# 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](<https://devfeed.tech/articles/technical-deep-dive-how-digitalocean-and-amd-delivered-a-2x-production-inference-performance-increase-for-character-ai-19948.md>)

Original publisher: [Read original article](<https://www.digitalocean.com/blog/technical-deep-dive-character-ai-amd>)

Author: Karnik Modi

Published: 2026-01-13T12:30:00Z

Content type: article

Language: en

Sources: [DigitalOcean](<https://devfeed.tech/sources/digitalocean.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Digital Ocean](<https://devfeed.tech/topics/digital-ocean.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [Inference Performance](<https://devfeed.tech/topics/inference-performance.md>), [GPU optimization](<https://devfeed.tech/topics/gpu-optimization.md>), [moe](<https://devfeed.tech/topics/moe.md>), [Kubernetes](<https://devfeed.tech/topics/kubernetes.md>), [model-deployment](<https://devfeed.tech/topics/model-deployment.md>), [PyTorch](<https://devfeed.tech/topics/pytorch.md>), [qwen](<https://devfeed.tech/topics/qwen.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [digitalocean](<https://devfeed.tech/tags/digitalocean.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [inference](<https://devfeed.tech/tags/inference.md>), [inference-performance](<https://devfeed.tech/tags/inference-performance.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [kubernetes](<https://devfeed.tech/tags/kubernetes.md>), [mixture-of-experts](<https://devfeed.tech/tags/mixture-of-experts.md>), [news](<https://devfeed.tech/tags/news.md>), [performance](<https://devfeed.tech/tags/performance.md>), [platforms](<https://devfeed.tech/tags/platforms.md>), [qwen](<https://devfeed.tech/tags/qwen.md>), [technical](<https://devfeed.tech/tags/technical.md>)

## 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,