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
