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Winning both speed and quality: How Yetter deals with diffusion models

Winning both speed and quality: How Yetter deals with diffusion models

Explore how the Yetter Inference Engine overcomes the limitations of step caching and model distillation for diffusion models. We analyze latency, diversity, quality, and negative-prompt handling to reveal what truly matters for scalable, real-time image generation.

Yetter, the GenAI API service: AI Optimization, Out of the Box

Yetter, the GenAI API service: AI Optimization, Out of the Box

Meet 'Yetter': the generative AI API service built for speed, efficiency, and scalability. Powered by our optimization inference engine, it delivers reliable image, video, and future LLM services at a fraction of the cost.

OwLite Meets Qualcomm Neural Network: Unlocking On-Device AI Performance

OwLite Meets Qualcomm Neural Network: Unlocking On-Device AI Performance

At SqueezeBits we have been empowering developers to efficiently deploy complex AI models while minimizing performance trade-offs with OwLite toolkit. With OwLite v2.5, we're excited to announce official support for Qualcomm Neural Network (QNN) through seamless integration with Qualcomm AI Hub.

How to Quantize Transformer-based model for TensorRT Deployment

How to Quantize Transformer-based model for TensorRT Deployment

This article describes the experimental results of quantized Vision Transformer model and its variants with OwLite.

How to Quantize YOLO models with OwLite

How to Quantize YOLO models with OwLite

This article describes the experimental results of quantized YOLO models with OwLite.

Winning both speed and quality: How Yetter deals with diffusion models

Winning both speed and quality: How Yetter deals with diffusion models

Explore how the Yetter Inference Engine overcomes the limitations of step caching and model distillation for diffusion models. We analyze latency, diversity, quality, and negative-prompt handling to reveal what truly matters for scalable, real-time image generation.

Yetter, the GenAI API service: AI Optimization, Out of the Box

Yetter, the GenAI API service: AI Optimization, Out of the Box

Meet 'Yetter': the generative AI API service built for speed, efficiency, and scalability. Powered by our optimization inference engine, it delivers reliable image, video, and future LLM services at a fraction of the cost.

OwLite Meets Qualcomm Neural Network: Unlocking On-Device AI Performance

OwLite Meets Qualcomm Neural Network: Unlocking On-Device AI Performance

At SqueezeBits we have been empowering developers to efficiently deploy complex AI models while minimizing performance trade-offs with OwLite toolkit. With OwLite v2.5, we're excited to announce official support for Qualcomm Neural Network (QNN) through seamless integration with Qualcomm AI Hub.

How to Quantize Transformer-based model for TensorRT Deployment

How to Quantize Transformer-based model for TensorRT Deployment

This article describes the experimental results of quantized Vision Transformer model and its variants with OwLite.

How to Quantize YOLO models with OwLite

How to Quantize YOLO models with OwLite

This article describes the experimental results of quantized YOLO models with OwLite.

OwLite: No More Compromising on AI Performance After Quantization

OwLite: No More Compromising on AI Performance After Quantization

Discover how OwLite simplifies AI model optimization with seamless integration and secure architecture.

When Should I Use Fits on Chips?

When Should I Use Fits on Chips?

This article describes when to use Fits on Chips toolkit with specific use cases.

Fits on Chips: Saving LLM Costs Became Easier Than Ever

Fits on Chips: Saving LLM Costs Became Easier Than Ever

This article introduces Fits on Chips, an LLMOps toolkit for performance evaluation.

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