Gemma 3 12B needs ~14.7 GB VRAM. Intel Arc A770 16GB has 16.0 GB. With Q4_K_M quantization, expect ~27 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
Tight fit
Decode
27.4 tok/s
TTFT
7067 ms
Safe context
20K
Memory
14.7 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 | A | Runs well | 27.4 tok/s | 3855 ms | 20K |
| Coding | A | Tight fit | 27.4 tok/s | 7067 ms | 20K |
| Agentic Coding | F | Too heavy | 13.4 tok/s | 20978 ms | 20K |
| Reasoning | A | Tight fit | 27.4 tok/s | 8352 ms | 20K |
| RAG | F | Too heavy | 13.4 tok/s | 26222 ms | 20K |
How Gemma 3 12B (12B params) fits at each quantization level on Intel Arc A770 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.7 GB | Low | A78 |
Q3_K_S | 3 | 5.9 GB | Low | A79 |
NVFP4 | 4 |
Copy-paste commands to run Gemma 3 12B on your machine.
Run
ollama run gemma3:12bYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
| 14B | S | 31.9 tok/s | ||
| 14.7B | S | 30.2 tok/s |
Yes, Intel Arc A770 16GB can run Gemma 3 12B with a A grade (Tight fit). Expected decode speed: 27.4 tok/s.
Gemma 3 12B (12B parameters) requires approximately 14.7 GB of memory with Q4_K_M quantization.
The recommended quantization for Gemma 3 12B is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc A770 16GB, Gemma 3 12B achieves approximately 27.4 tokens per second decode speed with a time-to-first-token of 7067ms using Q4_K_M quantization.
For coding workloads, Gemma 3 12B on Intel Arc A770 16GB receives a A grade with 27.4 tok/s and 20K context.
On Intel Arc A770 16GB, Gemma 3 12B can safely use up to 20K 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/gemma-3-12b-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:
6.7 GB |
| Medium |
| A80 |
Q4_K_M | 4 | 7.3 GB | Medium | A81 |
Q5_K_M | 5 | 8.6 GB | High | A81 |
Q6_KBest for your GPU | 6 | 9.8 GB | High | A81 |
Q8_0 | 8 | 12.8 GB | Very High | F0 |
F16 | 16 | 24.6 GB | Maximum | F0 |
| 21B | A | 29.2 tok/s |
| 14B | S | 31.7 tok/s |
| 22B | A | 10.7 tok/s |
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.