ElevenLabs Shares Inference Scaling Tips Without More GPUs
Summary
Angelos Peri of ElevenLabs presented at RAAIS 2026, offering practical advice on how to scale AI inference to serve a growing user base using existing hardware, addressing the challenge of long GPU procurement times. The talk provides clear tips for optimizing inference performance.
Why it matters
For professionals managing AI infrastructure, these tips are crucial for maintaining service quality and scalability amidst hardware supply chain challenges and budget constraints. Optimizing existing resources can significantly impact operational efficiency and cost-effectiveness.
How to implement this in your domain
- 1Review the provided video for specific inference optimization techniques applicable to current AI deployments.
- 2Implement profiling tools to identify bottlenecks in existing inference pipelines.
- 3Explore model quantization, pruning, and distillation to reduce model size and computational requirements.
- 4Optimize batching strategies and memory management for improved GPU utilization.
- 5Investigate serverless inference or dynamic scaling solutions to efficiently manage fluctuating loads.
Who benefits
Key takeaways
- Scaling AI inference without new GPUs is a critical challenge for many organizations.
- ElevenLabs shared practical tips for optimizing existing hardware.
- Techniques focus on maximizing efficiency and serving growing user bases.
- Hardware procurement delays necessitate software-based optimization strategies.
Original post by @nathanbenaich
"Scaling inference with @angelos_peri of @ElevenLabs at @raais 2026: OK, so you can't get more GPUs. Procurement takes months, sometimes years. So how do you serve your scaling user base on the same hardware? Watch this for the clearest inference tips on the street:"
View on XOriginally posted by @nathanbenaich on X · view source
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