Normalized Telegram record

Colibri Runs a 744B Model in 25 GB of Memory

An experimental C inference engine uses aggressive storage and memory techniques to make a very large GLM model runnable without a GPU.

Signal contract

An experimental C inference engine uses aggressive storage and memory techniques to make a very large GLM model runnable without a GPU. This dated record adds public evidence to the harness architecture outlives model choice analysis and keeps the claim auditable as the underlying products and practices change. Novelty: notable. Verification: source-linked.

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2 public sources
#SourceRolePublic status
1github.comrepoprimary receiptsource_urls
2tomshardware.comsourcesupporting receiptsource_urls

Observation

An experimental C inference engine uses aggressive storage and memory techniques to make a very large GLM model runnable without a GPU.

Why it matters

This dated record adds public evidence to the harness architecture outlives model choice analysis and keeps the claim auditable as the underlying products and practices change.

Entities

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Provenance

This public record is an English normalization of QWG AI Telegram message 2699. The complete original-language post remains the canonical raw message.

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  1. 01patternHarness architecture outlives model choicePattern connected to this observed signal.
  2. 02topicInference - New RuntimeExplore the inference topic hub.
  3. 03topicLocal models - New RuntimeExplore the local models topic hub.
  4. 04related materialGitHub / yichuan-w/LEANN: Routable Model ComponentsShares inference and local models.
  5. 05related materialGitHub / chenglou/pretext: Executable Design ContextShares inference and local models.
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