DLRover VS Ray

Let’s have a side-by-side comparison of DLRover vs Ray to find out which one is better. This software comparison between DLRover and Ray is based on genuine user reviews. Compare software prices, features, support, ease of use, and user reviews to make the best choice between these, and decide whether DLRover or Ray fits your business.

DLRover

DLRover
DLRover simplifies large AI model training. Offers fault-tolerance, flash checkpoint, auto-scaling. Speeds up training with PyTorch & TensorFlow extensions.

Ray

Ray
Ray is the AI Compute Engine. It powers the world's top AI platforms, supports all AI/ML workloads, scales from laptop to thousands of GPUs, and is Python - native. Unlock AI potential with Ray!

DLRover

Launched
Pricing Model Free
Starting Price
Tech used
Tag Software Development,Data Science

Ray

Launched 2013-01
Pricing Model Free
Starting Price
Tech used Google Tag Manager,HubSpot Analytics,Next.js,Gzip,OpenGraph,Progressive Web App,Webpack,Cowboy
Tag Mlops,Developer Tools,Data Science

DLRover Rank/Visit

Global Rank
Country
Month Visit

Top 5 Countries

Traffic Sources

Ray Rank/Visit

Global Rank 159921
Country China
Month Visit 270570

Top 5 Countries

29.88%
13.52%
7.22%
4.91%
3.69%
China United States Taiwan Germany Canada

Traffic Sources

1.61%
0.53%
0.07%
8.43%
48.92%
40.43%
social paidReferrals mail referrals search direct

Estimated traffic data from Similarweb

What are some alternatives?

When comparing DLRover and Ray, you can also consider the following products

LoRAX - LoRAX (LoRA eXchange) is a framework that allows users to serve thousands of fine-tuned models on a single GPU, dramatically reducing the cost of serving without compromising on throughput or latency.

Ludwig - Create custom AI models with ease using Ludwig. Scale, optimize, and experiment effortlessly with declarative configuration and expert-level control.

Activeloop - Activeloop-L0: Your AI Knowledge Agent for accurate, traceable insights from all multimodal enterprise data. Securely in your cloud, beyond RAG.

ktransformers - KTransformers, an open - source project by Tsinghua's KVCache.AI team and QuJing Tech, optimizes large - language model inference. It reduces hardware thresholds, runs 671B - parameter models on 24GB - VRAM single - GPUs, boosts inference speed (up to 286 tokens/s pre - processing, 14 tokens/s generation), and is suitable for personal, enterprise, and academic use.

FastRouter.ai - FastRouter.ai optimizes production AI with smart LLM routing. Unify 100+ models, cut costs, ensure reliability & scale effortlessly with one API.

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