Gemma 4 on a Used 8 GB RTX 3050: 116 Tokens/s for One User, 618 for 64

8 relevance

Benchmark and tutorial for running Gemma 4 on consumer hardware, highly actionable.

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Why It Matters

For engineers building cost-efficient AI infrastructure, this demonstrates that a sub-$200 GPU can serve Gemma 4 at competitive throughput, challenging cloud-first assumptions for inference deployment.

Key Takeaways

Evaluate used RTX 3050 OEMs for local LLM serving to reduce cloud inference costs while maintaining performance.

Gemma 4 on a Used 8 GB RTX 3050: 116 Tokens/s for One User, 618 for 64

Summary

A used RTX 3050 OEM (8 GB, $179.97) runs Gemma 4 E2B and E4B via llama.cpp with QAT GGUF, achieving 116 tok/s single-user and 618 tok/s for 64 users at full 131k context, undercutting AWS T4 rental by 342 hours. Python MCP tools automate deployment, while flash attention and per-layer embedding offloading optimize VRAM usage.

Author

xbill