RTX 3060 12GBBudget pick12 GB VRAM (+2)
C
This setup is broadly balanced for this model.39 tok/s decode
~$329 MSRP
HelpingAI2.5 10B i1 needs ~9.5 GB VRAM. RTX 3080 10GB has 10.0 GB. With Q4_K_M quantization, expect ~95 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
94.7 tok/s
TTFT
2045 ms
Safe context
23K
Memory
9.5 GB / 10.0 GB
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 94.7 tok/s | 1115 ms | 23K |
| Coding | C | Tight fit | 94.7 tok/s | 2045 ms | 23K |
| Agentic Coding | C | Runs with offload (needs ~0.4 GB host RAM) | 62.3 tok/s | 4522 ms | 23K |
| Reasoning | C | Tight fit | 94.7 tok/s | 2416 ms | 23K |
| RAG | C | Runs with offload (needs ~0.4 GB host RAM) | 62.3 tok/s | 5652 ms | 23K |
How HelpingAI2.5 10B i1 (10B params) fits at each quantization level on RTX 3080 10GB (10.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.9 GB | Low | C52 |
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 5070 12GBBest value~$549 MSRP
RTX 4070 Super 12GBNVIDIA upgrade~$599 MSRP
Yes, RTX 3080 10GB can run HelpingAI2.5 10B i1 with a C grade (Tight fit). Expected decode speed: 94.7 tok/s.
HelpingAI2.5 10B i1 (10B parameters) requires approximately 9.5 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 RTX 3080 10GB, HelpingAI2.5 10B i1 achieves approximately 94.7 tokens per second decode speed with a time-to-first-token of 2045ms using Q4_K_M quantization.
For coding workloads, HelpingAI2.5 10B i1 on RTX 3080 10GB receives a C grade with 94.7 tok/s and 23K context.
On RTX 3080 10GB, HelpingAI2.5 10B i1 can safely use up to 23K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
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-3080-10gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: