# AI use cases for Llmspeed (nicorobinreal/llmspeed)

Repo: [nicorobinreal/llmspeed](https://github.com/nicorobinreal/llmspeed) | Use cases: 4 | Page: https://dedupedai.com/use-cases/nicorobinreal-llmspeed/

llmspeed is a static web app that simulates how fast a local LLM streams tokens on a chosen GPU, Mac or CPU, so you can judge speed before buying hardware.

This file holds every use case of this repo. Each one starts with a "## " heading and ends with its URL.

- Check local LLM speed before buying hardware: https://dedupedai.com/use-cases/nicorobinreal-llmspeed/check-local-llm-speed-before-buying/
- Compare two machines side by side in race mode: https://dedupedai.com/use-cases/nicorobinreal-llmspeed/compare-two-machines-side-by-side/
- Submit a real llama-bench result as a pull request: https://dedupedai.com/use-cases/nicorobinreal-llmspeed/submit-a-real-benchmark-result/
- Run Llmspeed locally to test prediction data: https://dedupedai.com/use-cases/nicorobinreal-llmspeed/run-llmspeed-locally-for-development/

## How to check local LLM speed before buying hardware with Llmspeed

Repo: [nicorobinreal/llmspeed](https://github.com/nicorobinreal/llmspeed) | Difficulty: Beginner | Time: 15 minutes | Basis: From the README

Pick a GPU or Mac and model to see the streamed speed and whether it fits in memory.

**Who it is for:** A buyer or developer weighing a GPU or Mac purchase who wants to run a local model and needs to know if it will feel fast enough.

**The problem:** Spec sheets do not show how a model will stream, how long the first token takes, or whether a quantization fits in memory.

**The outcome:** A clear view of tokens per second, time to first token and memory fit for the exact hardware and model, with a trust label on each estimate.

### Steps

1. Open https://llmspeed.dev in a browser.
2. Pick a GPU or Mac from the list and choose a model.
3. Check the memory fit result for each quantization: fits, needs partial offload to system RAM, or won't run.
4. Watch the answer stream and note the wait before the first token for your typical prompt length.
5. Check the Measured, Derived or Projected label to see how much to trust the number.

### Prompt to paste into your agent

```text
Using the llmspeed repo (github.com/nicorobinreal/llmspeed) at https://llmspeed.dev, check the simulated speed and memory fit for <model name> on <hardware name>. Expected output: tokens per second, time to first token, and fit status for each quantization. Verify that the trust label (Measured, Derived or Projected) is shown next to the speed figure.
```

### Caveat

Llmspeed gives estimates rather than guarantees, and drivers, engine versions and thermals can move real numbers by 10 to 30%.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/nicorobinreal-llmspeed/check-local-llm-speed-before-buying/

## How to compare two machines side by side in race mode with Llmspeed

Repo: [nicorobinreal/llmspeed](https://github.com/nicorobinreal/llmspeed) | Difficulty: Beginner | Time: 15 minutes | Basis: From the README

Run two setups against the same model and share the race link with others.

**Who it is for:** A team lead or buyer choosing between two candidate machines, such as a 5090 GPU and a Mac Studio, for the same local model.

**The problem:** Comparing two machines from separate spec pages makes it hard to see which one streams faster for the same workload.

**The outcome:** A side-by-side streamed race between two setups, plus a shareable link to send to colleagues.

### Steps

1. Open https://llmspeed.dev in a browser.
2. Switch on race mode and choose the first hardware setup.
3. Choose the second hardware setup and the same model for both.
4. Watch both answers stream and compare the time to first token.
5. Copy the shareable link for the race and send it to the person who needs to decide.

### Prompt to paste into your agent

```text
Using the llmspeed repo (github.com/nicorobinreal/llmspeed) at https://llmspeed.dev, set up a race between <hardware A> and <hardware B> on <model name>. Expected output: a side-by-side streamed race and a shareable link. Verify that both sides use the same model before you share the link.
```

### Caveat

The race shows estimates, and Llmspeed currently simulates single devices only, so multi-GPU setups cannot be compared.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/nicorobinreal-llmspeed/compare-two-machines-side-by-side/

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

Repo: [nicorobinreal/llmspeed](https://github.com/nicorobinreal/llmspeed) | Difficulty: Advanced | Time: 1 hour | Basis: From the README

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

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

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

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

### 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 to paste into your agent

```text
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.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/nicorobinreal-llmspeed/submit-a-real-benchmark-result/

## How to run Llmspeed locally to test prediction data with Llmspeed

Repo: [nicorobinreal/llmspeed](https://github.com/nicorobinreal/llmspeed) | Difficulty: Intermediate | Time: 30 minutes | Basis: From the README

Install dependencies, start the dev server and build the static site from the repo.

**Who it is for:** A developer who wants to change or check the prediction data or the UI before proposing a change.

**The problem:** Changes to predictions live in a generated data file, and the site needs a local build to check them.

**The outcome:** A running local dev server and a production build that you can test against the predictions in data/app-data.json.

### Steps

1. Clone https://github.com/nicorobinreal/llmspeed and open the folder.
2. Run `npm install`.
3. Run `npm run dev` to start the local dev server.
4. Review data/app-data.json to see the predictions the app uses.
5. Run `npm run build` to create the production build with prerendered landing pages and sitemap.

### Prompt to paste into your agent

```text
In the llmspeed repo (github.com/nicorobinreal/llmspeed), run `npm install`, then `npm run dev`, and report the local URL. Expected output: a running dev server and a successful `npm run build`. Verify that data/app-data.json loads in the app without errors.
```

### Caveat

The README says predictions in data/app-data.json are generated by the benchmark pipeline, so manual edits may be overwritten on the next run.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/nicorobinreal-llmspeed/run-llmspeed-locally-for-development/