HelpingAI2.5 10B i1 needs ~12.5 GB VRAM. NVIDIA A100 40GB has 40.0 GB. With Q4_K_M quantization, expect ~140 tok/s.
Operating mode
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
Fit status
Runs well
Decode
140.0 tok/s
TTFT
1383 ms
Safe context
392K
Memory
12.5 GB / 40.0 GB
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 140.0 tok/s | 754 ms | 392K |
| Coding | C | Runs well | 140.0 tok/s | 1383 ms | 392K |
| Agentic Coding | C | Runs well | 140.0 tok/s | 2011 ms | 392K |
| Reasoning | C | Runs well | 140.0 tok/s | 1634 ms | 392K |
| RAG | C | Runs well | 140.0 tok/s | 2514 ms | 392K |
How HelpingAI2.5 10B i1 (10B params) fits at each quantization level on NVIDIA A100 40GB (40.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.9 GB | Low | C42 |
Q3_K_S | 3 | 4.9 GB | Low | C42 |
NVFP4 | 4 |
Copy-paste commands to run HelpingAI2.5 10B i1 on your machine.
Run
lms load hf-mradermacher--helpingai2-5-10b-i1-gguf && lms server startYes, NVIDIA A100 40GB can run HelpingAI2.5 10B i1 with a C grade (Runs well). Expected decode speed: 140.0 tok/s.
HelpingAI2.5 10B i1 (10B parameters) requires approximately 12.5 GB of memory with Q4_K_M quantization.
The recommended quantization for HelpingAI2.5 10B i1 is Q4_K_M, which balances quality and memory efficiency.
On NVIDIA A100 40GB, HelpingAI2.5 10B i1 achieves approximately 140.0 tokens per second decode speed with a time-to-first-token of 1383ms using Q4_K_M quantization.
For coding workloads, HelpingAI2.5 10B i1 on NVIDIA A100 40GB receives a C grade with 140.0 tok/s and 392K context.
On NVIDIA A100 40GB, HelpingAI2.5 10B i1 can safely use up to 392K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-mradermacher--helpingai2-5-10b-i1-gguf-on-a100-40gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
5.6 GB |
| Medium |
| C42 |
Q4_K_M | 4 | 6.1 GB | Medium | C42 |
Q5_K_M | 5 | 7.2 GB | High | C43 |
Q6_K | 6 | 8.2 GB | High | C43 |
Q8_0 | 8 | 10.7 GB | Very High | C44 |
F16Best for your GPU | 16 | 20.5 GB | Maximum | C48 |