L
localai.computer
ModelsGPUsSystemsAI SetupsBuildsOpenClawMethodology

Resources

  • Methodology
  • Submit Benchmark
  • About

Browse

  • AI Models
  • GPUs
  • PC Builds

Guides

  • OpenClaw Guide
  • How-To Guides

Legal

  • Privacy
  • Terms
  • Contact

© 2025 localai.computer. Hardware recommendations for running AI models locally.

ℹ️We earn from qualifying purchases through affiliate links at no extra cost to you. This supports our free content and research.

Can Apple M2 Max run google/gemma-2-9b-it?

Runs Q496GB VRAM availableRequires 5GB+

Apple M2 Max meets the minimum VRAM requirement for Q4 inference of google/gemma-2-9b-it. Review the quantization breakdown below to see how higher precision settings impact VRAM and throughput.

What this means for you

Apple M2 Max can run google/gemma-2-9b-it with Q4 quantization. At approximately 40 tokens/second, you can expect Moderate speed - useful for batch processing.

You have 91GB headroom, which is sufficient for system overhead and smooth operation.

Quantization breakdown

QuantizationVRAM neededVRAM availableEstimated speedVerdict
Q45GB96GB40.40 tok/s✅ Fits comfortably
Q810GB96GB29.27 tok/s✅ Fits comfortably
FP1620GB96GB14.38 tok/s✅ Fits comfortably

Suitable alternatives

AMD Instinct MI300X
192GB
546.38 tok/s
Price: —
NVIDIA H200 SXM 141GB
141GB
504.56 tok/s
Price: —
AMD Instinct MI300X
192GB
393.56 tok/s
Price: —
AMD Instinct MI250X
128GB
378.36 tok/s
Price: —
NVIDIA H100 SXM5 80GB
80GB
376.93 tok/s
Price: —

More questions

Apple M2 Max specs & pricingFull guide for google/gemma-2-9b-itgoogle/gemma-2-9b-it speed on Apple M2 Maxgoogle/gemma-2-9b-it Q4 requirements