Technical Blogs

A code-level tour of how the vLLM AFD Plugin separates Attention and FFN: plugin bootstrap, role-specific model construction, per-layer transfers, connector control and data planes, GPU fan-in/fan-out, ubatching, and the evidence-driven roadmap.

Explore a novel optimization technique that separates attention and feed-forward network computations in transformers to achieve better resource utilization, reduced memory overhead, and improved throughput. Learn how component-specific optimizations can unlock significant performance improvements in LLM serving systems.

Comprehensive analysis of DeepSeek model architecture components: Multi-head Latent Attention (MLA), Multi-Token Prediction (MTP), and Mixture of Experts (MoE). Explore the technical foundations that enable DeepSeek's high-performance inference capabilities with detailed architectural insights and implementation analysis.

Deep dive into DeepSeek-MoE inference optimization, exploring expert routing strategies, memory management, and performance tuning for production deployments. Learn advanced techniques for scaling Mixture of Experts models with vLLM and distributed serving architectures.

A comprehensive deep dive into the anatomy of vLLM, exploring its architecture, optimization techniques, and performance characteristics. Learn how vLLM achieves high-throughput serving with PagedAttention, continuous batching, and advanced memory management.

Explore the fundamentals of Physics-Informed Neural Networks (PINNs) and their applications in scientific computing. Learn how to integrate physical laws into neural network architectures for solving complex PDEs. Includes detailed video explanation of core concepts.

A practical guide to optimizing vLLM deployments for production environments. Covers memory management, batching strategies, and hardware acceleration techniques for maximum throughput.