# AI use cases for vLLM RLT (InferMatrixLab/vllm-rlt)

Repo: [InferMatrixLab/vllm-rlt](https://github.com/InferMatrixLab/vllm-rlt) | Use cases: 4 | Page: https://dedupedai.com/use-cases/infermatrixlab-vllm-rlt/

vllm-rlt is a standalone inference and serving engine for recurrent language models such as Ouro-1.4B, Nanbeige4.2-3B, and Huginn-0125.

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

- Serve a looped model behind an OpenAI-style endpoint: https://dedupedai.com/use-cases/infermatrixlab-vllm-rlt/serve-looped-model-openai-endpoint/
- Compare fixed-depth and early-exit decoding on Ouro: https://dedupedai.com/use-cases/infermatrixlab-vllm-rlt/compare-fixed-depth-vs-early-exit/
- Generate text for a prompt list offline in Python: https://dedupedai.com/use-cases/infermatrixlab-vllm-rlt/run-offline-batch-generation/
- Benchmark depth-aware serving on your own GPUs: https://dedupedai.com/use-cases/infermatrixlab-vllm-rlt/benchmark-serving-on-own-gpus/

## How to serve a looped model behind an OpenAI-style endpoint with vLLM RLT

Repo: [InferMatrixLab/vllm-rlt](https://github.com/InferMatrixLab/vllm-rlt) | Difficulty: Intermediate | Time: 60 minutes | Basis: From the README

Host Ouro-1.4B or another supported checkpoint so apps can call a streaming completions API.

**Who it is for:** Backend engineer who needs a self-hosted text generation endpoint for an internal app and has an NVIDIA GPU host.

**The problem:** Calling a recurrent language model directly from app code is hard to batch and stream, and the team wants a familiar completions interface.

**The outcome:** A running server that answers completions requests with streamed tokens, reachable from any client that speaks the OpenAI completions format.

### Steps

1. Run `uv pip install -e .` from the repository root after activating your Python environment.
2. Follow the step-by-step user guide in docs/launching.md to create an environment and download the chosen checkpoint.
3. Start the server as described in docs/launching.md.
4. Send a first completions request to the server and confirm that tokens come back.
5. Point your app at the endpoint and enable streaming if the app needs incremental output.

### Prompt to paste into your agent

```text
In the vllm-rlt repository, follow docs/launching.md to install the project, download ByteDance/Ouro-1.4B, and start the server. Send one completions request with the prompt <your test prompt> and report the returned text. Verify that the response is non-empty and that the server log shows no errors.
```

### Caveat

The README calls v0.1.0 experimental and recommends starting with synchronous execution, so keep advanced async options off for the first deployment.

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/infermatrixlab-vllm-rlt/serve-looped-model-openai-endpoint/

## How to compare fixed-depth and early-exit decoding on Ouro with vLLM RLT

Repo: [InferMatrixLab/vllm-rlt](https://github.com/InferMatrixLab/vllm-rlt) | Difficulty: Advanced | Time: 120 minutes | Basis: From the README

Measure how loop depth settings change Ouro-1.4B outputs for a chosen set of prompts.

**Who it is for:** Research engineer studying adaptive computation in looped transformers who needs side-by-side runs on the same prompts.

**The problem:** Early-exit decoding saves compute only if the outputs stay acceptable, and the team needs evidence for their own prompts.

**The outcome:** A small table of outputs and loop-depth behavior for fixed-depth and adaptive exit runs, plus a note on where the results differ.

### Steps

1. Run `uv pip install -e .` from the repository root.
2. Read the Ouro runtime notes in docs/cdb_runtime.md and the GSM8K evaluation guide in docs/accuracy.md to choose a fair test set.
3. Write a short Python script with the Python API that runs the same prompts once with fixed-depth decoding and once with Ouro's learned early-exit gate. (inferred, check the README)
4. Set the per-request loop bound and exit threshold for each run and record the outputs.
5. Compare the two result sets and note where outputs or depths diverge.

