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Articles · Rebeauty Atlas

AI

ME Mehedi Hasan

Part 3 — Implementation/Engine-Level: Choosing the Runtime That Gives You These for Free

You now know how to make the model fast (Part 1) and how to build a stable serving layer around it (Part 2). The final question is: which…

Jun 02, 2026 · 8 min read · 0 claps #vllm #sglang #tensorrt

Part 3 — Implementation/Engine-Level: Choosing the Runtime That Gives You These for Free

AI

SH Sharat Nellltla

The GPU Inference Stack: TensorRT, vLLM, Triton, and ONNX Runtime Compared

By Sharat Nellutla — Principal Engineering Manager, Microsoft. All content is original and copyright-protected.

May 13, 2026 · 20 min read · 0 claps #aiinference #vllm #tensorrt

The GPU Inference Stack: TensorRT, vLLM, Triton, and ONNX Runtime Compared

AI SCI

OR Or Zipori

Behind the LLM Math: The Serving Equations That Actually Matter

A lot of LLM serving work looks like this:

May 07, 2026 · 9 min read · 0 claps #llm #llm-serving #ai

Behind the LLM Math: The Serving Equations That Actually Matter

AI

PA Paras Jain

Unlocking LLM Performance: A Deep Dive into Inference Optimization

How to make Large Language Models faster, cheaper, and ready for production.

Apr 03, 2026 · 3 min read · 1 claps #llm-serving #infrastructure #ai-engineering

AI SPT

SH Shashanka B R

How I Built a Self-Scaling AI Inference Cluster from Scratch

From a single model running on a laptop to a distributed system serving hundreds of users — and what I learned along the way.

Mar 27, 2026 · 8 min read · 0 claps paywalled #kv-cache #consistent-hashing #pid-controller

How I Built a Self-Scaling AI Inference Cluster from Scratch

AI

OR Or Zipori

LLM Serving: Why So Hard??

This is the second post in our series. Last time, we looked at the “parallelism salad” of DP, TP, PP, and EP. Today, I want to address the…

Mar 26, 2026 · 5 min read · 55 claps #vllm #ai #llm-serving

AI

SA sarthak babbar

Guide to LLM Inference: Insights to vLLM, TGI, and the Quantization Revolution

In the early days of Generative AI, we were obsessed with parameters. We cheered as models grew from 7B to 70B to 405B. But in 2026, the…

Mar 23, 2026 · 5 min read · 0 claps #vllm #pagedattention #llm-inference

Guide to LLM Inference: Insights to vLLM, TGI, and the Quantization Revolution

TCH AI

GE George Hu

Serve Qwen LLM using KubeRay on Kubernetes

We need serve Qwen LLM to scale

Jan 23, 2026 · 5 min read · 0 claps #llm-serving #kubernetes #ray-serve

Serve Qwen LLM using KubeRay on Kubernetes

AI SPT

DI Dineshkumar Anandan

Beyond the Usual Suspects: Running Vision LLMs with Tool Calling on CPU using mistral.rs

Most discussions around modern LLM serving stop at familiar names: vLLM, SGLang, llama.cpp, TensorRT-LLM, AI Dynamo. They are fast…

Jan 08, 2026 · 3 min read · 14 claps #llm-serving #cpu-utilization #ai-tool-calling

AI

VA varun rao · Devmap

The Shelf Life of GPUs: Do You Actually Need H100s? (The Real Answer)

Or: Why Your A100s Aren’t Obsolete Yet (Probably)

Dec 30, 2025 · 8 min read · 0 claps #h100 #nvidia-h100 #nvidia-gpu

The Shelf Life of GPUs: Do You Actually Need H100s? (The Real Answer)

AI LIF

VA varun rao · Python in Plain English

Quantization Benchmarks: FP16 vs INT8 vs GPTQ vs AWQ — Which One Matters in Practice?

Or: How I Learned to Stop Worrying and Love Smaller Models

Dec 28, 2025 · 6 min read · 14 claps #quantization #fp16 #llm-serving