The memory bottleneck for long-context LLMs is now the battlefield. Google, Together AI, and Apple each bet on a different compression strategy. Which one will dominate inference in 2026?
Executive Summary: Three competing KV cache compression methods—TurboQuant, OSCAR, EpiCache—reveal a strategic fork: theoretical generality vs. deployable INT2 vs. conversational memory.
Intro: The core shift – from model size to inference memoryAnalysis: Strategic consequences of each approachBottom Line: Impact for executives – pick by constraint
Strategic Impact: The KV cache bottleneck is the single largest cost driver for long-context LLM inference. Choosing the right compression method today determines whether your deployment is cost-effective or memory-starved. With 1M-token contexts becoming standard, the wrong choice can double your infrastructure spend.
Decoding the signal for leaders. For the full strategic analysis, visit Signal Daily News.
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