### Prompt to paste into your agent

```text
In the vllm-rlt repository, use the Python API to run Ouro-1.4B on the prompts in <prompt file> twice, once with fixed four-loop decoding and once with the early-exit gate. Output a table with prompt, both answers, and the loop depth used. Verify that both runs use the same prompts and the same decoding settings except for the exit policy.
```

### Caveat

The README notes that output and exit-depth differences remain unresolved in some configurations, so treat any comparison as specific to your prompts and settings.

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/infermatrixlab-vllm-rlt/compare-fixed-depth-vs-early-exit/

## How to generate text for a prompt list offline in Python with vLLM RLT

Repo: [InferMatrixLab/vllm-rlt](https://github.com/InferMatrixLab/vllm-rlt) | Difficulty: Intermediate | Time: 60 minutes | Basis: From the README

Run many prompts through a supported checkpoint in one Python script without starting a server.

**Who it is for:** Data engineer who needs to generate text for a fixed list of prompts as part of a batch job on a GPU host.

**The problem:** A batch job should not depend on a running HTTP server, but it still needs efficient batching across prompts.

**The outcome:** A script that writes one generated output per input prompt to a file, using the offline Python API.

### Steps

1. Run `uv pip install -e .` from the repository root.
2. Follow docs/launching.md to download the chosen checkpoint, then read the Python API section of the documentation.
3. Write a Python script that loads the checkpoint through the Python API and passes your prompt list as a batch. (inferred, check the README)
4. Choose greedy decoding for repeatable output or seeded top-k or top-p sampling for varied output.
5. Write each prompt and its generated text to an output file and check the line count against the input.

### Prompt to paste into your agent

```text
In the vllm-rlt repository, write and run a Python script that loads ByteDance/Ouro-1.4B through the Python API and generates text for every prompt in <input file>, writing results to <output file>. Expected output is one line per prompt. Verify that the output has the same number of lines as the input.
```

### Caveat

The README documents the Python API and sampling options at a feature level, so confirm exact function names in the documentation before relying on them in a script.

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/infermatrixlab-vllm-rlt/run-offline-batch-generation/

## How to benchmark depth-aware serving on your own GPUs with vLLM RLT

Repo: [InferMatrixLab/vllm-rlt](https://github.com/InferMatrixLab/vllm-rlt) | Difficulty: Advanced | Time: 180 minutes | Basis: From the README

Reproduce the published baseline protocol on your hardware before choosing a serving layout.

**Who it is for:** Infrastructure lead deciding whether to run one replica or split prefill and decode pools across several GPUs on one host.

**The problem:** Published numbers come from specific hardware and workloads, so the team needs its own measurements before committing to a layout.

**The outcome:** A short report comparing throughput and latency for your GPU setup against the recorded baselines, with the workload described.

### Steps

1. Run `uv pip install -e .` from the repository root and confirm the server starts on your GPUs.
2. Read the performance baselines in the README and the linked PR #30 and PR #31 protocols to match concurrency, output length, and prompt length.
3. Read the prefill/decode disaggregation notes in the documentation to plan a P and D pool layout on one host.
4. Run the same fixed-arrival or concurrency workload against one replica and against a split layout, recording TTFT, ITL, and throughput. (inferred, check the README)
5. Write a summary that states the hardware, model, feature flags, and the measured percentage changes.

### Prompt to paste into your agent

```text
In the vllm-rlt repository, follow the protocol in the linked PR #30 and PR #31 to benchmark Ouro-1.4B BF16 on <your GPU setup> with a single replica and with a split prefill/decode layout. Output a table of throughput, TTFT, and ITL for each layout. Verify that the workload settings match across both runs.
```

### Caveat

The README says the published results are workload-specific and not a fresh v0.1.0 benchmark, so your numbers will only be comparable if the protocol matches exactly.

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/infermatrixlab-vllm-rlt/benchmark-serving-on-own-gpus/