Long Contexts Flip LLMs from Compute Champs to Memory Bottlenecks
Everyone chased million-token dreams. Reality? Inference latency explodes, turning hype into hardware headaches. This shift rewrites LLM economics.
β‘ Key Takeaways
- LLM inference flips memory-bound past 8-32k tokens due to quadratic KV cache growth.
- Latency spikes 10-20x at 128k, gutting throughput and raising costs.
- Fixes like FlashAttention help, but sparse models or new hardware needed for true long-context scaling.
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Originally reported by Towards AI
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