RayAnyscale
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Related Products
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About
Develop on your laptop and then scale the same Python code elastically across hundreds of nodes or GPUs on any cloud, with no changes. Ray translates existing Python concepts to the distributed setting, allowing any serial application to be easily parallelized with minimal code changes. Easily scale compute-heavy machine learning workloads like deep learning, model serving, and hyperparameter tuning with a strong ecosystem of distributed libraries. Scale existing workloads (for eg. Pytorch) on Ray with minimal effort by tapping into integrations. Native Ray libraries, such as Ray Tune and Ray Serve, lower the effort to scale the most compute-intensive machine learning workloads, such as hyperparameter tuning, training deep learning models, and reinforcement learning. For example, get started with distributed hyperparameter tuning in just 10 lines of code. Creating distributed apps is hard. Ray handles all aspects of distributed execution.
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About
distil labs optimizes AI workloads by replacing expensive frontier-model calls with custom small language models tuned to a specific task while maintaining the required quality bar. It observes real production traffic, captures traces from existing LLM requests, and automatically builds an evaluation set to understand how the workload actually behaves. It then generates and validates synthetic training data, matches the data distribution to the target workload, performs supervised fine-tuning and reinforcement learning, quantizes the model, and deploys an optimized endpoint. Results are automatically evaluated against the current model on accuracy, latency, and efficiency before teams choose to scale traffic. The resulting OpenAI-compatible endpoint combines a specialized SLM, prompt optimization, caching, and tuned serving for the use case.
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Platforms Supported
Windows
Supported
Mac
Supported
Linux
Supported
Cloud
Supported
On-Premises
Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
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Platforms Supported
Windows
Not Supported
Mac
Not Supported
Linux
Not Supported
Cloud
Supported
On-Premises
Not Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
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Audience
ML and AI Engineers, Software Developers
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Audience
AI product, engineering, and machine learning teams in need of a tool to optimize production LLM workloads with task-specific small language models
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Support
Phone Support
Not Supported
24/7 Live Support
Not Supported
Online
Supported
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Support
Phone Support
Not Supported
24/7 Live Support
Supported
Online
Supported
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API
Offers API
Supported
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API
Offers API
Supported
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Screenshots and Videos |
Screenshots and Videos |
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Pricing
Free
Open source. Consumption-based.
Free Version
Supported
Free Trial
Supported
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Pricing
$0.04 per 1M tokens
Free Version
Supported
Free Trial
Not Supported
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Reviews/
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Reviews/
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Training
Documentation
Supported
Webinars
Supported
Live Online
Supported
In Person
Supported
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Training
Documentation
Supported
Webinars
Not Supported
Live Online
Supported
In Person
Not Supported
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Company InformationAnyscale
Founded: 2019
United States
ray.io
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Company Informationdistil labs
Founded: 2024
Germany
www.distillabs.ai/
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Alternatives |
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Categories |
Categories |
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Integrations
Amazon EC2 Trn2 Instances
Supported
Amazon EKS
Supported
Amazon SageMaker
Supported
Amazon Web Services (AWS)
Supported
Anyscale
Supported
Apache Airflow
Supported
Azure Kubernetes Service (AKS)
Supported
Databricks
Supported
Feast
Supported
Flyte
Supported
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Integrations
Amazon EC2 Trn2 Instances
Not Supported
Amazon EKS
Not Supported
Amazon SageMaker
Not Supported
Amazon Web Services (AWS)
Not Supported
Anyscale
Not Supported
Apache Airflow
Not Supported
Azure Kubernetes Service (AKS)
Not Supported
Databricks
Not Supported
Feast
Not Supported
Flyte
Not Supported
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