RTX 5070 12GB经济之选12 GB VRAM (+1)672 GB/s (+188)
B
Raises estimated decode speed by about 36%.98 tok/s 解码
Raises estimated decode speed by about 36%.
~$549 MSRP
Can OpenHermes 2.5 7B run on GTX 1080 Ti 11GB?
YES — Runs Great
B57Good
Estimated from fit model
OpenHermes 2.5 7B needs ~8.5 GB VRAM. GTX 1080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~67 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: 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) — 8.5 GB, 71.9 tok/s, Runs well
8.5 GB required11.0 GB available
77% VRAM used
Fit status
Runs well
Decode
71.9 tok/s
TTFT
2693 ms
Safe context
8K
Memory
8.5 GB / 11.0 GB
Memory breakdown
Weights4.3 GB
KV Cache2.0 GB
Runtime1.2 GB
Headroom1.1 GB
See how fast it feels
See how fast it feelsOpenHermes 2.5 7B on GTX 1080 Ti 11GB
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: 71.9 tok/s decode · 2.7s TTFT (warm) · 180 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 71.9 tok/s | 1469 ms | 8K |
| Coding | B | Runs well | 66.9 tok/s | 2895 ms | 8K |
| Agentic Coding | C | Runs with offload | 71.9 tok/s | 3917 ms | 8K |
| Reasoning | B | Runs well | 71.9 tok/s | 3183 ms | 8K |
| RAG | C | Runs with offload | 71.9 tok/s | 4896 ms | 8K |
Quantization options
How OpenHermes 2.5 7B (7B params) fits at each quantization level on GTX 1080 Ti 11GB (11.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C51 |
Q3_K_S | 3 | 3.4 GB | Low | C52 |
NVFP4 | 4 | 3.9 GB | Medium | C52 |
Q4_K_M | 4 | 4.3 GB | Medium | C53 |
Q5_K_M | 5 | 5.0 GB | High | C54 |
Q6_K | 6 | 5.7 GB | High | C53 |
Q8_0Best for your GPU | 8 | 7.5 GB | Very High | C53 |
F16 | 16 | 14.3 GB | Maximum | F0 |
Get started
Copy-paste commands to run OpenHermes 2.5 7B on your machine.
Run
ollama run openhermes升级选项
能流畅运行 OpenHermes 2.5 7B 的硬件
RTX 5070 12GB经济之选RTX 4070 12GB最佳性价比12 GB VRAM (+1)504 GB/s (+20)
B
Raises estimated decode speed by about 32%.95.2 tok/s 解码
Raises estimated decode speed by about 32%.
~$599 MSRP
RTX 4070 Super 12GBNVIDIA 升级12 GB VRAM (+1)504 GB/s (+20)
B
Raises estimated decode speed by about 36%.97.7 tok/s 解码
Raises estimated decode speed by about 36%.
~$599 MSRP
Frequently asked questions
Can GTX 1080 Ti 11GB run OpenHermes 2.5 7B?
Yes, GTX 1080 Ti 11GB can run OpenHermes 2.5 7B with a B grade (Runs well). Expected decode speed: 66.9 tok/s.
How much VRAM does OpenHermes 2.5 7B need?
OpenHermes 2.5 7B (7B parameters) requires approximately 8.5 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 GTX 1080 Ti 11GB?
On GTX 1080 Ti 11GB, OpenHermes 2.5 7B achieves approximately 66.9 tokens per second decode speed with a time-to-first-token of 2895ms using Q4_K_M quantization.
Can GTX 1080 Ti 11GB run OpenHermes 2.5 7B for coding?
For coding workloads, OpenHermes 2.5 7B on GTX 1080 Ti 11GB receives a B grade with 66.9 tok/s and 8K context.
What context window can OpenHermes 2.5 7B use on GTX 1080 Ti 11GB?
On GTX 1080 Ti 11GB, 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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