LLM Inference

An in-depth look at optimizing agentic workloads using NVIDIA Dynamo, focusing on session-aware inference, prefill management, and performance scaling.

A deep dive into architectural strategies, speculative decoding, KTransformers offloading, and KV cache quantization required to run 125B-parameter MoE LLMs locally at 100 tokens per second on single consumer GPUs.

Discover how integrating Helion—an autotuned high-level kernel DSL—into vLLM's linear backend dramatically boosts LLM inference throughput while maintaining multi-GPU portability.

An in-depth technical analysis of Jagged Flash Attention (JFA) optimized via Tile Language Extensions (TLX) on NVIDIA Blackwell B200 GPUs, paving the way toward FlashAttention-4.

An in-depth architectural guide on KV cache management for distributed LLM inference, exploring multi-tier placement, asynchronous offloading, GPUDirect RDMA migration, and cost-aware eviction policies.

Deep dive into LLM inference acceleration using Speculative Decoding and EAGLE-3 dynamic trees, covering mathematical distribution proofs, architecture evolution, and production vLLM/SGLang configurations.

An in-depth technical analysis of 13 major Amazon SageMaker AI inference launches in 2026 YTD, covering managed endpoints, SageMaker HyperPod Inference, tiered KV caching, disaggregated prefill/decode, and deployment recommendations.

An in-depth technical analysis of FlashAttention-4 utilizing OCP MXFP8 microscaling block formats on NVIDIA Blackwell GPUs, achieving up to 2.85 PF/s forward and 2.0 PF/s backward performance.

An in-depth technical analysis of DeepSeek-v4.1 Flash, detailing its revolutionary KV cache compression algorithms, architectural breakthroughs, benchmarks, and API integration guides.

Learn how to transition deprecated TorchServe workloads to AWS Ray Serve Deep Learning Containers (DLC) on Amazon EKS. This comprehensive guide covers architectural migration, VLM deployment, and enterprise hybrid AI routing.

An in-depth technical analysis of DeepSeek's Multi-Head Latent Attention (MLA) architecture. Learn how low-rank latent compression, matrix absorption, and decoupled RoPE eliminate KV cache bottlenecks and reduce memory footprint by up to 93%.

A technical deep dive into PyTorch Conference China 2026 in Shanghai, exploring PyTorch 2.x compiler advancements, cloud-native distributed training, cross-hardware optimizations, and enterprise inference integration.