HelpingAI2 6B i1 needs ~6.5 GB VRAM. Intel Arc Pro A60 12GB has 12.0 GB. With Q4_K_M quantization, expect ~51 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 well
Decode
51.4 tok/s
TTFT
3766 ms
Safe context
142K
Memory
6.5 GB / 12.0 GB
The raw memory story may look fine, but the software ecosystem is still a constraint here.
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 51.4 tok/s | 2054 ms | 142K |
| Coding | C | Runs well | 51.4 tok/s | 3766 ms | 142K |
| Agentic Coding | C | Runs well | 51.4 tok/s | 5478 ms | 142K |
| Reasoning | C | Runs well | 51.4 tok/s | 4451 ms | 142K |
| RAG | C | Runs well | 51.4 tok/s | 6847 ms | 142K |
How HelpingAI2 6B i1 (6B params) fits at each quantization level on Intel Arc Pro A60 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.3 GB | Low | C48 |
Q3_K_S | 3 | 2.9 GB | Low | C49 |
NVFP4 | 4 |
Copy-paste commands to run HelpingAI2 6B i1 on your machine.
Run
lms load hf-mradermacher--helpingai2-6b-i1-gguf && lms server startYes, Intel Arc Pro A60 12GB can run HelpingAI2 6B i1 with a C grade (Runs well). Expected decode speed: 51.4 tok/s.
HelpingAI2 6B i1 (6B parameters) requires approximately 6.5 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 A60 12GB, HelpingAI2 6B i1 achieves approximately 51.4 tokens per second decode speed with a time-to-first-token of 3766ms using Q4_K_M quantization.
For coding workloads, HelpingAI2 6B i1 on Intel Arc Pro A60 12GB receives a C grade with 51.4 tok/s and 142K context.
On Intel Arc Pro A60 12GB, HelpingAI2 6B i1 can safely use up to 142K 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-a60-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
3.4 GB |
| Medium |
| C49 |
Q4_K_M | 4 | 3.7 GB | Medium | C50 |
Q5_K_M | 5 | 4.3 GB | High | C51 |
Q6_K | 6 | 4.9 GB | High | C51 |
Q8_0Best for your GPU | 8 | 6.4 GB | Very High | C52 |
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.