24 GB VRAM (+8)936 GB/s (+736)
C
Raises estimated decode speed by about 320%.107.4 tok/s decode
Raises estimated decode speed by about 320%.
Adds memory headroom for longer context windows and future model growth.
~$1,499 MSRP
Can HelpingAI2.5 10B i1 run on NVIDIA A2 16GB?
YES — Runs Great
C51Usable
○Estimated from fit model
HelpingAI2.5 10B i1 needs ~10.1 GB VRAM. NVIDIA A2 16GB has 16.0 GB. With Q4_K_M quantization, expect ~26 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: Very lowStack: BasicBottleneck: Memory bandwidth
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) — 10.1 GB, 25.6 tok/s, Runs well
10.1 GB required16.0 GB available
63% VRAM used
Fit status
Runs well
Decode
25.6 tok/s
TTFT
7570 ms
Safe context
97K
Memory
10.1 GB / 16.0 GB
Memory breakdown
Weights6.1 GB
KV Cache1.2 GB
Runtime1.2 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsHelpingAI2.5 10B i1 on NVIDIA A2 16GB
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: 25.6 tok/s decode · 7.6s TTFT (warm) · 64 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 | C | Runs well | 25.6 tok/s | 4129 ms | 97K |
| Coding | C | Runs well | 25.6 tok/s | 7570 ms | 97K |
| Agentic Coding | C | Runs well | 25.6 tok/s | 11011 ms | 97K |
| Reasoning | C | Runs well | 25.6 tok/s | 8947 ms | 97K |
| RAG | C | Runs well | 25.6 tok/s | 13764 ms | 97K |
Quantization options
How HelpingAI2.5 10B i1 (10B params) fits at each quantization level on NVIDIA A2 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.9 GB | Low | C47 |
Q3_K_S | 3 | 4.9 GB | Low | C48 |
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
Hardware that runs HelpingAI2.5 10B i1 well
24 GB VRAM (+8)1008 GB/s (+808)
C
Raises estimated decode speed by about 391%.125.6 tok/s decode
Raises estimated decode speed by about 391%.
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP
24 GB VRAM (+8)672 GB/s (+472)
C
Raises estimated decode speed by about 261%.92.5 tok/s decode
Raises estimated decode speed by about 261%.
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP