RTX 3060 12GBBudget pick12 GB VRAM (+1)
C
This setup is broadly balanced for this model.39 tok/s decode
~$329 MSRP
HelpingAI2.5 10B i1 needs ~9.6 GB VRAM. RTX 2080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~66 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
Tight fit
Decode
65.6 tok/s
TTFT
2949 ms
Safe context
35K
Memory
9.6 GB / 11.0 GB
This setup is broadly balanced for this model.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 65.6 tok/s | 1609 ms | 35K |
| Coding | C | Tight fit | 65.6 tok/s | 2949 ms | 35K |
| Agentic Coding | C | Runs with offload | 65.6 tok/s | 4290 ms | 35K |
| Reasoning | C | Tight fit | 65.6 tok/s | 3486 ms | 35K |
| RAG | C | Runs with offload | 65.6 tok/s | 5363 ms | 35K |
How HelpingAI2.5 10B i1 (10B params) fits at each quantization level on RTX 2080 Ti 11GB (11.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.9 GB | Low | C51 |
Q3_K_S | 3 | 4.9 GB | Low | C52 |
NVFP4 | 4 | 5.6 GB | Medium | C52 |
Q4_K_M | 4 | 6.1 GB | Medium | C52 |
Q5_K_MBest for your GPU | 5 | 7.2 GB | High | C51 |
Q6_K | 6 | 8.2 GB | High | F0 |
Q8_0 | 8 | 10.7 GB | Very High | F0 |
F16 | 16 | 20.5 GB | Maximum | F0 |
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
RTX 3060 12GBBudget pick~$329 MSRP
RTX 5060 Ti 16GBBest valueAdds memory headroom for longer context windows and future model growth.
~$449 MSRP
RTX 5070 12GBNVIDIA upgrade~$549 MSRP
Yes, RTX 2080 Ti 11GB can run HelpingAI2.5 10B i1 with a C grade (Tight fit). Expected decode speed: 65.6 tok/s.
HelpingAI2.5 10B i1 (10B parameters) requires approximately 9.6 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 2080 Ti 11GB, HelpingAI2.5 10B i1 achieves approximately 65.6 tokens per second decode speed with a time-to-first-token of 2949ms using Q4_K_M quantization.
For coding workloads, HelpingAI2.5 10B i1 on RTX 2080 Ti 11GB receives a C grade with 65.6 tok/s and 35K context.
On RTX 2080 Ti 11GB, HelpingAI2.5 10B i1 can safely use up to 35K 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-2080-ti-11gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: