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Scottcjn / ram-coffers

LLM infrastructure cost reduction via NUMA-aware weight banking: 147 t/s (8.8x stock llama.cpp) on refurbished enterprise POWER8. Self-hosted inference, no cloud APIs. Part of the Proof of Physical AI stack.

Why this job: says: llama.cpp; topics: llama-cpp, llm-inference, edge-ai

What it does

RAM Coffers is LLM infrastructure for inference cost reduction: a NUMA-aware conditional memory architecture that routes model knowledge through physical memory banks, so self-hosted enterprise hardware — refurbished IBM POWER8, Apple Silicon-class machines — serves models locally with no cloud API dependency. It is zero-abstraction LLMOps: the routing layer is the hardware topology itself, measured at 147 tokens/sec (8.8x stock llama.cpp). Verified machines doing this work earn inside the RustChain Proof of Physical AI ecosystem.

Get started

No single install command. Read the steps in the README (opens in new tab)

Maintenance
Active
Last commit 2 days ago
Stars
173
On GitHub
Licence
AGPL-3.0
Share-alike, even hosted
Downloads for
None listed
No release files
Latest version
None yet
No releases
Built with
Python
49% of the code

Is it looked after?

Contributors
3
Last 90 days
Issues
60% closed
14 still open
Pull requests
42 merged
17 waiting
Releases
None
Never published

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More: languages, topics and project record

Languages

  • Python 49%
  • C 40%
  • Metal 3%
  • Shell 3%

Topics

  • llama-cpp
  • llm
  • numa
  • power8
  • ai-inference
  • depin
  • hebbian
  • powerpc
  • proof-of-physical-ai
  • vec-perm
  • llm-inference
  • ppc64le

Project record

  • JobLocal LLMs
  • StatusListed
  • Commits102
  • Forks59
  • Watching30
  • Stars in 28 days+2
  • CreatedJan 19, 2026
  • First seen by usNov 7, 2026
  • CheckedNov 9, 2026

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