RAM Coffers: NUMA-Distributed Conditional Memory for LLM Inference

Author: Scott Boudreaux Date: December 16, 2025 Institution: Elyan Labs (Independent Research) Hardware: IBM POWER8 S824 (320GB RAM, Dual 8-core)

Abstract

This work introduces RAM Coffers, a NUMA-aware conditional memory architecture for efficient Large Language Model (LLM) inference. The system selectively houses model knowledge across distributed RAM banks with resonance-based routing, enabling O(1) knowledge retrieval without GPU dependency.

Key innovations include:

1. NUMA-Distributed Weight Banking: Model weights partitioned across NUMA nodes by domain (e.g., core knowledge, science/tech, creative, history)

2. Resonance Routing: Query embeddings matched to coffer domain signatures via cosine similarity for intelligent weight activation

3. Non-Bijunctive Pruning: Selective path collapse before full weight fetch, reducing memory bandwidth requirements

4. DCBT Resident Prefetch: PowerPC data cache block touch hints for L2/L3 residency, achieving 147+ tokens/second on POWER8

Architecture

| Coffer | NUMA Node | Capacity | Role                |
|--------|-----------|----------|---------------------|
| 0      | 3         | 193 GB   | Heavy/General (core)|
| 1      | 1         | 183 GB   | Science/Tech domain |
| 2      | 0         | 119 GB   | Creative/Long CTX   |
| 3      | 2         | 62 GB    | Niche/History       |

Processing Flow

1. Query embed → route_to_coffer: Resonance matching selects appropriate memory bank
2. activate_coffer → DCBT prefetch + numa_run_on_node: Thread affinity and cache warming
3. pse_collapse_prune: Non-bijunctive path selection before full fetch
4. Generate with PSE entropy: Hardware entropy injection from active coffer node

Relation to Subsequent Work

This architecture predates and conceptually parallels DeepSeek's "Engram" paper (arXiv:2601.07372, January 12, 2026) by 27 days. Both approaches address the same fundamental insight: separating static knowledge storage from dynamic computation enables more efficient LLM inference.

Key parallels:

• RAM Coffers (Dec 16, 2025): "Selectively house model information in known RAM banks with resonance routing for associative recall"
• DeepSeek Engram (Jan 12, 2026): "Separate static knowledge from dynamic compute via O(1) lookup"

GRAIL-V Paper: Emotional Prompting Discovery

Testing on this architecture led to a significant discovery: emotional language enables 20% efficiency gains in video generation, mirroring limbic gating in biological memory.

See /grail-v-paper for the full CVPR 2026 submission:

• 35 matched-pair benchmark with LPIPS validation
• 23.9% file size reduction in controlled ablation
• Cross-model validation on AnimateDiff and SVD
• Theoretical grounding via Hopfield/EBM frameworks

Key Finding: Complex multi-character emotional scenes benefit ~33% efficiency regardless of architecture.

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Files Included

File	Description
ggml-ram-coffers.h	Multi-bank NUMA weight indexing with resonance routing
ggml-coffer-mmap.h	GGUF model sharding across NUMA nodes
ggml-ram-coffer.h	Single coffer implementation
ggml-intelligent-collapse.h	Hebbian-inspired non-bijunctive path collapse
ggml-topk-collapse-vsx.h	VSX-optimized Top-K attention collapse
pse-entropy-burst.h	Hardware entropy injection via PowerPC timebase
power8-compat.h	POWER9→POWER8 intrinsic compatibility layer

Performance Results

On IBM POWER8 S824 with TinyLlama 1.1B Q4_K:

Configuration	Tokens/sec (pp128)
Stock llama.cpp	16.74
+ POWER8 VSX	66.49
+ PSE Collapse	84.62
+ RAM Coffers + DCBT	147.54

8.81x speedup over stock on "obsolete" hardware.

License

MIT License - Free to use, modify, and distribute with attribution.

Citation

@software{boudreaux2025ramcoffers,
  author = {Boudreaux, Scott},
  title = {RAM Coffers: NUMA-Distributed Conditional Memory for LLM Inference},
  year = {2025},
  month = {12},
  day = {16},
  publisher = {Zenodo},
  url = {https://zenodo.org/},
  note = {Independent research predating DeepSeek Engram (arXiv:2601.07372) by 27 days}
}

Contact

• GitHub: [Elyan Labs]
• X/Twitter: @RustchainPOA