HelpingAI2 6B i1 needs ~6.9 GB VRAM. Intel Arc A770 16GB has 16.0 GB. With Q4_K_M quantization, expect ~69 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
68.9 tok/s
TTFT
2812 ms
Safe context
224K
Memory
6.9 GB / 16.0 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 | 68.9 tok/s | 1534 ms | 224K |
| Coding | C | Runs well | 68.9 tok/s | 2812 ms | 224K |
| Agentic Coding | C | Runs well | 68.9 tok/s | 4090 ms | 224K |
| Reasoning | C | Runs well | 68.9 tok/s | 3323 ms | 224K |
| RAG | C | Runs well | 68.9 tok/s | 5112 ms | 224K |
How HelpingAI2 6B i1 (6B params) fits at each quantization level on Intel Arc A770 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.3 GB | Low | C46 |
Q3_K_S | 3 | 2.9 GB | Low | C46 |
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 A770 16GB can run HelpingAI2 6B i1 with a C grade (Runs well). Expected decode speed: 68.9 tok/s.
HelpingAI2 6B i1 (6B parameters) requires approximately 6.9 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 A770 16GB, HelpingAI2 6B i1 achieves approximately 68.9 tokens per second decode speed with a time-to-first-token of 2812ms using Q4_K_M quantization.
For coding workloads, HelpingAI2 6B i1 on Intel Arc A770 16GB receives a C grade with 68.9 tok/s and 224K context.
On Intel Arc A770 16GB, HelpingAI2 6B i1 can safely use up to 224K 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-a770-16gb" 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 |
| C47 |
Q4_K_M | 4 | 3.7 GB | Medium | C47 |
Q5_K_M | 5 | 4.3 GB | High | C48 |
Q6_K | 6 | 4.9 GB | High | C48 |
Q8_0 | 8 | 6.4 GB | Very High | C50 |
F16Best for your GPU | 16 | 12.3 GB | Maximum | C50 |
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.