Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
OpenHermes 2.5 7B needs ~7.9 GB VRAM. RTX 3070 Ti 8GB has 8.0 GB. With Q4_K_M quantization, expect ~98 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
84.0 tok/s
TTFT
2305 ms
Safe context
8K
Memory
7.9 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 | 84.0 tok/s | 1257 ms | 8K |
| Coding | C | Runs with offload | 98.0 tok/s | 1976 ms | 8K |
| Agentic Coding | F | Too heavy | 44.6 tok/s | 6320 ms | 8K |
| Reasoning | C | Runs with offload | 84.0 tok/s | 2724 ms | 8K |
| RAG | F | Too heavy | 44.6 tok/s | 7899 ms | 8K |
How OpenHermes 2.5 7B (7B params) fits at each quantization level on RTX 3070 Ti 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C54 |
Q3_K_S | 3 | 3.4 GB | Low | C54 |
NVFP4 | 4 |
Copy-paste commands to run OpenHermes 2.5 7B on your machine.
Run
ollama run openhermesUpgrade options
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
Raises estimated decode speed by about 27%.
Adds memory headroom for longer context windows and future model growth.
~$549 MSRP
Adds memory headroom for longer context windows and future model growth.
~$599 MSRP
Yes, RTX 3070 Ti 8GB can run OpenHermes 2.5 7B with a C grade (Runs with offload). Expected decode speed: 98.0 tok/s.
OpenHermes 2.5 7B (7B parameters) requires approximately 7.9 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 3070 Ti 8GB, OpenHermes 2.5 7B achieves approximately 98.0 tokens per second decode speed with a time-to-first-token of 1976ms using Q4_K_M quantization.
For coding workloads, OpenHermes 2.5 7B on RTX 3070 Ti 8GB receives a C grade with 98.0 tok/s and 8K context.
On RTX 3070 Ti 8GB, 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-3070-ti-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| Medium |
| C54 |
Q4_K_M | 4 | 4.3 GB | Medium | C54 |
Q5_K_MBest for your GPU | 5 | 5.0 GB | High | C54 |
Q6_K | 6 | 5.7 GB | High | F0 |
Q8_0 | 8 | 7.5 GB | Very High | F0 |
F16 | 16 | 14.3 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.