Gemma 2 9B needs ~11.4 GB VRAM. Intel Arc B570 10GB has 10.0 GB. With Q3_K_S quantization, expect ~20 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
2.5 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
14.4 tok/s
TTFT
13456 ms
Safe context
8K
Memory
12.5 GB / 10.0 GB
Offload
20%
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 | B | Runs with offload | 29.7 tok/s | 3550 ms | 8K |
| Coding | F | Too heavy | 14.4 tok/s | 13456 ms | 8K |
| Agentic Coding | F | Too heavy | 7.1 tok/s | 39428 ms | 8K |
| Reasoning | F | Too heavy | 14.4 tok/s | 15903 ms | 8K |
| RAG | F | Too heavy | 7.1 tok/s | 49286 ms | 8K |
How Gemma 2 9B (9B params) fits at each quantization level on Intel Arc B570 10GB (10.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | B66 |
Q3_K_S | 3 | 4.4 GB | Low | B67 |
NVFP4 | 4 | 5.0 GB | Medium | B67 |
Q4_K_M | 4 | 5.5 GB | Medium | B67 |
Q5_K_MBest for your GPU | 5 | 6.5 GB | High | B67 |
Q6_K | 6 | 7.4 GB | High | F0 |
Q8_0 | 8 | 9.6 GB | Very High | F0 |
F16 | 16 | 18.5 GB | Maximum | F0 |
Copy-paste commands to run Gemma 2 9B on your machine.
Run
ollama run gemma2Upgrade options
Intel Arc B580 12GBBudget pickMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 50%.
~$249 MSRP
Intel Arc A770 16GBBest valueMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$349 MSRP
Intel Arc Pro B50 16GBIntel upgradeMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$399 MSRP
Yes, Intel Arc B570 10GB can run Gemma 2 9B at Q3_K_S quantization (Very compromised (needs ~0.6 GB host RAM)). The recommended Q4_K_M requires 12.5 GB which exceeds available memory, but at Q3_K_S it needs only 11.4 GB. Expected decode speed: 20.0 tok/s.
Gemma 2 9B (9B parameters) requires approximately 12.5 GB at Q4_K_M quantization. On Intel Arc B570 10GB, it fits at Q3_K_S using 11.4 GB.
The recommended quantization is Q4_K_M, but on Intel Arc B570 10GB the best fitting quantization is Q3_K_S, which uses 11.4 GB.
On Intel Arc B570 10GB, Gemma 2 9B achieves approximately 20.0 tokens per second decode speed with a time-to-first-token of 9669ms using Q3_K_S quantization.
For coding workloads, Gemma 2 9B on Intel Arc B570 10GB receives a F grade with 14.4 tok/s and 8K context.
On Intel Arc B570 10GB, Gemma 2 9B can safely use up to 8K tokens of context at Q3_K_S quantization. The model's official context limit is 8K, 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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