RTX 5070 12GBOpción económica12 GB VRAM (+2)
B
Sube la velocidad estimada de decodificación alrededor de un 27%.106.6 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 27%.
~$549 MSRP
Can OpenHermes 2.5 7B run on RTX 3080 10GB?
YES — Runs Great
B57Good
Estimated from fit model
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.
Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: Balanced
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 8.1 GB, 84.0 tok/s, Runs well
8.1 GB required10.0 GB available
81% VRAM used
Fit status
Runs well
Decode
84.0 tok/s
TTFT
2305 ms
Safe context
8K
Memory
8.1 GB / 10.0 GB
Memory breakdown
Weights4.3 GB
KV Cache2.0 GB
Runtime0.9 GB
Headroom1.0 GB
See how fast it feels
See how fast it feelsOpenHermes 2.5 7B on RTX 3080 10GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 84.0 tok/s decode · 2.3s TTFT (warm) · 210 tok/s prefill
What limits this setup
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.
Best improvement path
Performance by 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 |
Quantization options
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 |
Get started
Copy-paste commands to run OpenHermes 2.5 7B on your machine.
Run
ollama run openhermesOpciones de mejora
Hardware que ejecuta bien OpenHermes 2.5 7B
RTX 5070 12GBOpción económicaRTX 4070 Super 12GBMejor relación calidad-precio12 GB VRAM (+2)
B
Este setup está bastante equilibrado para este modelo.89.9 tok/s decodificación
~$599 MSRP
RTX 2080 Ti 11GBMejora NVIDIA11 GB VRAM (+1)
B
Este setup está bastante equilibrado para este modelo.98 tok/s decodificación
~$999 MSRP
Frequently asked questions
Can RTX 3080 10GB run OpenHermes 2.5 7B?
Yes, RTX 3080 10GB can run OpenHermes 2.5 7B with a B grade (Runs well). Expected decode speed: 84.0 tok/s.
How much VRAM does OpenHermes 2.5 7B need?
OpenHermes 2.5 7B (7B parameters) requires approximately 8.1 GB of memory with Q4_K_M quantization.
What is the best quantization for OpenHermes 2.5 7B?
The recommended quantization for OpenHermes 2.5 7B is Q4_K_M, which balances quality and memory efficiency.
What speed will OpenHermes 2.5 7B run at on RTX 3080 10GB?
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.
Can RTX 3080 10GB run OpenHermes 2.5 7B for coding?
For coding workloads, OpenHermes 2.5 7B on RTX 3080 10GB receives a B grade with 84.0 tok/s and 8K context.
What context window can OpenHermes 2.5 7B use on RTX 3080 10GB?
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.
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