RX 9070 16GBBudget pick
16 GB VRAM (+4)640 GB/s (+256)
C
Raises estimated decode speed by about 111%.63.8 tok/s decode
Raises estimated decode speed by about 111%.
Adds memory headroom for longer context windows and future model growth.
~$479 MSRP
Helply 10.2b chat i1 needs ~9.5 GB VRAM. Intel Arc Pro A60 12GB has 12.0 GB. With Q4_K_M quantization, expect ~30 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
30.2 tok/s
TTFT
6402 ms
Safe context
49K
Memory
9.5 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 | 30.2 tok/s | 3492 ms | 49K |
| Coding | C | Runs well | 30.2 tok/s | 6402 ms | 49K |
| Agentic Coding | C | Tight fit | 30.2 tok/s | 9312 ms | 49K |
| Reasoning | C | Runs well | 30.2 tok/s | 7566 ms | 49K |
| RAG | C | Tight fit | 30.2 tok/s | 11640 ms | 49K |
How Helply 10.2b chat i1 (10.199999809265137B params) fits at each quantization level on Intel Arc Pro A60 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.0 GB | Low | C50 |
Q3_K_S | 3 | 5.0 GB | Low | C51 |
NVFP4 | 4 |
Copy-paste commands to run Helply 10.2b chat i1 on your machine.
Run
lms load hf-mradermacher--helply-10-2b-chat-i1-gguf && lms server startUpgrade options
Raises estimated decode speed by about 111%.
Adds memory headroom for longer context windows and future model growth.
~$479 MSRP
Raises estimated decode speed by about 106%.
Adds memory headroom for longer context windows and future model growth.
~$499 MSRP
Yes, Intel Arc Pro A60 12GB can run Helply 10.2b chat i1 with a C grade (Runs well). Expected decode speed: 30.2 tok/s.
Helply 10.2b chat i1 (10.199999809265137B parameters) requires approximately 9.5 GB of memory with Q4_K_M quantization.
The recommended quantization for Helply 10.2b chat i1 is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc Pro A60 12GB, Helply 10.2b chat i1 achieves approximately 30.2 tokens per second decode speed with a time-to-first-token of 6402ms using Q4_K_M quantization.
For coding workloads, Helply 10.2b chat i1 on Intel Arc Pro A60 12GB receives a C grade with 30.2 tok/s and 49K context.
On Intel Arc Pro A60 12GB, Helply 10.2b chat i1 can safely use up to 49K 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--helply-10-2b-chat-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:
5.7 GB |
| Medium |
| C52 |
Q4_K_M | 4 | 6.2 GB | Medium | C52 |
Q5_K_M | 5 | 7.3 GB | High | C51 |
Q6_KBest for your GPU | 6 | 8.4 GB | High | C51 |
Q8_0 | 8 | 10.9 GB | Very High | F0 |
F16 | 16 | 20.9 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.