GPU Optimization

An in-depth technical analysis of unlocking FP and BF16 instruction issue rates on Nvidia 3000 series GPUs, discussing architectural constraints, hardware limitations, and implications for local LLM inference and AI development.

A comprehensive guide on deploying the 763B DeepSeek-V4.1-Flash model on a single 96GB VRAM GPU using the deepseek-one-gpu engine, detailing NUMA affinity, expert offloading, and performance optimization.

An in-depth review of AWS kernel optimizations for fused Mixture-of-Experts models on Amazon SageMaker AI, detailing Swap-AB, Triton FP8 routing, and elimination of JIT recompilation.

A deep dive into the FBTriton kernel design for Table Batched Embedding (TBE) operators, exploring how to scale high-performance recommendation systems.

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 analysis of the hidden costs behind underutilized GPU infrastructure and how developers can leverage API aggregation to maximize ROI.

Discover how building a custom GPU-resident Top-K CUDA kernel eliminates PCIe transfer bottlenecks in Agentic RAG pipelines, delivering microsecond-level retrieval for high-performance LLM applications.

Discover how Hearth, a new Kubernetes Operator, solves the problem of idle GPU costs by enabling scale-to-zero for LLMs like DeepSeek and Qwen across NVIDIA and Ascend hardware.

A deep dive into Google's Gemma 4 release, focusing on GPU requirements, VRAM management, and the revolutionary MoE architecture for local LLM deployment.

A comprehensive guide to optimizing vLLM for production, covering memory budgeting, failure mode diagnostics, and architectural deep dives for stable LLM serving.

Explore why the fundamental difference between compute-bound prefill and memory-bound decode phases necessitates a shift toward disaggregated inference architectures for 2-4x cost savings.