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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…

Product page generation

Generate thousands of product pages in hours, at a fraction of the cost

Effortlessly create compelling, SEO-friendly product pages for your entire catalog. Sutro’s batch processing turns a massive project into a simple, fast, and cost-effective task.

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 a Full Catalog, Simplified

Sutro takes the pain away from generating product pages at scale, unblocking your most ambitious e-commerce 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 page generation prompts. Accelerate experiments by testing on a sample of products before committing to a large job.

Scale

Scale your product page generation so your team can do more in less time. Process your entire catalog in hours, not days, with no infrastructure headaches or exploding costs.

Integrate

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

Scale your catalog effortlessly

From a few new SKUs to thousands, generate rich product pages for your entire inventory without worrying about infrastructure. Confidently handle millions of requests at a time.

Reduce content creation costs by 10x

Reduce content creation costs by 10x

Reduce content creation costs by 10x

Get results faster and slash your budget. Sutro's parallel processing makes generating product pages significantly more affordable than traditional methods or one-off API calls.

Launch products faster

Shorten development cycles by turning bulk content generation into a task that runs in hours, not days. Get your products to market quicker than ever before.

Product description enrichment

Longer description goes here, should span multiple lines.

SEO meta description generation

Automatically create optimized meta descriptions for your entire site to improve search rankings.

Product insight mining

Easily sift through thousands of product reviews to unlock valuable insights.

Structured Extraction

Transform unstructured manufacturer specs into structured insights that drive business decisions.

Content Personalization

Tailor marketing and advertising efforts to thousands of individuals, personas, and demographics.

Customer review analysis

Analyze sentiment and key topics from customer feedback at scale.

Product description enrichment

Longer description goes here, should span multiple lines.

SEO meta description generation

Automatically create optimized meta descriptions for your entire site to improve search rankings.

Product insight mining

Easily sift through thousands of product reviews to unlock valuable insights.

Structured Extraction

Transform unstructured manufacturer specs into structured insights that drive business decisions.

Content Personalization

Tailor marketing and advertising efforts to thousands of individuals, personas, and demographics.

Customer review analysis

Analyze sentiment and key topics from customer feedback at scale.

Product description enrichment

Longer description goes here, should span multiple lines.

SEO meta description generation

Automatically create optimized meta descriptions for your entire site to improve search rankings.

Product insight mining

Easily sift through thousands of product reviews to unlock valuable insights.

Structured Extraction

Transform unstructured manufacturer specs into structured insights that drive business decisions.

Content Personalization

Tailor marketing and advertising efforts to thousands of individuals, personas, and demographics.

Customer review analysis

Analyze sentiment and key topics from customer feedback at scale.

FAQ

What is Sutro?

How does Sutro reduce costs?

How do I integrate Sutro into my workflow?

What kind of tasks can I run with Sutro?

How does Sutro handle scaling?

What Will You Scale with Sutro?

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