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URL: https://willitrunai.com/can-run/gemma-2-9b-on-arc-b570-10gb

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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
Share:

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

8K

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

WorkloadGradeFitDecodeTTFTContext
ChatBRuns with offload29.7 tok/s3550 ms8K
CodingFToo heavy14.4 tok/s13456 ms8K
Agentic CodingFToo heavy7.1 tok/s39428 ms8K
ReasoningFToo heavy14.4 tok/s15903 ms8K
RAGFToo heavy7.1 tok/s49286 ms8K

Quantization options

How Gemma 2 9B (9B params) fits at each quantization level on Intel Arc B570 10GB (10.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.5 GB
LowB66
Q3_K_S
3
4.4 GB
LowB67
NVFP4
4

Get started

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

Run

ollama run gemma2

Upgrade options

Hardware that runs Gemma 2 9B well

👁 Intel
Intel Arc B580 12GBBudget pick
12 GB VRAM (+2)456 GB/s (+76)
C
Makes the model fit on the accelerator instead of staying completely out of reach.21.6 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 50%.

~$249 MSRP

👁 Intel
Intel Arc A770 16GBBest value
16 GB VRAM (+6)560 GB/s (+180)
B
Makes the model fit on the accelerator instead of staying completely out of reach.36.5 tok/s decode

Makes 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
Intel Arc Pro B50 16GBIntel upgrade
16 GB VRAM (+6)
B
Makes the model fit on the accelerator instead of staying completely out of reach.17.5 tok/s decode

Makes 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

Frequently asked questions

See all results for Intel Arc B570 10GBSee all hardware for Gemma 2 9B
5.0 GB
Medium
B67
Q4_K_M
4
5.5 GB
MediumB67
Q5_K_MBest for your GPU
5
6.5 GB
HighB67
Q6_K
6
7.4 GB
HighF0
Q8_0
8
9.6 GB
Very HighF0
F16
16
18.5 GB
MaximumF0

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.