The AI industry’s focus is rapidly shifting from model training to efficient, scalable inference. For AI-native companies, inference costs represent the vast majority of lifetime expenses, shaping unit economics and product viability.
This is where Together AI is doubling down. The company is leveraging foundational research to accelerate LLM inference, announcing its ATLAS system, which uses runtime-learning accelerators to deliver up to 4x faster LLM inference. This adaptive speculative decoding approach learns from live traffic, outperforming static methods.
The Inference Imperative
Jensen Huang, NVIDIA CEO, highlighted that users pay for work, not just information, underscoring the shift towards agentic systems that demand reliable, low-latency inference. This makes inference a complex optimization challenge, balancing latency, throughput, model evolution, and concurrency.
Together AI’s approach tackles these challenges through a compounded stack of research, systems engineering, and hardware expertise.
Research That Ships
The company's research arm has yielded significant advancements, including FlashAttention-4, which offers up to 1.3x faster NVIDIA Blackwell inference performance compared to cuDNN. These innovations are integrated into their production systems swiftly.
