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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…

Content moderation

Scale your content moderation without scaling your costs

Use Sutro to classify millions of pieces of user-generated content in hours, not days. Ensure a safe and compliant community 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 Moderation at Scale, Simplified

Sutro takes the pain away from testing and scaling LLM batch moderation jobs to protect your platform and users.

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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Rapidly Prototype

Start small and iterate fast on your content moderation workflows. Accelerate experiments by testing on Sutro before committing to large jobs to find the right prompts and models.

Scale

Scale your moderation workflows to process billions of tokens in hours, not days, with no infrastructure headaches or exploding costs.

Integrate

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

Scale Effortlessly

Confidently handle millions of user-generated posts, comments, and reviews at a time without the pain of managing infrastructure.

Reduce Costs by 10x or More

Reduce Costs by 10x or More

Reduce Costs by 10x or More

Get moderation results faster and reduce costs by parallelizing your LLM classification calls through Sutro.

Ship Faster

Automatically organize your content into meaningful categories without involving your ML engineer. Shorten development cycles by getting feedback from large batch jobs in minutes.

Sentiment analysis

Longer description goes here, should span multiple lines.

Text classification

Automatically organize your data into meaningful categories without involving your ML engineer.

Structured Extraction

Transform unstructured data into structured insights that drive business decisions.

Customer review analysis

Easily sift through thousands of product reviews and unlock valuable product insights while brewing your morning coffee.

Data scraping

Crawl millions of web pages, and extract analytics-ready datasets for your company or your customers.

Entity extraction

Transform unstructured data into structured insights that drive business decisions.

Sentiment analysis

Longer description goes here, should span multiple lines.

Text classification

Automatically organize your data into meaningful categories without involving your ML engineer.

Structured Extraction

Transform unstructured data into structured insights that drive business decisions.

Customer review analysis

Easily sift through thousands of product reviews and unlock valuable product insights while brewing your morning coffee.

Data scraping

Crawl millions of web pages, and extract analytics-ready datasets for your company or your customers.

Entity extraction

Transform unstructured data into structured insights that drive business decisions.

Sentiment analysis

Longer description goes here, should span multiple lines.

Text classification

Automatically organize your data into meaningful categories without involving your ML engineer.

Structured Extraction

Transform unstructured data into structured insights that drive business decisions.

Customer review analysis

Easily sift through thousands of product reviews and unlock valuable product insights while brewing your morning coffee.

Data scraping

Crawl millions of web pages, and extract analytics-ready datasets for your company or your customers.

Entity extraction

Transform unstructured data into structured insights that drive business decisions.

FAQ

What can I do with Sutro?

What kind of scale can Sutro handle?

How does Sutro help reduce costs?

How do I integrate Sutro into my current workflow?

What are some common use cases for Sutro?

What Will You Scale with Sutro?

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