Product
Open-Source
Optimization Engine
Our compression engine is made by researchers for engineers. It is designed to make your life easier. With just two lines of code, no need for extensive re-engineering.
Open-Source
Optimization Engine
Our compression engine is made by researchers for engineers. It is designed to make your life easier. With just two lines of code, no need for extensive re-engineering.
Open-Source
Optimization Engine
Our compression engine is made by researchers for engineers. It is designed to make your life easier. With just two lines of code, no need for extensive re-engineering.
Proven Expertise
270+ published papers at NeurIPS, ICML, ICLR...
Universal Compatibility
Any method, any hardware.
Flexible Approach
A single technique or combination of methods.
Hardware Agnostic
All chips - cloud, on-prem, or at the edge.
Proven Expertise
270+ published papers at NeurIPS, ICML, ICLR...
Universal Compatibility
Deep expertise in AI efficiency & research .
Flexible Approach
A single technique or combination of methods.
Hardware Agnostic
All chips - cloud, on-prem, or at the edge.
Proven Expertise
270+ published papers at NeurIPS, ICML, ICLR...
Universal Compatibility
Any method, any hardware.
Flexible Approach
A single technique or combination of methods.
Hardware Agnostic
All chips - cloud, on-prem, or at the edge.
Pruning
Pruning eliminates unnecessary weights and neurons in deep learning models, reducing size and computational demands while retaining model performance. It helps simplify your models for faster inference and easier deployment without compromising accuracy.
Structured Pruning
Removes full neurons or filters in a layer, optimizing resource usage without altering model architecture.
Semi-Structured Pruning
Strikes a balance between structured and unstructured pruning by selectively removing groups of weights or small sub-tensors with a specific pattern within a layer.
Unstructured Pruning
Reduces individual weights based on importance metrics, offering fine-grained control over parameter reduction.
Dynamic Pruning
Continuously adjusts parameters during runtime to maintain optimal performance for various use cases.
Pruning
Pruning eliminates unnecessary weights and neurons in deep learning models, reducing size and computational demands while retaining model performance. It helps simplify your models for faster inference and easier deployment without compromising accuracy.
Structured Pruning
Removes full neurons or filters in a layer, optimizing resource usage without altering model architecture.
Semi-Structured Pruning
Strikes a balance between structured and unstructured pruning by selectively removing groups of weights or small sub-tensors with a specific pattern within a layer.
Unstructured Pruning
Reduces individual weights based on importance metrics, offering fine-grained control over parameter reduction.
Dynamic Pruning
Continuously adjusts parameters during runtime to maintain optimal performance for various use cases.
Pruning
Pruning eliminates unnecessary weights and neurons in deep learning models, reducing size and computational demands while retaining model performance. It helps simplify your models for faster inference and easier deployment without compromising accuracy.
Structured Pruning
Removes full neurons or filters in a layer, optimizing resource usage without altering model architecture.
Semi-Structured Pruning
Strikes a balance between structured and unstructured pruning by selectively removing groups of weights or small sub-tensors with a specific pattern within a layer.
Unstructured Pruning
Reduces individual weights based on importance metrics, offering fine-grained control over parameter reduction.
Dynamic Pruning
Continuously adjusts parameters during runtime to maintain optimal performance for various use cases.
Quantization
Quantization reduces the precision of model weights and activations, optimizing models to use fewer computational resources and memory while maintaining accuracy. This technique is particularly valuable for inference speed-ups in resource-constrained environments.
Post-Training Quantization
Simplifies pre-trained models by lowering precision without retraining, making them more efficient in execution.
Quantization-Aware Training
Integrates quantization during training to preserve performance, even at reduced precision.
Mixed Precision
Combines different levels of precision within a model, balancing speed and accuracy for specific hardware configurations.
Quantization
Quantization reduces the precision of model weights and activations, optimizing models to use fewer computational resources and memory while maintaining accuracy. This technique is particularly valuable for inference speed-ups in resource-constrained environments.
Post-Training Quantization
Simplifies pre-trained models by lowering precision without retraining, making them more efficient in execution.
Quantization-Aware Training
Integrates quantization during training to preserve performance, even at reduced precision.
Mixed Precision
Combines different levels of precision within a model, balancing speed and accuracy for specific hardware configurations.
Quantization
Quantization reduces the precision of model weights and activations, optimizing models to use fewer computational resources and memory while maintaining accuracy. This technique is particularly valuable for inference speed-ups in resource-constrained environments.
Post-Training Quantization
Simplifies pre-trained models by lowering precision without retraining, making them more efficient in execution.
Quantization-Aware Training
Integrates quantization during training to preserve performance, even at reduced precision.
Mixed Precision
Combines different levels of precision within a model, balancing speed and accuracy for specific hardware configurations.
Compilation
Compilation translates high-level model representations into optimized, low-level instructions tailored for specific hardware environments. This ensures that your models run as efficiently as possible, maximizing both speed and resource use.
Kernel Optimization
Merges multiple operations into a single, efficient kernel, minimizing overhead and execution time.
Graph Optimization
Reorganizes the computational graph to remove redundancies and optimize memory use.
Hardware-Specific Compilation
Customizes models for execution on specific hardware, whether that’s CPUs, GPUs, or accelerators like TPUs.
Compilation
Compilation translates high-level model representations into optimized, low-level instructions tailored for specific hardware environments. This ensures that your models run as efficiently as possible, maximizing both speed and resource use.
Kernel Optimization
Merges multiple operations into a single, efficient kernel, minimizing overhead and execution time.
Graph Optimization
Reorganizes the computational graph to remove redundancies and optimize memory use.
Hardware-Specific Compilation
Customizes models for execution on specific hardware, whether that’s CPUs, GPUs, or accelerators like TPUs.
Compilation
Compilation translates high-level model representations into optimized, low-level instructions tailored for specific hardware environments. This ensures that your models run as efficiently as possible, maximizing both speed and resource use.
Kernel Optimization
Merges multiple operations into a single, efficient kernel, minimizing overhead and execution time.
Graph Optimization
Reorganizes the computational graph to remove redundancies and optimize memory use.
Hardware-Specific Compilation
Customizes models for execution on specific hardware, whether that’s CPUs, GPUs, or accelerators like TPUs.
Caching
Caching accelerates model performance by storing and reusing previously computed results, reducing redundant computations. This is particularly effective in high-traffic environments where the same data or model outputs are frequently accessed.
Feature Caching
Saves intermediate computations to avoid recalculating common feature maps during inference.
Model Caching
Retains model instances in memory for faster reuse in repeated inference tasks.
Distributed Caching
Extends caching capabilities across distributed systems, improving scalability for large-scale deployments.
Caching
Caching accelerates model performance by storing and reusing previously computed results, reducing redundant computations. This is particularly effective in high-traffic environments where the same data or model outputs are frequently accessed.
Feature Caching
Saves intermediate computations to avoid recalculating common feature maps during inference.
Model Caching
Retains model instances in memory for faster reuse in repeated inference tasks.
Distributed Caching
Extends caching capabilities across distributed systems, improving scalability for large-scale deployments.
Caching
Caching accelerates model performance by storing and reusing previously computed results, reducing redundant computations. This is particularly effective in high-traffic environments where the same data or model outputs are frequently accessed.
Feature Caching
Saves intermediate computations to avoid recalculating common feature maps during inference.
Model Caching
Retains model instances in memory for faster reuse in repeated inference tasks.
Distributed Caching
Extends caching capabilities across distributed systems, improving scalability for large-scale deployments.
Batching
Batching groups multiple inputs together for simultaneous processing, improving overall throughput. By optimizing how data is batched and processed, your models can handle more tasks in less time, especially in inference-heavy environments.
Dynamic Batching
Automatically adjusts batch sizes based on workload, optimizing resource use during varying traffic loads.
Asynchronous Batching
Processes batches concurrently, reducing idle time and improving throughput for real-time applications.
Distributed Batching
Manages batching across multiple nodes for large-scale systems, ensuring efficient resource utilization in cloud or distributed environments.
Batching
Batching groups multiple inputs together for simultaneous processing, improving overall throughput. By optimizing how data is batched and processed, your models can handle more tasks in less time, especially in inference-heavy environments.
Dynamic Batching
Automatically adjusts batch sizes based on workload, optimizing resource use during varying traffic loads.
Asynchronous Batching
Processes batches concurrently, reducing idle time and improving throughput for real-time applications.
Distributed Batching
Manages batching across multiple nodes for large-scale systems, ensuring efficient resource utilization in cloud or distributed environments.
Batching
Batching groups multiple inputs together for simultaneous processing, improving overall throughput. By optimizing how data is batched and processed, your models can handle more tasks in less time, especially in inference-heavy environments.
Dynamic Batching
Automatically adjusts batch sizes based on workload, optimizing resource use during varying traffic loads.
Asynchronous Batching
Processes batches concurrently, reducing idle time and improving throughput for real-time applications.
Distributed Batching
Manages batching across multiple nodes for large-scale systems, ensuring efficient resource utilization in cloud or distributed environments.
Introducing Pruna Enterprise
Inefficient models waste resources, drive up costs, and harm the environment. Optimize with us—saving on all fronts while making a difference.
Introducing Pruna Enterprise
Inefficient models waste resources, drive up costs, and harm the environment. Optimize with us—saving on all fronts while making a difference.
Introducing Pruna Enterprise
Inefficient models waste resources, drive up costs, and harm the environment. Optimize with us—saving on all fronts while making a difference.
© 2024 Pruna AI - Built with Pretzels & Croissants 🥨 🥐
© 2024 Pruna AI - Built with Pretzels & Croissants 🥨 🥐
© 2024 Pruna AI - Built with Pretzels & Croissants