RTX 3060 12GBBudget pick12 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.48.7 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
Hermes 2 Pro Llama 3 8B needs ~7.8 GB VRAM. RTX 2060 Super 8GB has 8.0 GB. With Q4_K_M quantization, expect ~53 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 with offload
Decode
53.2 tok/s
TTFT
3636 ms
Safe context
19K
Memory
7.8 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.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
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 | 53.2 tok/s | 1983 ms | 19K |
| Coding | C | Runs with offload | 53.2 tok/s | 3636 ms | 19K |
| Agentic Coding | C | Very compromised (needs ~0.4 GB host RAM) | 31.9 tok/s | 8839 ms | 19K |
| Reasoning | C | Runs with offload | 53.2 tok/s | 4297 ms | 19K |
| RAG | C | Very compromised (needs ~0.4 GB host RAM) | 31.9 tok/s | 11048 ms |
How Hermes 2 Pro Llama 3 8B (8B params) fits at each quantization level on RTX 2060 Super 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
RTX 3060 12GBBudget pickAdds memory headroom for longer context windows and future model growth.
~$329 MSRP
RTX 5070 12GBBest valueRaises estimated decode speed by about 63%.
Adds memory headroom for longer context windows and future model growth.
~$549 MSRP
RTX 4070 Super 12GBNVIDIA upgradeRaises estimated decode speed by about 49%.
Adds memory headroom for longer context windows and future model growth.
~$599 MSRP
Yes, RTX 2060 Super 8GB can run Hermes 2 Pro Llama 3 8B with a C grade (Runs with offload). Expected decode speed: 53.2 tok/s.
Hermes 2 Pro Llama 3 8B (8B parameters) requires approximately 7.8 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 RTX 2060 Super 8GB, Hermes 2 Pro Llama 3 8B achieves approximately 53.2 tokens per second decode speed with a time-to-first-token of 3636ms using Q4_K_M quantization.
For coding workloads, Hermes 2 Pro Llama 3 8B on RTX 2060 Super 8GB receives a C grade with 53.2 tok/s and 19K context.
On RTX 2060 Super 8GB, Hermes 2 Pro Llama 3 8B can safely use up to 19K 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-rtx-2060-super-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 19K |
4.5 GB |
| 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.