Gemma 3 4B needs ~6.6 GB VRAM. Intel Arc Pro A60 12GB has 12.0 GB. With Q4_K_M quantization, expect ~56 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
56.0 tok/s
TTFT
3457 ms
Safe context
58K
Memory
6.6 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 | A | Runs well | 56.0 tok/s | 1886 ms | 58K |
| Coding | A | Runs well | 56.0 tok/s | 3457 ms | 58K |
| Agentic Coding | A | Runs well | 56.0 tok/s | 5029 ms | 58K |
| Reasoning | A | Runs well | 56.0 tok/s | 4086 ms | 58K |
| RAG | A | Runs well | 56.0 tok/s | 6286 ms | 58K |
How Gemma 3 4B (4B params) fits at each quantization level on Intel Arc Pro A60 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.6 GB | Low | B69 |
Q3_K_S | 3 | 2.0 GB | Low | B69 |
NVFP4 | 4 |
Copy-paste commands to run Gemma 3 4B on your machine.
Run
ollama run gemma3:4bYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen 3.5 9B | 9B | S | 36.8 tok/s | |
| Qwen 3 14B | 14B | A | 14.9 tok/s |
Yes, Intel Arc Pro A60 12GB can run Gemma 3 4B with a A grade (Runs well). Expected decode speed: 56.0 tok/s.
Gemma 3 4B (4B parameters) requires approximately 6.6 GB of memory with Q4_K_M quantization.
The recommended quantization for Gemma 3 4B is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc Pro A60 12GB, Gemma 3 4B achieves approximately 56.0 tokens per second decode speed with a time-to-first-token of 3457ms using Q4_K_M quantization.
For coding workloads, Gemma 3 4B on Intel Arc Pro A60 12GB receives a A grade with 56.0 tok/s and 58K context.
On Intel Arc Pro A60 12GB, Gemma 3 4B can safely use up to 58K tokens of context. The model's official context limit is 128K, 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-3-4b-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:
2.2 GB |
| Medium |
| B70 |
Q4_K_M | 4 | 2.4 GB | Medium | B70 |
Q5_K_M | 5 | 2.9 GB | High | A70 |
Q6_K | 6 | 3.3 GB | High | A71 |
Q8_0 | 8 | 4.3 GB | Very High | A72 |
F16Best for your GPU | 16 | 8.2 GB | Maximum | A73 |
| Qwen 3 8B | 8B | S | 41.4 tok/s |
 Phi-4-reasoning-plus 14B | 14.7B | A | 12 tok/s |
 Nemotron Nano 8B | 8B | S | 41.4 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.