Llama 3.2 11B Vision needs ~11.5 GB VRAM. Intel Arc A770 16GB has 16.0 GB. With Q4_K_M quantization, expect ~38 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
40.4 tok/s
TTFT
4795 ms
Safe context
16K
Memory
11.5 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 | 40.4 tok/s | 2616 ms | 16K |
| Coding | B | Runs well | 37.6 tok/s | 5155 ms | 16K |
| Agentic Coding | B | Tight fit | 40.4 tok/s | 6975 ms | 16K |
| Reasoning | B | Runs well | 40.4 tok/s | 5667 ms | 16K |
| RAG | B | Tight fit | 40.4 tok/s | 8719 ms | 16K |
How Llama 3.2 11B Vision (11B params) fits at each quantization level on Intel Arc A770 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.3 GB | Low | B62 |
Q3_K_S | 3 | 5.4 GB | Low | B64 |
NVFP4 | 4 |
Copy-paste commands to run Llama 3.2 11B Vision on your machine.
Run
ollama run llama3.2-vision:11bYes, Intel Arc A770 16GB can run Llama 3.2 11B Vision with a B grade (Runs well). Expected decode speed: 37.6 tok/s.
Llama 3.2 11B Vision (11B parameters) requires approximately 11.5 GB of memory with Q4_K_M quantization.
The recommended quantization for Llama 3.2 11B Vision is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc A770 16GB, Llama 3.2 11B Vision achieves approximately 37.6 tokens per second decode speed with a time-to-first-token of 5155ms using Q4_K_M quantization.
For coding workloads, Llama 3.2 11B Vision on Intel Arc A770 16GB receives a B grade with 37.6 tok/s and 16K context.
On Intel Arc A770 16GB, Llama 3.2 11B Vision can safely use up to 16K tokens of context. The model's official context limit is 16K, 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/llama-3.2-11b-vision-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:
6.2 GB |
| Medium |
| B64 |
Q4_K_M | 4 | 6.7 GB | Medium | B65 |
Q5_K_M | 5 | 7.9 GB | High | B66 |
Q6_K | 6 | 9.0 GB | High | B66 |
Q8_0Best for your GPU | 8 | 11.8 GB | Very High | B65 |
F16 | 16 | 22.5 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.