HelpingAI2.5 5B i1 needs ~4.7 GB VRAM. Intel Arc A370M 4GB has 4.0 GB. With NVFP4 quantization, expect ~11 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
0.9 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
8.7 tok/s
TTFT
22332 ms
Safe context
4K
Memory
4.9 GB / 4.0 GB
Offload
20%
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 | D | Very compromised | 9.9 tok/s | 10709 ms | 4K |
| Coding | F | Too heavy | 8.7 tok/s | 22332 ms | 4K |
| Agentic Coding | F | Too heavy | 6.8 tok/s | 41135 ms | 4K |
| Reasoning | F | Too heavy | 8.7 tok/s | 26393 ms | 4K |
| RAG | F | Too heavy | 6.8 tok/s | 51419 ms | 4K |
How HelpingAI2.5 5B i1 (5B params) fits at each quantization level on Intel Arc A370M 4GB (4.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.0 GB | Low | F0 |
Q3_K_S | 3 | 2.5 GB | Low | F0 |
NVFP4 | 4 |
Copy-paste commands to run HelpingAI2.5 5B i1 on your machine.
Run
lms load hf-mradermacher--helpingai2-5-5b-i1-gguf && lms server startUpgrade options
Intel Arc A380 6GBBudget pickMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$139 MSRP
Intel Arc A580 8GBBest valueMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$179 MSRP
Intel Arc B570 10GBIntel upgradeMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$219 MSRP
Yes, Intel Arc A370M 4GB can run HelpingAI2.5 5B i1 at NVFP4 quantization (Very compromised (needs ~0.4 GB host RAM)). The recommended Q4_K_M requires 4.9 GB which exceeds available memory, but at NVFP4 it needs only 4.7 GB. Expected decode speed: 11.1 tok/s.
HelpingAI2.5 5B i1 (5B parameters) requires approximately 4.9 GB at Q4_K_M quantization. On Intel Arc A370M 4GB, it fits at NVFP4 using 4.7 GB.
The recommended quantization is Q4_K_M, but on Intel Arc A370M 4GB the best fitting quantization is NVFP4, which uses 4.7 GB.
On Intel Arc A370M 4GB, HelpingAI2.5 5B i1 achieves approximately 11.1 tokens per second decode speed with a time-to-first-token of 17502ms using NVFP4 quantization.
For coding workloads, HelpingAI2.5 5B i1 on Intel Arc A370M 4GB receives a F grade with 8.7 tok/s and 4K context.
On Intel Arc A370M 4GB, HelpingAI2.5 5B i1 can safely use up to 4K tokens of context at NVFP4 quantization. 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--helpingai2-5-5b-i1-gguf-on-arc-a370m-4gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| Medium |
| F0 |
Q4_K_M | 4 | 3.1 GB | Medium | F0 |
Q5_K_M | 5 | 3.6 GB | High | F0 |
Q6_K | 6 | 4.1 GB | High | F0 |
Q8_0 | 8 | 5.4 GB | Very High | F0 |
F16 | 16 | 10.3 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.