Exla FLOPs
Exla FLOPs is an on-demand GPU cluster service that allows instant access to distributed training clusters with H100, A100, and other GPUs, offering the lowest pricing for H100s among cloud providers.
https://gpus.exla.ai/?ref=producthunt
Product Information
Updated:Jul 11, 2025
What is Exla FLOPs
Exla FLOPs is a cloud service that enables users to launch distributed GPU clusters for AI/ML workloads within seconds. Born out of the founders' own challenges in scaling AI training beyond 8 GPUs, it was developed to eliminate the complexities of manually connecting nodes across different cloud providers. The service supports various GPU types including H100s and A100s, and uniquely offers instant access to large GPU clusters of 64, 128 or more GPUs without waitlists or commitments.
Key Features of Exla FLOPs
Exla FLOPs is an on-demand GPU cluster service that allows users to instantly launch and scale distributed training clusters with high-performance GPUs like H100s and A100s. The service offers the lowest pricing for H100s among cloud providers and enables users to spin up large GPU clusters (64, 128, or more GPUs) without waitlists or commitments, while providing optimized performance for AI/ML workloads.
Instant Scalability: Ability to immediately spin up large GPU clusters of 64, 128, or more GPUs without waiting lists or commitments
Cost-Effective Pricing: Offers the lowest pricing for H100 GPUs compared to other cloud providers with pay-as-you-go model
Multiple GPU Support: Supports various GPU types including H100, A100, and allows mixing different GPU types in clusters
Distributed Training Optimization: Specialized infrastructure for handling distributed training workloads across multiple GPUs efficiently
Use Cases of Exla FLOPs
Large-Scale AI Training: Enables training of large AI models requiring multiple GPUs with efficient distributed computing capabilities
Research and Development: Supports scientific research and AI model development with flexible access to high-performance computing resources
Model Fine-tuning: Facilitates quick and efficient fine-tuning of existing AI models with scalable GPU resources
Temporary Compute Scaling: Provides burst capacity for organizations needing temporary access to large GPU clusters
Pros
No waitlists or long-term commitments required
Competitive pricing for high-end GPUs
Flexible scaling and GPU mixing options
Cons
Limited to specific GPU types
Requires expertise in distributed training setup
How to Use Exla FLOPs
Install Required Dependencies: Install EXLA and its dependencies including CUDA and cuDNN compatible with your GPU drivers. For precompiled XLA binaries, specify target matching your CUDA version (like cuda12).
Configure GPU Backend: Set XLA_TARGET environment variable to use GPUs and configure the EXLA backend with: Nx.default_backend({EXLA.Backend, device: :cuda})
Initialize GPU Client: Configure EXLA client settings with: Application.put_env(:exla, :clients, cuda: [platform: :cuda, lazy_transfers: :never])
Transfer Data to GPU: Use Nx.backend_transfer() to move tensors from CPU to GPU memory for processing
Define Computation: Create functions with your ML computations and specify EXLA as the compiler with defn_options: [compiler: EXLA]
Execute on GPU: Run your computations which will now execute on the GPU using the EXLA backend for accelerated performance
Monitor Performance: Track GPU metrics like FLOPS, throughput and latency to evaluate performance of your AI workloads
Exla FLOPs FAQs
Exla FLOPs is an on-demand GPU cluster service that allows users to launch distributed training clusters with GPUs like H100, A100 in seconds for AI/ML workloads.
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