Author: DigitalOcean - Bewertung: 3x - Views:12
Stop overpaying for AI inference! In this video, we reveal 3 battle-tested strategies for slashing your LLM costs - proven techniques we used to cut Character AI's production inference costs by 50% while serving 20 million monthly active users.
2026 is shaping up to be make-or-break for companies running AI at scale. Unlike traditional software, AI costs explode with usage. The companies that figure out inference efficiency will thrive—the rest will fall behind. We're sharing exactly what worked in production so you can apply these lessons to your own AI infrastructure.
👇 **WHAT YOU'LL LEARN IN THIS VIDEO** 👇
🖥️ **Tip 1: Diversify Your GPU Options** Break free from vendor lock-in! Learn how AMD's ROCm and vLLM support let you tap into more available (and affordable) GPU hardware like the MI325X, instead of fighting over scarce alternatives.
⚡ **Tip 2: Quantization & Configuration** Discover how FP8 quantization can double the users you serve on the same GPU—plus the critical vLLM configuration gotchas that trip up most teams (including the one flag you absolutely cannot skip).
🔧 **Tip 3: Optimize Your Parallelism** Master tensor parallelism (TP) and data parallelism (DP) to find the perfect balance between latency and throughput. We break down why Character AI's hybrid DP2TP4 configuration delivered 91% better throughput.
🎁 **Bonus: Optimized GPU Kernels** Get free performance gains with AMD's AITER (AI Tensor Engine for ROCm) high-performance operators.
📖 Check out our technical deep dive blog post for the full configuration details and benchmarks!
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TIMESTAMPS
0:00 - Introduction
0:49 - Character AI's Challenge
1:57 - Tip 1: Diversify Your GPU Options
3:55 - Tip 2: Quantization and Configuration
6:57 - Tip 3: Optimize Your Parallelism Configuration
10:35 - Bonus Tip: Use Optimized GPU Kernels
10:59 - Results and Wrap Up