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RTX 5060 Ti Local Language Model Testing Update
I previously shared some testing results for the RTX 5060 Ti in local language model environments. Since then, I have cleaned up and structured the project more effectively.
The project now consists of a more organized benchmark/recipe repository with a static results explorer, schema-validated benchmark JSON, clear notes on llama.cpp/vLLM, single-card and dual-card RTX 5060 Ti recipes, a model-agnostic download helper, and better labels for generation speed, prompt evaluation speed, MTP/no-MTP, and thinking mode.
The repository is available at: https://github.com/5p00kyy/club-5060ti. The results explorer can be found here: https://5p00kyy.github.io/club-5060ti/.
The baseline used in the testing is still the RTX 5060 Ti with 16GB of VRAM, particularly for runs involving larger models like Qwen3.6. I do not want to imply that these results are universal; instead, they offer a structured approach to benchmarking and reporting.
One point raised in comments was about using different GPU architectures together. My current understanding is that llama.cpp/GGUF is the best starting place for testing on non-5060 Ti setups or mixed-GPU environments, while vLLM NVFP4/MTP should not be assumed to work unchanged across all architectures.
Mixed-card and non-5060 Ti results are welcome but should be reported separately. They do not blend into the baseline of two RTX 5060 Ti cards.
What would be useful from other testers:
- dual 5060 Ti results on different CPUs/motherboards
- mixed-GPU and non-5060 Ti
llama.cppresults - vLLM version drift reports
- clear failure reports, not just successful runs
An older LLMBench row has been imported as archived historical data to maintain the integrity of the project. The plan is to rerun useful cases under a new benchmark protocol rather than relying on mixed-method results.
https://github.com/5p00kyy/llm-bench has been folded into this project as the results/data side of club-5060ti, replacing its previous role as a separate benchmark repository.
If you test something, please include all relevant details. Those are crucial for making the results useful and comparable.
Additional Update
I have adjusted the project’s framing to make it more broadly applicable to RTX 5060 Ti local inference scenarios. The repository now splits into distinct hardware lanes:
- 1x RTX 5060 Ti
- 2x RTX 5060 Ti
- 3x/4x+ RTX 5060 Ti
- Mixed RTX 5060 Ti + other CUDA GPUs
- Other CUDA GPU comparisons and adaptations
This approach ensures that single-card setups, quad-card configurations, and mixed systems are treated separately without implying direct comparability.
Key Takeaways
- The project has been refactored for better organization and clarity.
- Mixed-GPU and non-5060 Ti results are now encouraged to be reported separately.
- A new benchmark protocol is being adopted, replacing mixed-method results.
- Distinct hardware lanes have been established in the repository for different configurations.
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Originally published at reddit.com. Curated by AI Maestro.
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