Best practices to accelerate inference for large-scale production workloads
AdapTive-LeArning Speculator System (ATLAS): A New Paradigm in LLM Inference via Runtime-Learning Accelerators Together AI Delivers Top Speeds for DeepSeek-R1-0528 Inference on NVIDIA Blackwell RESOURCES / GUIDES November 10, 2025 2 MINUTE READ TL;DR Running LLMs in production requires more than deploying a model. This guide covers the four essential components that determine whether your inference infrastructure scales: optimized kernels that actually saturate GPU hardware, quantization strategies that preserve quality while cutting costs, speculative decoding that reduces latency without accuracy loss, and infrastructure design that handles real-world traffic patterns. We'll walk through how each component works, why it matters, and how Together approaches the technical challenges that most teams hit in production. When a user asks an AI assistant to analyze a 50-page document, answers that take seconds aren't fast enough. This expectation is reshaping infrastructure requirements acr
When a user asks an AI assistant to analyze a 50-page document, answers that take seconds aren't fast enough. This expectation is reshaping infrastructure requirements across the AI industry. The competitive environment and pace of innovation in AI-native products has fundamentally changed what users consider acceptable — and what businesses must deliver to remain competitive. This shift isn't just about user experience — it's economics. Inference costs account for the majority of operational expenses in AI-native applications achieving product-market fit and starting to scale. Higher throughp
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