AI Signal 492
Open-weight models cheaper than closed at common intelligence scores
Open-weight models achieve comparable performance at roughly one fifth the cost of closed models, enabling self-hosted inference at $0.12 to $0.35 per million output tokens.
Engineers can reduce inference expenses by selecting cost-efficient hardware that supports long-running workloads, extending the useful economic life of older GPUs such as the A100.
Written by elseif from the cluster below · every claim links back to a sourceThe three things worth knowing
Open-weight models cost roughly one fifth of comparable closed models at matched intelligence levels.
Self-hosting on rented hardware yields compute-only costs between $0.12 and $0.35 per million output tokens at full utilization.
The A100 delivers cheaper output than the H100 at spot and multi-year term prices for specific sparse workloads.
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The paper demonstrates that open-weight demand can sustain the economic relevance of older GPU generations by making them cost-competitive for latency-tolerant, compute-intensive tasks.
Adopting open-weight inference requires operators to manage self-hosted deployments and evaluate hardware based on compute-only cost per token rather than peak performance alone.
Because older GPUs retain higher term-price retention than newer families, they can remain viable for multi-year contracts when serving suitable workloads.
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