HelpingAI2.5 10B i1 needs ~9.0 GB VRAM. GTX 1080 8GB has 8.0 GB. With Q4_K_M quantization, expect ~18 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
1.0 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~0.7 GB host RAM)
Decode
17.6 tok/s
TTFT
11031 ms
Safe context
4K
Memory
9.0 GB / 8.0 GB
Offload
10%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
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.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 0.7 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs with offload (needs ~0.3 GB host RAM) | 20.4 tok/s | 5187 ms | 4K |
| Coding | D | Very compromised (needs ~0.7 GB host RAM) | 17.6 tok/s | 11031 ms | 4K |
| Agentic Coding | F | Too heavy | 13.4 tok/s | 21009 ms | 4K |
| Reasoning | D | Very compromised (needs ~0.7 GB host RAM) | 17.6 tok/s | 13036 ms | 4K |
| RAG | F | Too heavy | 13.4 tok/s | 26262 ms |
How HelpingAI2.5 10B i1 (10B params) fits at each quantization level on GTX 1080 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.9 GB | Low | C53 |
Q3_K_SBest for your GPU | 3 | 4.9 GB | Low | C52 |
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 pickRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 86%.
~$329 MSRP
RTX 5060 Ti 16GBBest valueRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 159%.
~$449 MSRP
RTX 4060 Ti 16GBNVIDIA upgradeRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 80%.
~$499 MSRP
Yes, GTX 1080 8GB can run HelpingAI2.5 10B i1 with a D grade (Very compromised (needs ~0.7 GB host RAM)). Expected decode speed: 17.6 tok/s.
HelpingAI2.5 10B i1 (10B parameters) requires approximately 9.0 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 GTX 1080 8GB, HelpingAI2.5 10B i1 achieves approximately 17.6 tokens per second decode speed with a time-to-first-token of 11031ms using Q4_K_M quantization.
For coding workloads, HelpingAI2.5 10B i1 on GTX 1080 8GB receives a D grade with 17.6 tok/s and 4K context.
On GTX 1080 8GB, HelpingAI2.5 10B i1 can safely use up to 4K 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-gtx-1080-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 4K |
| 4 |
5.6 GB |
| Medium |
| F0 |
Q4_K_M | 4 | 6.1 GB | Medium | F0 |
Q5_K_M | 5 | 7.2 GB | High | F0 |
Q6_K | 6 | 8.2 GB | High | F0 |
Q8_0 | 8 | 10.7 GB | Very High | F0 |
F16 | 16 | 20.5 GB | Maximum | F0 |
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.