RTX 5070 12GBBudget pick12 GB VRAM (+2)
B
Raises estimated decode speed by about 27%.106.6 tok/s decode
Raises estimated decode speed by about 27%.
~$549 MSRP
OpenHermes 2.5 7B needs ~8.1 GB VRAM. RTX 3080 10GB has 10.0 GB. With Q4_K_M quantization, expect ~84 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 well
Decode
84.0 tok/s
TTFT
2305 ms
Safe context
8K
Memory
8.1 GB / 10.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 84.0 tok/s | 1257 ms | 8K |
| Coding | B | Runs well | 84.0 tok/s | 2305 ms | 8K |
| Agentic Coding | C | Runs with offload (needs ~0 GB host RAM) | 84.0 tok/s | 3352 ms | 8K |
| Reasoning | B | Runs well | 84.0 tok/s | 2724 ms | 8K |
| RAG | C | Runs with offload (needs ~0 GB host RAM) | 84.0 tok/s | 4190 ms | 8K |
How OpenHermes 2.5 7B (7B params) fits at each quantization level on RTX 3080 10GB (10.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C51 |
Q3_K_S | 3 | 3.4 GB | Low | C53 |
NVFP4 | 4 | 3.9 GB | Medium | C53 |
Q4_K_M | 4 | 4.3 GB | Medium | C54 |
Q5_K_M | 5 | 5.0 GB | High | C54 |
Q6_KBest for your GPU | 6 | 5.7 GB | High | C53 |
Q8_0 | 8 | 7.5 GB | Very High | F0 |
F16 | 16 | 14.3 GB | Maximum | F0 |
Copy-paste commands to run OpenHermes 2.5 7B on your machine.
Run
ollama run openhermesUpgrade options
RTX 5070 12GBBudget pickRaises estimated decode speed by about 27%.
~$549 MSRP
RTX 4070 Super 12GBBest value~$599 MSRP
RTX 2080 Ti 11GBNVIDIA upgrade~$999 MSRP
Yes, RTX 3080 10GB can run OpenHermes 2.5 7B with a B grade (Runs well). Expected decode speed: 84.0 tok/s.
OpenHermes 2.5 7B (7B parameters) requires approximately 8.1 GB of memory with Q4_K_M quantization.
The recommended quantization for OpenHermes 2.5 7B is Q4_K_M, which balances quality and memory efficiency.
On RTX 3080 10GB, OpenHermes 2.5 7B achieves approximately 84.0 tokens per second decode speed with a time-to-first-token of 2305ms using Q4_K_M quantization.
For coding workloads, OpenHermes 2.5 7B on RTX 3080 10GB receives a B grade with 84.0 tok/s and 8K context.
On RTX 3080 10GB, OpenHermes 2.5 7B can safely use up to 8K tokens of context. The model's official context limit is 8K, 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/openhermes-2.5-7b-on-rtx-3080-10gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: