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Batch LLM Inference is better with Sutro

Run LLM Batch Jobs in Hours, Not Days, at a Fraction of the Cost.

Get Started

Generate a question/answer pair for the following chunk of vLLM documentation

Inputs

Outputs

Intro to vLLM

vLLM is a fast and easy-to-use library for LLM inference and serving. Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry.

Loading Models

vLM models can be loaded in two different ways. To pass a loaded model into the vLLM framework for further processing and inference without reloading it from disk or a model hub, first start by generating


Using the Open AI Server

Run:ai Model Streamer is a library to read tensors in concurrency, while streaming it to GPU memory. Further reading can be found in Run:ai Model Streamer Documentation.

vLLM supports loading weights in Safetensors format using the Run:ai Model Streamer. You first need to install vLLM RunAI optional dependency:

Question: Is vLLM compatible with all open-source models? ...

Question: How do I load a custom model from HuggingFace? ...

Question: Can I use the OpenAI compatible server to replace calls...

+128 more…

Batch LLM Inference is better with Sutro

Run LLM Batch Jobs in Hours, Not Days, at a Fraction of the Cost.

Get Started

Generate a question/answer pair for the following chunk of vLLM documentation

Inputs

Outputs

Intro to vLLM

vLLM is a fast and easy-to-use library for LLM inference and serving. Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry.

Loading Models

vLM models can be loaded in two different ways. To pass a loaded model into the vLLM framework for further processing and inference without reloading it from disk or a model hub, first start by generating


Using the Open AI Server

Run:ai Model Streamer is a library to read tensors in concurrency, while streaming it to GPU memory. Further reading can be found in Run:ai Model Streamer Documentation.

vLLM supports loading weights in Safetensors format using the Run:ai Model Streamer. You first need to install vLLM RunAI optional dependency:

Question: Is vLLM compatible with all open-source models? ...

Question: How do I load a custom model from HuggingFace? ...

Question: Can I use the OpenAI compatible server to replace calls...

+128 more…

Image labeling

Label Millions of Images at a Fraction of the Cost

Enrich your data with meaningful labels to improve model training and data preparation. Run LLM batch jobs in hours, not days, without the pain of managing infrastructure.

Get Access

Generate a question/answer pair for the following chunk of vLLM documentation

Inputs

Outputs

Intro to vLLM

vLLM is a fast and easy-to-use library for LLM inference and serving. Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry.

Loading Models

vLM models can be loaded in two different ways. To pass a loaded model into the vLLM framework for further processing and inference without reloading it from disk or a model hub, first start by generating


Using the Open AI Server

Run:ai Model Streamer is a library to read tensors in concurrency, while streaming it to GPU memory. Further reading can be found in Run:ai Model Streamer Documentation.

vLLM supports loading weights in Safetensors format using the Run:ai Model Streamer. You first need to install vLLM RunAI optional dependency:

Question: Is vLLM compatible with all open-source models? ...

Question: How do I load a custom model from HuggingFace? ...

Question: Can I use the OpenAI compatible server to replace calls...

+128 more…

Batch LLM Inference is better with Sutro

Run LLM Batch Jobs in Hours, Not Days, at a Fraction of the Cost.

Get Started

Generate a question/answer pair for the following chunk of vLLM documentation

Inputs

Outputs

Intro to vLLM

vLLM is a fast and easy-to-use library for LLM inference and serving. Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry.

Loading Models

vLM models can be loaded in two different ways. To pass a loaded model into the vLLM framework for further processing and inference without reloading it from disk or a model hub, first start by generating


Using the Open AI Server

Run:ai Model Streamer is a library to read tensors in concurrency, while streaming it to GPU memory. Further reading can be found in Run:ai Model Streamer Documentation.

vLLM supports loading weights in Safetensors format using the Run:ai Model Streamer. You first need to install vLLM RunAI optional dependency:

Question: Is vLLM compatible with all open-source models? ...

Question: How do I load a custom model from HuggingFace? ...

Question: Can I use the OpenAI compatible server to replace calls...

+128 more…

Batch LLM Inference is better with Sutro

Run LLM Batch Jobs in Hours, Not Days, at a Fraction of the Cost.

Get Started

Generate a question/answer pair for the following chunk of vLLM documentation

Inputs

Outputs

Intro to vLLM

vLLM is a fast and easy-to-use library for LLM inference and serving. Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry.

Loading Models

vLM models can be loaded in two different ways. To pass a loaded model into the vLLM framework for further processing and inference without reloading it from disk or a model hub, first start by generating


Using the Open AI Server

Run:ai Model Streamer is a library to read tensors in concurrency, while streaming it to GPU memory. Further reading can be found in Run:ai Model Streamer Documentation.

vLLM supports loading weights in Safetensors format using the Run:ai Model Streamer. You first need to install vLLM RunAI optional dependency:

Question: Is vLLM compatible with all open-source models? ...

Question: How do I load a custom model from HuggingFace? ...

Question: Can I use the OpenAI compatible server to replace calls...

+128 more…

From Idea to Millions of Labels, Simplified

Sutro takes the pain away from testing and scaling LLM batch jobs to unblock your most ambitious AI projects.

import sutro as so

from pydantic import BaseModel

class ReviewClassifier(BaseModel):

sentiment: str

user_reviews = '.

User_reviews.csv

User_reviews-1.csv

User_reviews-2.csv

User_reviews-3.csv

system_prompt = 'Classify the review as positive, neutral, or negative.'

results = so.infer(user_reviews, system_prompt, output_schema=ReviewClassifier)

Progress: 1% | 1/514,879 | Input tokens processed: 0.41m, Tokens generated: 591k

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Prototype

Start small and iterate fast on your image labeling workflows. Accelerate experiments by testing on Sutro before committing to large jobs.

Scale

Scale your labeling workflows so your team can do more in less time. Process millions of images in hours, not days, with no infrastructure headaches or exploding costs.

Integrate

Seamlessly connect Sutro to your existing LLM workflows. Sutro's Python SDK is compatible with popular data orchestration tools, like Airflow and Dagster.

Reduce Costs by 10x or More

Get results faster and significantly reduce costs by parallelizing your LLM calls through Sutro. Process massive datasets without exploding your budget.

Scale Effortlessly to Millions of Images

Scale Effortlessly to Millions of Images

Scale Effortlessly to Millions of Images

Confidently handle millions of requests, and billions of tokens at a time. Go from a small sample to full-scale production without infrastructure headaches.

Shorten Development Cycles

Rapidly prototype your labeling workflows. Get feedback from large batch jobs in as little as minutes before scaling up to your entire dataset.

Image Classification

Longer description goes here, should span multiple lines.

Synthetic data generation

Generate high-quality, diverse data to improve model performance, without the complexity.

Structured Extraction

Transform unstructured data into structured insights that drive business decisions.

Document OCR

Convert massive amounts of scanned documents into machine-readable text for further analysis.

RAG data preparation

Easily convert large corpuses of text into vector representations for semantic search and recommendations.

Metadata generation

Enrich your data with meaningful tags and metadata to improve search and data preparation.

Image Classification

Longer description goes here, should span multiple lines.

Synthetic data generation

Generate high-quality, diverse data to improve model performance, without the complexity.

Structured Extraction

Transform unstructured data into structured insights that drive business decisions.

Document OCR

Convert massive amounts of scanned documents into machine-readable text for further analysis.

RAG data preparation

Easily convert large corpuses of text into vector representations for semantic search and recommendations.

Metadata generation

Enrich your data with meaningful tags and metadata to improve search and data preparation.

Image Classification

Longer description goes here, should span multiple lines.

Synthetic data generation

Generate high-quality, diverse data to improve model performance, without the complexity.

Structured Extraction

Transform unstructured data into structured insights that drive business decisions.

Document OCR

Convert massive amounts of scanned documents into machine-readable text for further analysis.

RAG data preparation

Easily convert large corpuses of text into vector representations for semantic search and recommendations.

Metadata generation

Enrich your data with meaningful tags and metadata to improve search and data preparation.

FAQ

What is Sutro?

How does Sutro reduce costs?

What kind of tasks can I perform with Sutro?

Can I test my workflow before committing to a large job?

How does Sutro integrate with my existing tools?

What Will You Scale with Sutro?

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