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InferenceFS stores file contents via LLM latent space, needing only filenames as metadata
InferenceFS replaces storing data in π digits with LLM latent space lookup, requiring only filenames as metadata to infer file contents.
Engineers can reduce local disk usage to nearly zero because only filenames are stored, shifting storage cost to API usage. Each read incurs an API call priced at about $0.01 and takes roughly five seconds for a typical file, while writes are not persisted. The approach works only when the LLM service is reachable and filename collisions are avoided.
Written by elseif from the cluster below · every claim links back to a sourceThe three things worth knowing
InferenceFS retrieves file contents by asking a language model what the most likely bytes are for a given filename, keeping only the filename as metadata.
Read performance improved from about five minutes with πfs to roughly five seconds per 400-line file via an API call costing $0.01.
Writes are accepted but discarded, and the system depends on continuous network access to the LLM backend, making it unsuitable for offline or write-heavy workloads.
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InferenceFS shifts the storage model from looking up data in the digits of π to querying the latent space of a large language model. The only metadata kept per file is its filename; the model infers the file’s contents on read. This change reduces local storage requirements to the size of the directory tree alone. The approach mirrors πfs’s idea but replaces the transcendental number with a model trained on the internet.
Adopting InferenceFS means trading disk space for API usage and network latency. Each file read triggers an API call that, according to the source, costs about $0.01 and returns a response in roughly five seconds for a 400-line file. Writes are accepted by the filesystem but are discarded, as the model always provides the final content. Users must manage an API key and bear the ongoing cost proportional to read frequency.
The system stops working when there is no network connection or when the LLM service is unavailable or rate-limited. Filename-based deduplication means that any two files with the same name will receive identical contents, which can lead to unintended data loss. Additionally, the model’s ability to generate accurate binary files depends on its training; unsupported or obscure formats may not be produced correctly. Consequently, InferenceFS is best suited for read-heavy workloads where filename uniqueness is guaranteed and occasional latency and cost are acceptable.
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