RX 9070 16GBBudget pick
16 GB VRAM (+4)640 GB/s (+256)
B
Raises estimated decode speed by about 111%.78.6 tok/s decode
Raises estimated decode speed by about 111%.
Adds memory headroom for longer context windows and future model growth.
~$479 MSRP
Yi 1.5 9B needs ~9.1 GB VRAM. Intel Arc Pro A60 12GB has 12.0 GB. With Q4_K_M quantization, expect ~37 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
37.3 tok/s
TTFT
5194 ms
Safe context
4K
Memory
9.1 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 | B | Runs well | 37.3 tok/s | 2833 ms | 4K |
| Coding | B | Runs well | 37.3 tok/s | 5194 ms | 4K |
| Agentic Coding | B | Tight fit | 37.3 tok/s | 7555 ms | 4K |
| Reasoning | B | Runs well | 37.3 tok/s | 6139 ms | 4K |
| RAG | B | Tight fit | 37.3 tok/s | 9444 ms | 4K |
How Yi 1.5 9B (9B params) fits at each quantization level on Intel Arc Pro A60 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C54 |
Q3_K_S | 3 | 4.4 GB | Low | B56 |
NVFP4 | 4 |
Copy-paste commands to run Yi 1.5 9B on your machine.
Run
lms load Yi-1.5-9B-Chat && 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 105%.
Adds memory headroom for longer context windows and future model growth.
~$499 MSRP
Yes, Intel Arc Pro A60 12GB can run Yi 1.5 9B with a B grade (Runs well). Expected decode speed: 37.3 tok/s.
Yi 1.5 9B (9B parameters) requires approximately 9.1 GB of memory with Q4_K_M quantization.
The recommended quantization for Yi 1.5 9B is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc Pro A60 12GB, Yi 1.5 9B achieves approximately 37.3 tokens per second decode speed with a time-to-first-token of 5194ms using Q4_K_M quantization.
For coding workloads, Yi 1.5 9B on Intel Arc Pro A60 12GB receives a B grade with 37.3 tok/s and 4K context.
On Intel Arc Pro A60 12GB, Yi 1.5 9B can safely use up to 4K tokens of context. The model's official context limit is 4K, 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/yi-1.5-9b-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:
5.0 GB |
| Medium |
| B56 |
Q4_K_M | 4 | 5.5 GB | Medium | B57 |
Q5_K_M | 5 | 6.5 GB | High | B57 |
Q6_KBest for your GPU | 6 | 7.4 GB | High | B56 |
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.