Training a model to handle 128K context isn't just running inference on longer sequences — it requires a dedicated mid-training phase because positional encoding, attention entropy, and information retrieval all break in distinct ways beyond the training window.
Training a model to handle 128K context isn't just running inference on longer sequences — it requires a dedicated mid-training phase because positional encoding, attention entropy, and information retrieval all break in distinct ways beyond the training window.
The KV cache is the memory bottleneck in LLM inference. As context length grows, it dominates GPU memory. Here's how quantization, eviction policies, and architectural changes manage it.
The KV cache is the memory bottleneck in LLM inference. As context length grows, it dominates GPU memory. Here's how quantization, eviction policies, and architectural changes manage it.