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 RTX 5090 run google/gemma-2-2b-it?

Runs Q432GB VRAM availableRequires 1GB+

RTX 5090 meets the minimum VRAM requirement for Q4 inference of google/gemma-2-2b-it. Review the quantization breakdown below to see how higher precision settings impact VRAM and throughput.

What this means for you

RTX 5090 can run google/gemma-2-2b-it with Q4 quantization. At approximately 390 tokens/second, you can expect Excellent speed - conversational response times under 1 second.

You have 31GB headroom, which is sufficient for system overhead and smooth operation.

Quantization breakdown

QuantizationVRAM neededVRAM availableEstimated speedVerdict
Q41GB32GB390.12 tok/s✅ Fits comfortably
Q82GB32GB255.58 tok/s✅ Fits comfortably
FP164GB32GB145.39 tok/s✅ Fits comfortably

Suitable alternatives

AMD Instinct MI300X
192GB
979.74 tok/s
Price: —
NVIDIA H200 SXM 141GB
141GB
872.99 tok/s
Price: —
NVIDIA H200 SXM 141GB
141GB
625.72 tok/s
Price: —
AMD Instinct MI300X
192GB
603.41 tok/s
Price: —
NVIDIA H100 SXM5 80GB
80GB
584.42 tok/s
Price: —

More questions

RTX 5090 specs & pricingFull guide for google/gemma-2-2b-itgoogle/gemma-2-2b-it speed on RTX 5090google/gemma-2-2b-it Q4 requirements