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Last month I was running cost projections for an agentic AI platform called OptiMax that we are building at a global technology firm.
The platform orchestrates self-healing agent fleets across AWS Bedrock, GCP Vertex, and Azure OpenAI.
One agent type handles contract analysis — legal documents, compliance checks, clause extraction. Average document length: 47,000 tokens.
We were evaluating microgpt, an open-source transformer implementation marketed as “production-ready long-context inference.”
The benchmark page promised 100,000-token context windows. The memory profiler showed 34 GB VRAM usage on an A100 for a single forward pass. The marketing claimed efficient attention.
We did not deploy microgpt.
Not because of the memory footprint, initially.
Because of a number in the attention mechanism that contradicted the cost structure.
The advertised 100K context window was consuming 14x more tokens than necessary due to one architectural decision that nobody mentions in the documentation.
That decision is in line 127 of the standard multi-head attention implementation.