HelpingAI2.5 10B i1 needs ~11.7 GB VRAM. NVIDIA V100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~99 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
98.9 tok/s
TTFT
1958 ms
Safe context
294K
Memory
11.7 GB / 32.0 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 | 98.9 tok/s | 1068 ms | 294K |
| Coding | C | Runs well | 98.9 tok/s | 1958 ms | 294K |
| Agentic Coding | C | Runs well | 98.9 tok/s | 2849 ms | 294K |
| Reasoning | C | Runs well | 98.9 tok/s | 2315 ms | 294K |
| RAG | C | Runs well | 98.9 tok/s | 3561 ms | 294K |
How HelpingAI2.5 10B i1 (10B params) fits at each quantization level on NVIDIA V100 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.9 GB | Low | C43 |
Q3_K_S | 3 | 4.9 GB | Low | C43 |
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 V100 32GB can run HelpingAI2.5 10B i1 with a C grade (Runs well). Expected decode speed: 98.9 tok/s.
HelpingAI2.5 10B i1 (10B parameters) requires approximately 11.7 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 V100 32GB, HelpingAI2.5 10B i1 achieves approximately 98.9 tokens per second decode speed with a time-to-first-token of 1958ms using Q4_K_M quantization.
For coding workloads, HelpingAI2.5 10B i1 on NVIDIA V100 32GB receives a C grade with 98.9 tok/s and 294K context.
On NVIDIA V100 32GB, HelpingAI2.5 10B i1 can safely use up to 294K 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-v100-32gb" 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 |
| C43 |
Q4_K_M | 4 | 6.1 GB | Medium | C44 |
Q5_K_M | 5 | 7.2 GB | High | C44 |
Q6_K | 6 | 8.2 GB | High | C44 |
Q8_0 | 8 | 10.7 GB | Very High | C46 |
F16Best for your GPU | 16 | 20.5 GB | Maximum | C49 |