HelpingAI2.5 10B i1 needs ~13.3 GB VRAM. RTX A6000 48GB has 48.0 GB. With Q4_K_M quantization, expect ~96 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
95.7 tok/s
TTFT
2023 ms
Safe context
490K
Memory
13.3 GB / 48.0 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 | 95.7 tok/s | 1104 ms | 490K |
| Coding | C | Runs well | 95.7 tok/s | 2023 ms | 490K |
| Agentic Coding | C | Runs well | 95.7 tok/s | 2943 ms | 490K |
| Reasoning | C | Runs well | 95.7 tok/s | 2391 ms | 490K |
| RAG | C | Runs well | 95.7 tok/s | 3679 ms | 490K |
How HelpingAI2.5 10B i1 (10B params) fits at each quantization level on RTX A6000 48GB (48.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.9 GB | Low | C41 |
Q3_K_S | 3 | 4.9 GB | Low | C41 |
NVFP4 | 4 | 5.6 GB | Medium | C41 |
Q4_K_M | 4 | 6.1 GB | Medium | C41 |
Q5_K_M | 5 | 7.2 GB | High | C42 |
Q6_K | 6 | 8.2 GB | High | C42 |
Q8_0 | 8 | 10.7 GB | Very High | C42 |
F16Best for your GPU | 16 | 20.5 GB | Maximum | C46 |
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, RTX A6000 48GB can run HelpingAI2.5 10B i1 with a C grade (Runs well). Expected decode speed: 95.7 tok/s.
HelpingAI2.5 10B i1 (10B parameters) requires approximately 13.3 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 RTX A6000 48GB, HelpingAI2.5 10B i1 achieves approximately 95.7 tokens per second decode speed with a time-to-first-token of 2023ms using Q4_K_M quantization.
For coding workloads, HelpingAI2.5 10B i1 on RTX A6000 48GB receives a C grade with 95.7 tok/s and 490K context.
On RTX A6000 48GB, HelpingAI2.5 10B i1 can safely use up to 490K 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-a6000-48gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: