Unlock the Potential of AI

Explore why Physical AI deployment needs synthetic data at scale with Squeezebits' research and discover how to overcome inference bottlenecks to accelerate Roboost Agent.

Scaling Physical AI requires reliable synthetic data. Learn how RoBoost Agent integrates NVIDIA Cosmos to transform world models into trustworthy data engines for robotics and autonomous driving.

A recap of the Intel® Gaudi® hands-on workshop co-hosted by SqueezeBits and Lablup. AI model compression, fine-tuning, and vLLM serving on Gaudi® hardware with Backend.AI.

Introducing ATOM™-Max, rebellions’ next-generation NPU designed for high-performance AI inference. Learn how its runtime, profiling tools, and PyTorch-native integrations enable developers to run and serve models efficiently without sacrificing usability.

Rebellions and SqueezeBits Co-Host a vLLM Hands-on Workshop: Workshop Highlights, PyTorch Best Practices, Performance Optimization, and Developer First-Hand Tips!

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.

Explore how Intel’s new Gaudi-3 compares to Gaudi-2, NVIDIA A100, and H100. We analyze real-world GEMM efficiency, attention performance, and LLM serving results to uncover what truly matters for AI inference and training workloads.

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.

The guide to LLM guided decoding! This deep-dive benchmark compares XGrammar and LLGuidance on vLLM and SGLang to help you find the optimal setup for generating structured output based on your use case.

In this article, we introduce how to run LLMs efficiently on Apple Silicon with disaggregated inference technique.

Efficient AI Study & Meetup recap: SqueezeBits' community study on AI model compression, featuring paper reviews, participant interviews, and networking from the offline meetup.

Trimming large multilingual vocabularies in Small Language Models (SLM) is a simple, low-risk way to boost efficiency to its limit. It accelerates the model inference significantly while keeping accuracy almost unchanged.

LoRA excels at efficient fine-tuning but suffers at higher ranks due to gradient entanglement. We introduce GraLoRA, which addresses these issues through finer-grained, block-wise updates, significantly enhancing performance and expressivity without overhead. GraLoRA outperforms LoRA across tasks, achieving up to +8.5% improvement in HumanEval+ Pass@1.

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.

SqueezeBits has partnered with Intel to make Gaudi NPUs more usable in practice. We optimized LLMs and diffusion models for Gaudi-2 and created yetter, a generative AI API service.

SqueezeBits at Japan IT Week Spring 2025 in Tokyo: AI model compression demos, OwLite and Fits on Chips introductions, Japan market entry experiences, and team stories from the frontline.

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

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

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

This article discusses inference efficiency when running the FLUX.1 models on Intel Gaudi-2 hardware.

From Edge AI to NVIDIA GTC: Squeezebits team members share firsthand stories from global AI events, including networking insights, technical trends, and conference experiences.

With TensorRT-LLM now open source, we can finally take a deep dive into the secret sauce behind its impressive performance.

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

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

A brief review of the research paper from our team, published at ICML 2024.

This article is about an open-source library for direct conversion of PyTorch models to TensorRT-LLM.

This article explores the rise and fall of ONNX, from its early success as a unifying stasndard for AI frameworks to its gradual shift into a niche tool in the era of PyTorch 2.0.

This article provides a comparative analysis of serving vision-language models on vLLM and TensorRT-LLM.

In this blog series, we thoroughly evaluate Intel's AI accelerator, the Gaudi series, focusing on its performance, features, and usability.

In this blog series, we thoroughly evaluate Intel's AI accelerator, the Gaudi series, focusing on its performance, features, and usability.

This article provides a comparative analysis of automatic prefix caching.

This article provides a comparative analysis of speculative decoding.

This article provides a comparative analysis of multi-LoRA serving capabilities of vLLM and TensorRT-LLM frameworks.

In this blog series, we thoroughly evaluate Intel's AI accelerator, the Gaudi series, focusing on its performance, features, and usability.

This article provides a comparative analysis of different parallelism strategies on vLLM and TensorRT-LLM frameworks.

In this blog series, we thoroughly evaluate Intel's AI accelerator, the Gaudi series, focusing on its performance, features, and usability.

This article provides a comparative analysis of the effects of KV cache quantization on vLLM and TensorRT-LLM frameworks.

This article provides a comparative analysis of the effects of weight-activation quantization on vLLM and TensorRT-LLM frameworks.

This article provides a comparative analysis of the effects of weight-only quantization on vLLM and TensorRT-LLM frameworks.

This article provides a comparative analysis of vLLM and TensorRT-LLM frameworks, focusing on performance with fixed and dynamic datasets.

This article provides a comparative analysis of schedulers in vLLM and TensorRT-LLM frameworks.

This article provides a comparative analysis of vLLM and TensorRT-LLM frameworks with various sampling methods.

This article provides a comparative analysis of vLLM and TensorRT-LLM frameworks, focusing on batching configurations and thoroughly examining the effects of maximum batch size and maximum number of tokens.