HelpingAI2.5 10B i1 needs ~13.3 GB VRAM. RTX 6000 Ada 48GB has 48.0 GB. With Q4_K_M quantization, expect ~129 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
129.0 tok/s
TTFT
1500 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 | 129.0 tok/s | 818 ms | 490K |
| Coding | C | Runs well | 129.0 tok/s | 1500 ms | 490K |
| Agentic Coding | C | Runs well | 129.0 tok/s | 2182 ms | 490K |
| Reasoning | C | Runs well | 129.0 tok/s | 1773 ms | 490K |
| RAG | C | Runs well | 129.0 tok/s | 2728 ms | 490K |
How HelpingAI2.5 10B i1 (10B params) fits at each quantization level on RTX 6000 Ada 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 |
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 6000 Ada 48GB can run HelpingAI2.5 10B i1 with a C grade (Runs well). Expected decode speed: 129.0 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 6000 Ada 48GB, HelpingAI2.5 10B i1 achieves approximately 129.0 tokens per second decode speed with a time-to-first-token of 1500ms using Q4_K_M quantization.
For coding workloads, HelpingAI2.5 10B i1 on RTX 6000 Ada 48GB receives a C grade with 129.0 tok/s and 490K context.
On RTX 6000 Ada 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-rtx-6000-ada-48gb" 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 |
| 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 |