12 GB VRAM (+2)
C
This setup is broadly balanced for this model.39 tok/s decode
~$329 MSRP
Can HelpingAI2.5 10B i1 run on RTX 3080 10GB?
YES — Tight Fit
C53Usable
○Estimated from fit model
HelpingAI2.5 10B i1 needs ~9.5 GB VRAM. RTX 3080 10GB has 10.0 GB. With Q4_K_M quantization, expect ~95 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) — 9.5 GB, 94.7 tok/s, Tight fit
9.5 GB required10.0 GB available
95% VRAM used
Fit status
Tight fit
Decode
94.7 tok/s
TTFT
2045 ms
Safe context
23K
Memory
9.5 GB / 10.0 GB
Memory breakdown
Weights6.1 GB
KV Cache1.2 GB
Runtime1.2 GB
Headroom1.0 GB
See how fast it feels
See how fast it feelsHelpingAI2.5 10B i1 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: 94.7 tok/s decode · 2.0s TTFT (warm) · 237 tok/s prefill
What limits this setup
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.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 94.7 tok/s | 1115 ms | 23K |
| Coding | C | Tight fit | 94.7 tok/s | 2045 ms | 23K |
| Agentic Coding | C | Runs with offload (needs ~0.4 GB host RAM) | 62.3 tok/s | 4522 ms | 23K |
| Reasoning | C | Tight fit | 94.7 tok/s | 2416 ms | 23K |
| RAG | C | Runs with offload (needs ~0.4 GB host RAM) | 62.3 tok/s | 5652 ms |
Quantization options
How HelpingAI2.5 10B i1 (10B params) fits at each quantization level on RTX 3080 10GB (10.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.9 GB | Low | C52 |
Q3_K_S | 3 | 4.9 GB | Low | C52 |
NVFP4 | 4 |
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