Building a GGUF Model Loader with Built-in Dequant: A Portfolio Project for Systems Engineers
A practical guide to building a GGUF model loader with dequantization, demonstrating systems skills for engineering roles.
Model-Serving
Distributed Inference: How Modern AI Systems Scale Beyond a Single GPU
How modern AI serving systems split a single model across many accelerators. Covers tensor, pipeline, and expert parallelism, KV-cache sharding, and production patterns from vLLM, TensorRT-LLM, and DeepSeek.
Inference Is Becoming a Distributed-Systems Problem
Once a model fits on one accelerator, inference is mostly GEMMs and kv cache lookups. Once it doesn’t, you inherit every problem distributed systems has spent thirty years failing to solve.
The LLM Inference Roadmap I Wish More Engineers Followed
Most teams treat LLM inference like a REST call. This roadmap walks through the layers that actually decide cost and latency in production — from continuous batching to KV cache layout to speculative decoding.
Mastering Low‑Latency Inference Pipelines with NVIDIA Triton and Distributed Model Serving Consistency
Introduction In production‑grade AI systems, latency is often the decisive factor. A recommendation engine that takes 150 ms to respond may be acceptable for a web page, but the same delay can be catastrophic for an autonomous vehicle or a high‑frequency trading platform. Achieving sub‑10 ms inference while scaling to thousands of requests per second is a non‑trivial engineering challenge that involves careful orchestration of hardware, software, and networking. This article dives deep into how to design, implement, and operate low‑latency inference pipelines using the NVIDIA Triton Inference Server (formerly TensorRT Inference Server) and a distributed model‑serving architecture that guarantees consistency across multiple nodes. We will cover: ...