# GPU optimization Techniques for improving software performance on GPUs through parallel execution, memory use, and instruction efficiency. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Co-Designing AI Model Attention for Fast, Interactive Long-Context Inference DevFeed: [Co-Designing AI Model Attention for Fast, Interactive Long-Context Inference]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-07-31T22:16:17Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Inference Performance](), [long-context](), [Artificial Intelligence](), [GPU](), [GPU optimization](), [Large Language Model](), [Nvidia]() Tags: [agentic-ai-generative-ai](), [ai](), [ai-inference](), [data-center-cloud](), [developer-tools-techniques](), [featured](), [gpu](), [inference](), [inference-performance](), [long-context](), [memory](), [model-architecture](), [nvidia](), [performance]() ### AI overview This article examines how co-designing dense attention with GPU execution can improve throughput and interactivity for long-context inference. It analyzes group size, head dimension, sequence length, and the different compute and memory behavior of prefill and decode, including the effects of speculative decoding and prefix caching. ### Source excerpt As agentic and long-context workloads become common, the context lengths increase and attention consumes a larger share of inference time (Figure 1). Because... ## Technical Deep Dive: How DigitalOcean and AMD Delivered a 2x Production Inference Performance Increase for Character.ai DevFeed: [Technical Deep Dive: How DigitalOcean and AMD Delivered a 2x Production Inference Performance Increase for Character.ai]() Original publisher: [Read original article]() Author: Karnik Modi Published: 2026-01-13T12:30:00Z Content type: article Language: en Sources: [DigitalOcean]() Topics: [Artificial Intelligence](), [Digital Ocean](), [GPU](), [Inference](), [Inference Performance](), [GPU optimization](), [moe](), [Kubernetes](), [model-deployment](), [PyTorch](), [qwen]() Tags: [ai](), [digitalocean](), [engineering](), [gpu](), [inference](), [inference-performance](), [infrastructure](), [kubernetes](), [mixture-of-experts](), [news](), [performance](), [platforms](), [qwen](), [technical]() ### AI overview This technical deep dive describes how Character.ai, AMD, and DigitalOcean optimized inference for the Qwen3-235B Instruct FP8 model on AMD Instinct MI300X and MI325X GPUs. The reported optimizations, including parallelization, FP8 execution paths, optimized kernels, topology-aware allocation, and Kubernetes orchestration, produced up to a 2x improvement in production request throughput under stated latency and concurrency constraints. ### Source excerpt Background: How Character.ai worked with DigitalOcean and AMD to optimize performance Character.ai, a leading AI entertainment platform with about 20 million worldwide users, wanted to optimize GPU performance and achieve lower inference costs for its application, which requires low-latency performance at large scale. They approached DigitalOcean and AMD in order to achieve this goal. Working closely together, the Character.ai, AMD, and DigitalOcean teams optimized AMD Instinct™ MI300X and MI325X GPU platforms, resulting in a 2x production inference throughput. In optimized configurations, DigitalOcean delivered high request density per node while maintaining exceptional p90 responsiveness for initial token and sustained token generation throughput, outperforming prior deployments on generic, non-optimized GPU infrastructure. These gains were achieved through platform-level optimizations, including clever parallelization strategies for large Mixture-of-Experts models, efficient FP8 execution paths, optimized kernels with AITER, topology-aware GPU allocation, and production-ready Kubernetes orchestration through DigitalOcean Kubernetes (DOKS). Together, these capabilities allowed Character.ai to scale inference predictably without increasing operational burden. In this post, we will explore the specific orchestration and tuning strategies that made these gains possible. Technical deep dive overview Character.ai leverages multiple models like Qwen, Mistral and more to power their applications. This document is focused on how we optimized the Qwen3-235B Instruct FP8 model on a cluster of DigitalOcean featuring AMD Instinct GPUs. This workload was migrated from a generic, non-optimized setup on other providers to AMD Instinct™ MI325X platform on DigitalOcean, and following the outlined optimizations we were able to achieve up to a 2x improvement in request throughput (QPS) under strict latency and concurrency constraints. The Character.ai team has a demanding workload, ## How LinkedIn Reduced GPU Memory Usage by 60% for LLM Training DevFeed: [How LinkedIn Reduced GPU Memory Usage by 60% for LLM Training]() Original publisher: [Read original article]() Author: Richard Oliver Bray Published: 2025-01-16T15:19:14Z Content type: tutorial Language: en Sources: [Hacking Scale by Better Stack]() Topics: [Large Language Model](), [GPU optimization](), [GPU](), [Library](), [LLM Techniques]() Tags: [gpu](), [library](), [llm](), [memory](), [performance](), [techniques](), [training]() ### AI overview This article explains how LinkedIn improved the efficiency of training large language models. It describes the resource demands of pre-training and reports that LinkedIn reduced GPU memory usage by 60% by applying selected GPU performance techniques in a library. ### Source excerpt LinkedIn hand-picked the best GPU performance techniques and put them in a library