gemma 3 12b it needs ~10.6 GB VRAM. Intel Arc B570 10GB has 10.0 GB. With Q4_K_M quantization, expect ~19 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
0.6 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.4 GB host RAM)
Decode
18.9 tok/s
TTFT
10223 ms
Safe context
9K
Memory
10.6 GB / 10.0 GB
Offload
10%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
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.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs with offload | 28.0 tok/s | 3767 ms | 9K |
| Coding | D | Runs with offload (needs ~0.4 GB host RAM) | 18.9 tok/s | 10223 ms | 9K |
| Agentic Coding | F | Too heavy | 14.7 tok/s | 19160 ms | 9K |
| Reasoning | D | Runs with offload (needs ~0.4 GB host RAM) | 18.9 tok/s | 12081 ms | 9K |
| RAG | F | Too heavy | 14.7 tok/s | 23950 ms | 9K |
How gemma 3 12b it (12B params) fits at each quantization level on Intel Arc B570 10GB (10.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.7 GB | Low | C53 |
Q3_K_S | 3 | 5.9 GB | Low | C53 |
NVFP4Best for your GPU | 4 | 6.7 GB | Medium | C52 |
Q4_K_M | 4 | 7.3 GB | Medium | F0 |
Q5_K_M | 5 | 8.6 GB | High | F0 |
Q6_K | 6 | 9.8 GB | High | F0 |
Q8_0 | 8 | 12.8 GB | Very High | F0 |
F16 | 16 | 24.6 GB | Maximum | F0 |
Copy-paste commands to run gemma 3 12b it on your machine.
Run
lms load hf-maziyarpanahi--gemma-3-12b-it-gguf && lms server startアップグレードオプション
Intel Arc B580 12GBコスパ重視Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 58%.
〜$249 MSRP
Intel Arc A770 16GBコスパ最良Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 82%.
〜$349 MSRP
Intel Arc Pro B50 16GBIntelアップグレードRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Adds memory headroom for longer context windows and future model growth.
〜$399 MSRP
Yes, Intel Arc B570 10GB can run gemma 3 12b it with a D grade (Runs with offload (needs ~0.4 GB host RAM)). Expected decode speed: 18.9 tok/s.
gemma 3 12b it (12B parameters) requires approximately 10.6 GB of memory with Q4_K_M quantization.
The recommended quantization for gemma 3 12b it is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc B570 10GB, gemma 3 12b it achieves approximately 18.9 tokens per second decode speed with a time-to-first-token of 10223ms using Q4_K_M quantization.
For coding workloads, gemma 3 12b it on Intel Arc B570 10GB receives a D grade with 18.9 tok/s and 9K context.
On Intel Arc B570 10GB, gemma 3 12b it can safely use up to 9K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
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.
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