HelpingAI 9B 200k i1 needs ~8.6 GB VRAM. Intel Arc B580 12GB has 12.0 GB. With Q4_K_M quantization, expect ~40 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
39.9 tok/s
TTFT
4856 ms
Safe context
67K
Memory
8.6 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 | 39.9 tok/s | 2649 ms | 67K |
| Coding | C | Runs well | 39.9 tok/s | 4856 ms | 67K |
| Agentic Coding | C | Runs well | 39.9 tok/s | 7063 ms | 67K |
| Reasoning | C | Runs well | 39.9 tok/s | 5739 ms | 67K |
| RAG | C | Runs well | 39.9 tok/s | 8829 ms | 67K |
How HelpingAI 9B 200k i1 (9B params) fits at each quantization level on Intel Arc B580 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C49 |
Q3_K_S | 3 | 4.4 GB | Low | C51 |
NVFP4 | 4 |
Copy-paste commands to run HelpingAI 9B 200k i1 on your machine.
Run
lms load hf-mradermacher--helpingai-9b-200k-i1-gguf && lms server startYes, Intel Arc B580 12GB can run HelpingAI 9B 200k i1 with a C grade (Runs well). Expected decode speed: 39.9 tok/s.
HelpingAI 9B 200k i1 (9B parameters) requires approximately 8.6 GB of memory with Q4_K_M quantization.
The recommended quantization for HelpingAI 9B 200k i1 is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc B580 12GB, HelpingAI 9B 200k i1 achieves approximately 39.9 tokens per second decode speed with a time-to-first-token of 4856ms using Q4_K_M quantization.
For coding workloads, HelpingAI 9B 200k i1 on Intel Arc B580 12GB receives a C grade with 39.9 tok/s and 67K context.
On Intel Arc B580 12GB, HelpingAI 9B 200k i1 can safely use up to 67K 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--helpingai-9b-200k-i1-gguf-on-arc-b580-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
5.0 GB |
| Medium |
| C51 |
Q4_K_M | 4 | 5.5 GB | Medium | C52 |
Q5_K_M | 5 | 6.5 GB | High | C52 |
Q6_KBest for your GPU | 6 | 7.4 GB | High | C51 |
Q8_0 | 8 | 9.6 GB | Very High | F0 |
F16 | 16 | 18.5 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.