12 GB VRAM (+1)
C
This setup is broadly balanced for this model.39 tok/s decode
~$329 MSRP
Can HelpingAI2.5 10B i1 run on GTX 1080 Ti 11GB?
YES — Tight Fit
C51Usable
○Estimated from fit model
HelpingAI2.5 10B i1 needs ~9.6 GB VRAM. GTX 1080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~47 tok/s.
Runtime: OllamaCapacity: TightBandwidth: MediumStack: BasicBottleneck: Balanced
Share:
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) — 9.6 GB, 46.8 tok/s, Tight fit
9.6 GB required11.0 GB available
87% VRAM used
Fit status
Tight fit
Decode
46.8 tok/s
TTFT
4136 ms
Safe context
35K
Memory
9.6 GB / 11.0 GB
Memory breakdown
Weights6.1 GB
KV Cache1.2 GB
Runtime1.2 GB
Headroom1.1 GB
See how fast it feels
See how fast it feelsHelpingAI2.5 10B i1 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: 46.8 tok/s decode · 4.1s TTFT (warm) · 117 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 | C | Runs well | 46.8 tok/s | 2256 ms | 35K |
| Coding | C | Tight fit | 46.8 tok/s | 4136 ms | 35K |
| Agentic Coding | C | Runs with offload | 46.8 tok/s | 6015 ms | 35K |
| Reasoning | C | Tight fit | 46.8 tok/s | 4888 ms | 35K |
| RAG | C | Runs with offload | 46.8 tok/s | 7519 ms | 35K |
Quantization options
How HelpingAI2.5 10B i1 (10B params) fits at each quantization level on GTX 1080 Ti 11GB (11.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.9 GB | Low | C51 |
Q3_K_S | 3 | 4.9 GB | Low | C52 |
NVFP4 | 4 | 5.6 GB | Medium | C52 |
Q4_K_M | 4 | 6.1 GB | Medium | C52 |
Q5_K_MBest for your GPU | 5 | 7.2 GB | High | C51 |
Q6_K | 6 | 8.2 GB | High | F0 |
Q8_0 | 8 | 10.7 GB | Very High | F0 |
F16 | 16 | 20.5 GB | Maximum | F0 |
Get started
Copy-paste commands to run HelpingAI2.5 10B i1 on your machine.
Run
lms load hf-mradermacher--helpingai2-5-10b-i1-gguf && lms server startUpgrade options
Hardware that runs HelpingAI2.5 10B i1 well
16 GB VRAM (+5)
C
Adds memory headroom for longer context windows and future model growth.45.5 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
16 GB VRAM (+5)
C
Adds memory headroom for longer context windows and future model growth.34.5 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$499 MSRP