The described attention design shares KV and uses 128 (64) query heads with a dimension of 512 per head, contrasted with MLA's focus on KV-cache efficiency.
The description presents an attention design using MQA with shared KV: 128 (64) query heads, each with a dimension of 512. By comparison, MLA is described as a low-order design focused on KV-cache efficiency.
The change illustrates an engineering trade-off: reducing pressure on the KV cache may give way to more computation per token and representational capacity. There is no universally best option; outcomes depend on a system's needs and constraints.
To assess an architecture, compare memory use and computation per token under equivalent conditions. Also record task quality and latency, so an improvement in one area does not hide a regression in another.
If you use AI to study or apply these ideas, submit only authorized material and remove personal or confidential data. Check your organization's policies and validate conclusions with your own measurements; this comparison does not show that any platform solves the engineering problem.