Bypass CPU RAM for LLM Inference
Run 70B models on consumer GPUs with NVMe-to-GPU direct transfers. Eliminate memory bottlenecks and reduce AI infrastructure costs by 10x. Start today.
Running LLMs is an infrastructure problem before it is a modeling one. These posts cover the operational layer I work in: GPU cluster orchestration, inference optimization from vLLM to raw C and NVMe-direct pipelines, the retrieval and knowledge-base plumbing that feeds models real context, and the MLOps work that keeps them serving in production. Less prompt engineering, more capacity planning.
Run 70B models on consumer GPUs with NVMe-to-GPU direct transfers. Eliminate memory bottlenecks and reduce AI infrastructure costs by 10x. Start today.
Consistency diffusion models achieve 14x faster inference. Learn to optimize production LLM deployments with practical examples and real benchmarks.
Cut through AI code review hype. Learn proven patterns for LLM-based reviews that improve velocity without sacrificing quality. Start smarter today.
Deploy fast, privacy-first AI code completion with local models. Master training, optimization, and production patterns. Start building today.
Master pure C implementations for 10-100x faster AI inference. Production-tested patterns for deploying memory-efficient LLMs at scale. Reduce costs now.
Build production-ready local RAG systems without complex infrastructure. Practical patterns for embeddings, vector search, and retrieval. Start today.
An in-depth technical analysis of how Cloudflare Sandbox SDK enables production-grade LLM code execution with VM-level isolation, streaming feedback, and edge deployment. From architecture to implementation patterns.
Revolutionize API integration testing with RAG systems. Automatically generate, validate, and maintain test suites for microservices at scale, reducing manual effort and catching critical bugs.
Transform documentation with vector search. Build intelligent, auto-updating knowledge bases using vector embeddings, semantic chunking, and LLMs. Reduce maintenance, boost relevance, and empower teams.
Automate open educational resource curation at scale. Build an AI system using LLMs and vector search for validating, categorizing, and enriching free programming books.
Build an intelligent, RAG-powered learning system for DevOps and Cloud Architecture using vector search, automated knowledge extraction, and open-source documentation. Enhance your developer experience with personalized, semantic search.
Deep dive into adversarial attacks on production LLM systems. Learn data poisoning vectors, detection strategies, and hardening techniques for robust AI security at scale.
A comprehensive guide to architecting and deploying GPU-accelerated Kubernetes clusters for large language model inference, from resource scheduling to cost optimization.
Replace alert fatigue with AI-powered incident detection. This guide shows how LLMs, vector similarity, and automated root cause analysis slash MTTR by 70% and eliminate SRE burnout.
Discover how mise unifies polyglot version management and task automation across DevOps, AI/ML, and Platform Engineering for faster onboarding and CI/CD.
A critical analysis of backup strategies, disaster recovery planning, and infrastructure resilience in modern cloud environments.
A practical guide to integrating LLMs into your CI/CD pipelines for automated code reviews, security scanning, and intelligent feedback—with real-world implementation patterns and cost analysis.
A comprehensive guide to architecting production-ready RAG systems, covering vector database selection, chunking strategies, embedding pipelines, and LLM orchestration at scale.
18 posts · all topics →