Best practices to accelerate inference for large-scale production workloads

Best practices to accelerate inference for large-scale production workloads

Inference costs scale with every user request — and the features users love most burn through margin fastest. For AI-native companies, that's the difference between 80% SaaS margins and the 40–60% reality most teams are navigating.

This ebook breaks down four techniques Together AI uses to optimize production inference: speculative decoding, optimized kernels, near-lossless compression, and hardware acceleration.

What's inside:

Speculative decoding: up to 3x faster generation, no quality change
Optimized kernels: what off-the-shelf frameworks leave on the table
Near-lossless compression: faster inference without model degradation
Hardware acceleration: why chip selection multiplies every other optimization

What stage is your AI project in? *

Click here to consent to the collection and processing of your personal data by The Infotech Beat & Together AI under applicable data privacy laws for fulfillment of 'the offer.

Complete the Form Below

By accessing advertiser content, your details will be used by The Infotech Beat & Together AI for the fulfillment of 'the offer' and follow-up after the fulfillment of the offer.

Together AI may follow up in accordance with their Privacy Policy, which offers more information on the privacy practices and how your personal data will be processed by or on behalf of Together AI.

See the Privacy Policy for more information on the privacy practices of Together AI and how your personal data will be processed by or on behalf of Together AI.