HelpingAI2 6B needs ~4.3 GB VRAM. Intel Arc A370M 4GB has 4.0 GB. With Q2_K quantization, expect ~13 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
1.7 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
5.4 tok/s
TTFT
35790 ms
Safe context
4K
Memory
5.7 GB / 4.0 GB
Offload
30%
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 | F | Too heavy | 6.2 tok/s | 17058 ms | 4K |
| Coding | F | Too heavy | 5.4 tok/s | 35790 ms | 4K |
| Agentic Coding | F | Too heavy | 4.2 tok/s | 66600 ms | 4K |
| Reasoning | F | Too heavy | 5.4 tok/s | 42297 ms | 4K |
| RAG | F | Too heavy | 4.2 tok/s | 83251 ms | 4K |
How HelpingAI2 6B (6B params) fits at each quantization level on Intel Arc A370M 4GB (4.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.3 GB | Low | F0 |
Q3_K_S | 3 | 2.9 GB | Low | F0 |
NVFP4 | 4 |
Copy-paste commands to run HelpingAI2 6B on your machine.
Run
lms load hf-helpingai--helpingai2-6b && 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 6B at Q2_K quantization (Very compromised (needs ~0.2 GB host RAM)). The recommended Q4_K_M requires 5.7 GB which exceeds available memory, but at Q2_K it needs only 4.3 GB. Expected decode speed: 12.6 tok/s.
HelpingAI2 6B (6B parameters) requires approximately 5.7 GB at Q4_K_M quantization. On Intel Arc A370M 4GB, it fits at Q2_K using 4.3 GB.
The recommended quantization is Q4_K_M, but on Intel Arc A370M 4GB the best fitting quantization is Q2_K, which uses 4.3 GB.
On Intel Arc A370M 4GB, HelpingAI2 6B achieves approximately 12.6 tokens per second decode speed with a time-to-first-token of 15398ms using Q2_K quantization.
For coding workloads, HelpingAI2 6B on Intel Arc A370M 4GB receives a F grade with 5.4 tok/s and 4K context.
On Intel Arc A370M 4GB, HelpingAI2 6B can safely use up to 8K tokens of context at Q2_K 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-helpingai--helpingai2-6b-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 | 3.7 GB | Medium | F0 |
Q5_K_M | 5 | 4.3 GB | High | F0 |
Q6_K | 6 | 4.9 GB | High | F0 |
Q8_0 | 8 | 6.4 GB | Very High | F0 |
F16 | 16 | 12.3 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.