vLLM

A comprehensive guide on serving and tuning the Qwen3.8-27B dense FP8 model on a single RTX Pro 6000 GPU using vLLM, including MTP speculative decoding configuration and common production pitfalls.

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.

NVIDIA introduces DGX Spark hardware configured with 64GB of unified memory. Explore how local workstation compute combined with cloud API aggregators creates scalable, cost-effective hybrid AI architectures.

An analysis of how vLLM's shift toward custom kernels impacts torch.compile compatibility and what developers need to know to maintain performance.

An in-depth analysis of how Shopify leverages PyTorch and vLLM to create high-efficiency, low-cost continual learning pipelines for production-grade AI.

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 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 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.

A deep dive into 2026 performance benchmarks, TCO analysis, and architectural blueprints for self-hosting LLM stacks using LiteLLM, vLLM, Ollama, and LocalAI.

Discover the critical distinction between open-source and open-weight LLMs, build custom evaluation frameworks, measure inference latency, and calculate true TCO for production enterprise AI systems.

A comprehensive guide on configuring vLLM, Ray, and RoCE networking to run DeepSeek-V4-Flash across two NVIDIA DGX Spark nodes with a 1,048,576-token context window.

An in-depth technical analysis of vLLM sessions at PyTorch Conference North America 2026, covering disaggregated serving, KV cache optimization, PyTorch integration, MoE scaling, and production deployment.