Inference Optimization

A deep dive into optimizing multimodal workflows using the LFM2.5-VL-DSpark framework for high-throughput inference.

An in-depth architectural comparison of SGLang and vLLM, evaluating RadixAttention prefix caching, dynamic FSM structured decoding, and empirical 8x H100 benchmarks.

An in-depth technical analysis of Liquid Foundation Models (LFMs) combined with the DSpark optimization engine, achieving 3.2x faster inference speeds compared to standard architectures.

Discover the 'Tiered Decode Law' that predicts LLM inference speed across hardware. Learn how depth-aware quantization allows a 110B model to run on a 2016 desktop with 16GB RAM.

The AI infrastructure landscape is shifting as a massive $400 million chip-backed loan signals a move from training-centric GPU investments to specialized inference hardware.

Naveen Rao's new venture, Un-0, unveils an image-generation system designed to drastically reduce energy consumption and costs, potentially disrupting the current LLM and generative AI infrastructure landscape.

An in-depth technical analysis of Google's Gemma 2 architecture, exploring how hybrid attention, knowledge distillation, and GQA enable 27B models to outperform much larger competitors.

Explore the transition from vLLM V0 to V1, focusing on the architectural shift to support complex Reinforcement Learning workflows like GRPO and PPO with a 'correctness-first' approach.