HelpingAI2 6B i1 needs ~5.9 GB VRAM. Intel Arc Pro A40 6GB has 6.0 GB. With Q4_K_M quantization, expect ~26 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
Runs with offload
Decode
25.7 tok/s
TTFT
7532 ms
Safe context
19K
Memory
5.9 GB / 6.0 GB
The raw memory story may look fine, but the software ecosystem is still a constraint here.
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.
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 | Tight fit | 25.7 tok/s | 4108 ms | 19K |
| Coding | C | Runs with offload | 25.7 tok/s | 7532 ms | 19K |
| Agentic Coding | D | Very compromised (needs ~0.3 GB host RAM) | 15.9 tok/s | 17660 ms | 19K |
| Reasoning | C | Runs with offload | 25.7 tok/s | 8901 ms | 19K |
| RAG | D | Very compromised (needs ~0.3 GB host RAM) | 15.9 tok/s | 22075 ms |
How HelpingAI2 6B i1 (6B params) fits at each quantization level on Intel Arc Pro A40 6GB (6.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.3 GB | Low | C54 |
Q3_K_S | 3 | 2.9 GB | Low | C53 |
NVFP4Best for your GPU |
Copy-paste commands to run HelpingAI2 6B i1 on your machine.
Run
lms load hf-mradermacher--helpingai2-6b-i1-gguf && lms server startUpgrade options
Intel Arc A580 8GBBudget pickRaises estimated decode speed by about 167%.
Adds memory headroom for longer context windows and future model growth.
~$179 MSRP
Intel Arc B570 10GBBest valueRaises estimated decode speed by about 118%.
Adds memory headroom for longer context windows and future model growth.
~$219 MSRP
Intel Arc B580 12GBIntel upgradeRaises estimated decode speed by about 133%.
Adds memory headroom for longer context windows and future model growth.
~$249 MSRP
Yes, Intel Arc Pro A40 6GB can run HelpingAI2 6B i1 with a C grade (Runs with offload). Expected decode speed: 25.7 tok/s.
HelpingAI2 6B i1 (6B parameters) requires approximately 5.9 GB of memory with Q4_K_M quantization.
The recommended quantization for HelpingAI2 6B i1 is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc Pro A40 6GB, HelpingAI2 6B i1 achieves approximately 25.7 tokens per second decode speed with a time-to-first-token of 7532ms using Q4_K_M quantization.
For coding workloads, HelpingAI2 6B i1 on Intel Arc Pro A40 6GB receives a C grade with 25.7 tok/s and 19K context.
On Intel Arc Pro A40 6GB, HelpingAI2 6B i1 can safely use up to 19K 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--helpingai2-6b-i1-gguf-on-arc-pro-a40-6gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 19K |
| 4 |
3.4 GB |
| Medium |
| C53 |
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 |
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.
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.