DEDUPED Hot AI repos, turned into use cases Get the Monday 5

How to submit a real llama-bench result as a pull request with Llmspeed

Run llama-bench on your machine and add a verified row to the benchmark CSV.

Short answer: Run llama-bench -m your-model.gguf -p 512 -n 128 on your machine; then Fill one row with hardware, model, quant, backend, engine_version, gen_tps, prefill_tps, prompt_tokens, source_url and notes; Append the row to benchmarks/submissions.csv in a fork of the repo. All steps.

  • Money
  • Advanced
  • 1 hour
  • From the README
  • Any agent

Who it is forA local LLM user with a GPU or Mac who wants to improve the accuracy of Llmspeed estimates for hardware others also own.

The problemEstimates for some hardware and models are modeled rather than measured, so real measurements are the most valuable contribution.

The outcomeA pull request that appends your measured row to the benchmark file, with the full llama-bench output attached.

What you need first

  • A web browser to use llmspeed.dev
  • For contributing a benchmark: a machine with llama.cpp and a .gguf model file
  • For local development: npm

Steps

  1. Run llama-bench -m your-model.gguf -p 512 -n 128 on your machine.
  2. Fill one row with hardware, model, quant, backend, engine_version, gen_tps, prefill_tps, prompt_tokens, source_url and notes.
  3. Append the row to benchmarks/submissions.csv in a fork of the repo.
  4. Open a pull request and paste the full llama-bench output into the PR description.
  5. Link a post, issue or gist with the full output in the source_url column.

Prompt for your agent

In the llmspeed repo (github.com/nicorobinreal/llmspeed), run `llama-bench -m <model.gguf> -p 512 -n 128` and append one row to benchmarks/submissions.csv. Expected output: a pull request with one new CSV row and the full llama-bench output in the description. Verify that gen_tps matches the tg128 result and prefill_tps matches the pp512 result.

Caveat. The README asks for the full llama-bench output in the PR description, so a row without it may not be accepted.

Use this in your AI agent

Ask your agent

Using the Deduped MCP, get the setup steps for nicorobinreal/llmspeed and "Submit a real llama-bench result as a pull request"

Add Deduped to Claude Code once

claude mcp add --transport http deduped https://dedupedai.com/mcp

Cursor, ChatGPT, others

Share: X LinkedIn Hacker News Email Copy link

Third-party repos we don't own. AI-written use cases can be wrong. Disclaimer