Inference

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harshuljain13/llm-inference-at-scale: A Practitioner handbook for production llm serving.

 🧠AI  Content type: Code
github.com··Hacker News

DeepSeekV4 1.6T Day 0 to Day 43 Performance Over Time - Huawei, GB300 NVL72, MI355X, B200

 🧠AI  Content type: News

Alignment Collapse Under KV Cache Quantization: Diagnosis and Mitigation

 📄AI Papers  Content type: Academic
arxiv.org·

AI Serving Platform That Adapts to Your Model

 🚀MLOps  Content type: Blog
databricks.com·

A system programmer’s guide to LLM inference

 💬LLMs  Content type: Blog

Inferoa AI harness claimed 90% cache savings. We ran it and measured 97.8%

 📊AI Evals

Running LLM Inference on Kubernetes: What It Actually Takes

 🧠AI  Content type: Blog
fairwinds.com·

Infrastructure Options for Scalable AI Inference

 📊AI Evals  Content type: Blog
mirantis.com·

2x GH200 for LLM inference, Part 2: vLLM, DeepSeek V4 Flash, and MTP

 🚀MLOps  Content type: Blog
dnhkng.github.io·

The Inference Alpha: Maximizing Frontier Models on AMD

 Transformers  Content type: Blog
digitalocean.com·

LLM Inference Engineering Room — Part 3: The Orchestration Layer

 💬LLMs  Content type: Blog

lightmetal: GPU LLM Inference From a Single Java 25 JAR

 🚀MLOps  Content type: Blog
adambien.blog·

NVIDIA Accelerates Google DeepMind’s DiffusionGemma for Local AI

 🧠AI  Content type: Blog
blogs.nvidia.com·

Anatomy of a high-performance EP kernel

 🎯Fine-Tuning  Content type: Blog
fergusfinn.com··Hacker News

From GPU to Token: The 8-Layer Observability Stack for AI Infrastructure

 🔀LoRA  Content type: Blog
jimmysong.io·

The hidden bottleneck in LLM inference and the impact on MLPerf benchmarking

 💬LLMs
edn.com·

PagedAttention vs Traditional KV Cache: How vLLM Reinvented GPU Memory for LLM Inference

 🔀LoRA  Content type: Blog
medium.com
·

DiffusionGemma: The Developer Guide- Google Developers Blog

 🧠AI  Content type: Blog

MoQ GGUFs and GSQ: Low-Bit GGUFs Are About to Get Much Better

 🧠AI  Content type: News  Content type: Blog

Qwen 3.6 27B AutoRound GGUF, need your feedback

 🎯Fine-Tuning

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