Yi Coder 9B needs ~9.5 GB VRAM. Intel Arc A770 16GB has 16.0 GB. With Q4_K_M quantization, expect ~46 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
49.9 tok/s
TTFT
3878 ms
Safe context
87K
Memory
9.5 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 | B | Runs well | 45.9 tok/s | 2301 ms | 87K |
| Coding | B | Runs well | 45.9 tok/s | 4218 ms | 87K |
| Agentic Coding | B | Runs well | 45.9 tok/s | 6135 ms | 87K |
| Reasoning | B | Runs well | 45.9 tok/s | 4985 ms | 87K |
| RAG | B | Runs well | 45.9 tok/s | 7669 ms | 87K |
How Yi Coder 9B (9B params) fits at each quantization level on Intel Arc A770 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | B60 |
Q3_K_S | 3 | 4.4 GB | Low | B60 |
NVFP4 | 4 |
Copy-paste commands to run Yi Coder 9B on your machine.
Run
lms load Yi-Coder-9B-Chat && lms server startYes, Intel Arc A770 16GB can run Yi Coder 9B with a B grade (Runs well). Expected decode speed: 45.9 tok/s.
Yi Coder 9B (9B parameters) requires approximately 9.5 GB of memory with Q4_K_M quantization.
The recommended quantization for Yi Coder 9B is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc A770 16GB, Yi Coder 9B achieves approximately 45.9 tokens per second decode speed with a time-to-first-token of 4218ms using Q4_K_M quantization.
For coding workloads, Yi Coder 9B on Intel Arc A770 16GB receives a B grade with 45.9 tok/s and 87K context.
On Intel Arc A770 16GB, Yi Coder 9B can safely use up to 87K tokens of context. The model's official context limit is 131K, 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-coder-9b-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:
5.0 GB |
| Medium |
| B61 |
Q4_K_M | 4 | 5.5 GB | Medium | B61 |
Q5_K_M | 5 | 6.5 GB | High | B62 |
Q6_K | 6 | 7.4 GB | High | B63 |
Q8_0Best for your GPU | 8 | 9.6 GB | Very High | B63 |
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.