Can Gemma 2 9B run on Intel Arc B570 10GB?

YES — With Q3_K_S

C53Usable

Estimated from fit model

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.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Host offload

Operating mode

Choose the run profile you care about

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.

Gemma 2 9B at Q4_K_M needs 12.5 GB — too much for Intel Arc B570 10GB (10.0 GB). Runs at Q3_K_S (11.4 GB) with low quality. 2 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 12.5 GB, exceeds 10.0 GB available

12.5 GB required10.0 GB available

125% VRAM needed

2.5 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

14.4 tok/s

TTFT

13456 ms

Safe context

Memory

12.5 GB / 10.0 GB

Offload

20%

Memory breakdown

Weights5.5 GB

KV Cache5.1 GB

Runtime0.9 GB

Headroom1.0 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsGemma 2 9B on Intel Arc B570 10GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
  - Full privacy: your prompts never leave your machine
  - No per-token cost or rate limits
  - Works offline once the model is downloaded
  - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

  Model size     Q4 (4-bit)    Q8 (8-bit)    FP16
  7B params      ~4.3 GB       ~7.5 GB       ~14 GB
  13B params     ~7.9 GB       ~13.9 GB      ~26 GB
  70B params     ~42.7 GB      ~74.9 GB      ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 14.4 tok/s decode · 13.5s TTFT (warm) · 36 tok/s prefill

What limits this setup

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.

Best improvement path

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.

Performance by workload

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

Quantization options

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

Get started

Copy-paste commands to run Gemma 2 9B on your machine.

Run

ollama run gemma2

Opciones de mejora

Hardware que ejecuta bien Gemma 2 9B

Intel Arc B580 12GBOpción económica

12 GB VRAM (+2)456 GB/s (+76)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.21.6 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Sube la velocidad estimada de decodificación alrededor de un 50%.

~$249 MSRP

Intel Arc A770 16GBMejor relación calidad-precio

16 GB VRAM (+6)560 GB/s (+180)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.36.5 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$349 MSRP

Intel Arc Pro B50 16GBMejora Intel

16 GB VRAM (+6)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.17.5 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$399 MSRP

Frequently asked questions

Can Intel Arc B570 10GB run Gemma 2 9B?

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.

How much VRAM does Gemma 2 9B need?

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.

What is the best quantization for Gemma 2 9B?

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.

What speed will Gemma 2 9B run at on Intel Arc B570 10GB?

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.

Can Intel Arc B570 10GB run Gemma 2 9B for coding?

For coding workloads, Gemma 2 9B on Intel Arc B570 10GB receives a F grade with 14.4 tok/s and 8K context.

What context window can Gemma 2 9B use on Intel Arc B570 10GB?

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.

What should I upgrade first if Gemma 2 9B feels slow on Intel Arc B570 10GB?

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.

Would CUDA be a better path than Intel Arc B570 10GB for Gemma 2 9B?

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.

See all results for Intel Arc B570 10GB See all hardware for Gemma 2 9B

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