended quantization for Hermes 2 Pro Llama 3 8B is Q4_K_M, which balances quality and memory efficiency.
On RX 6600 8GB, Hermes 2 Pro Llama 3 8B achieves approximately 22.5 tokens per second decode speed with a time-to-first-token of 8608ms using Q4_K_M quantization.
For coding workloads, Hermes 2 Pro Llama 3 8B on RX 6600 8GB receives a C grade with 22.5 tok/s and 24K context.
On RX 6600 8GB, Hermes 2 Pro Llama 3 8B can safely use up to 24K 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-2-pro-llama-3-8b-gguf-on-rx-6600-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 24K |
| Medium |
| C53 |
Q4_K_MBest for your GPU | 4 | 4.9 GB | Medium | C53 |
Q5_K_M | 5 | 5.8 GB | High | F0 |
Q6_K | 6 | 6.6 GB | High | F0 |
Q8_0 | 8 | 8.6 GB | Very High | F0 |
F16 | 16 | 16.4 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.