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URL: https://willitrunai.com/can-run/gemma-2-9b-on-arc-pro-b60-24gb

⇱ Gemma 2 9B on Intel Arc Pro B60 24GB? YES

Can Gemma 2 9B run on Intel Arc Pro B60 24GB?

YES — Runs Great

B66Good
○Estimated from fit model

Gemma 2 9B needs ~13.9 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~36 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: Balanced
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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 13.9 GB, 35.7 tok/s, Runs well
13.9 GB required24.0 GB available
58% VRAM used

Fit status

Runs well

Decode

35.7 tok/s

TTFT

5424 ms

Safe context

8K

Memory

13.9 GB / 24.0 GB

Memory breakdown

Weights5.5 GB
KV Cache5.1 GB
Runtime0.9 GB
Headroom2.4 GB

See how fast it feels

See how fast it feelsGemma 2 9B on Intel Arc Pro B60 24GB
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: 35.7 tok/s decode · 5.4s TTFT (warm) · 89 tok/s prefill

What limits this setup

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.

Best improvement path

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.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatBRuns well35.7 tok/s2959 ms8K
CodingBRuns well35.7 tok/s5424 ms8K
Agentic CodingBRuns well35.7 tok/s7890 ms8K
ReasoningBRuns well35.7 tok/s6411 ms8K
RAGBRuns well35.7 tok/s9862 ms8K

Quantization options

How Gemma 2 9B (9B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.5 GB
LowB59
Q3_K_S
3
4.4 GB
LowB60
NVFP4
4
5.0 GB
MediumB60
Q4_K_M
4
5.5 GB
MediumB60
Q5_K_M
5
6.5 GB
HighB61
Q6_K
6
7.4 GB
HighB61
Q8_0
8
9.6 GB
Very HighB63
F16Best for your GPU
16
18.5 GB
MaximumB64

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

👁 NVIDIA
RTX 5090 32GBBudget pick
32 GB VRAM (+8)1792 GB/s (+1336)
B
Raises estimated decode speed by about 300%.142.7 tok/s decode

Raises estimated decode speed by about 300%.

Adds memory headroom for longer context windows and future model growth.

This is not only a hardware jump. It also gives you a cleaner runtime ecosystem for local LLM tooling.

~$1,999 MSRP

MacBook Pro M4 Max 36GBBest value
36 GB Unified (+12)
B
This setup is broadly balanced for this model.40.8 tok/s decode

~$2,499 MSRP

Frequently asked questions

See all results for Intel Arc Pro B60 24GBSee all hardware for Gemma 2 9B