HelpingAI2.5 10B i1 needs ~9.0 GB VRAM. Intel Arc A750 8GB has 8.0 GB. With Q4_K_M quantization, expect ~21 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
21.3 tok/s
TTFT
9104 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.
Runtime ecosystem is narrower than CUDA
Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.
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.
Prefer CUDA if you want the path of least resistance
If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
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 | Runs with offload (needs ~0.3 GB host RAM) | 24.5 tok/s | 4308 ms | 4K |
| Coding | D | Very compromised | 21.3 tok/s | 9104 ms | 4K |
| Agentic Coding | F | Too heavy | 16.4 tok/s | 17148 ms | 4K |
| Reasoning | D | Very compromised (needs ~0.7 GB host RAM) | 21.3 tok/s | 10760 ms | 4K |
| RAG | F | Too heavy | 16.4 tok/s | 21435 ms |
How HelpingAI2.5 10B i1 (10B params) fits at each quantization level on Intel Arc A750 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
Intel Arc B570 10GBBudget pickRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 58%.
~$219 MSRP
Intel Arc B580 12GBBest valueRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 69%.
~$249 MSRP
Intel Arc A770 16GBIntel upgradeRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 94%.
~$349 MSRP
Yes, Intel Arc A750 8GB can run HelpingAI2.5 10B i1 with a D grade (Very compromised). Expected decode speed: 21.3 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 Intel Arc A750 8GB, HelpingAI2.5 10B i1 achieves approximately 21.3 tokens per second decode speed with a time-to-first-token of 9104ms using Q4_K_M quantization.
For coding workloads, HelpingAI2.5 10B i1 on Intel Arc A750 8GB receives a D grade with 21.3 tok/s and 4K context.
On Intel Arc A750 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-arc-a750-8gb" 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.
Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.