Successfully Running Gemma4-26B On-Prem? Looking to Discuss Deployment Struggles & Stable Setups

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Hi everyone,

I’ve been working on deploying Gemma4-26B on-premise at a server level using vLLM, and I wanted to connect with others who have successfully set it up or faced similar challenges.

During deployment, I ran into several issues including:

  • CUDA driver mismatches

  • PyTorch/CUDA compatibility problems

  • vLLM engine initialization failures

  • GemmaTokenizer compatibility errors

  • Transformers version conflicts

  • GPU initialization issues

  • Docker vs native environment differences

  • FlashAttention setup concerns

After multiple debugging attempts and environment changes, I’m trying to understand what deployment stacks are currently the most stable for Gemma4-26B in production/on-prem environments.

Would love to discuss:

  • Working CUDA + driver combinations

  • Stable PyTorch/vLLM/Transformers versions

  • Docker vs non-Docker deployment experiences

  • Multi-GPU setups

  • Quantized deployments

  • Recommended inference settings

  • Production stability observations

If anyone has successfully deployed Gemma4-26B reliably, I’d really appreciate hearing about your setup and lessons learned.

Thanks!

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