LLM-INFERENCE

AI inference innovator d-Matrix announces integration with NVIDIA NVLink Fusion, uniting its Raptor XPUs with NVIDIA MGX rack systems and Spectrum-X networking for ultra-low latency enterprise LLM serving.

A comprehensive performance and cost benchmark of 30B Mixture-of-Experts (MoE) models on AWS SageMaker AI across G5, G6, G6e, and next-gen G7 instances powered by NVIDIA Blackwell GPUs.

An in-depth look at M3-AVM, a preemptive Copy-on-Write execution engine designed to surgically correct LLM reasoning chains in real-time without full reprocessing.

A deep technical analysis of implementing Speculative Decoding in vLLM on AMD ROCm GPUs, covering hardware execution dynamics, benchmark comparisons, code implementations, and optimization strategies for high-throughput LLM serving.

Setting LLM temperature to 0 does not guarantee deterministic output. Discover how GPU dynamic batching, CUDA kernel non-associativity, and concurrency cause up to 30 percent output variance in identical runs.

An in-depth analysis of how Nvidia is securing its market moat by focusing on high-speed networking, interconnects, and data center traffic control rather than just raw GPU compute cycles.

OpenAI introduces Jalapeño, a custom ASIC developed in partnership with Broadcom, designed to eliminate the trade-off between latency and throughput in LLM inference.

A deep dive into the architecture, performance benchmarks, and decision frameworks for choosing between vLLM and Ollama for LLM inference in 2026.

Discover how AirLLM enables large language model inference on consumer hardware by bypassing VRAM limitations through layer-wise loading and disk-to-GPU streaming.

An in-depth technical analysis of reducing inference-time computation overhead in reasoning models like DeepSeek-R1 and OpenAI o1 while maintaining high-quality outputs.

Learn the real arithmetic behind GPU memory for LLMs, focusing on the KV cache, fragmentation, and overhead that most tutorials skip.

Discover a new method to transfer KV caches between different-sized LLMs in the same family using linear mapping, achieving up to 25x speedup over traditional re-prefill.