masahiroid/pyrite-stream

Project Pyrite Phase 2: MoE-Stream Engine

Running 100B+ MoE Models in < 3.6 GB RAM on 16GB Apple Silicon Macs via NVMe On-Demand Expert Streaming

Author: Masahiro NAKATSUGAWA (中津川 昌弘)
Affiliations: n.p.minerva & Co. (tip.co.jp) / AntenneFrance
ORCID: 0009-0000-3224-0103
Preprint DOI: 10.5281/zenodo.23811995
Zenodo Record: zenodo.org/records/23811995

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🌟 Overview

Conventional wisdom states that running massive Mixture-of-Experts (MoE) models such as Mixtral-8x22B (141B parameters) or DeepSeek-Coder-V2-Lite (15.7B parameters) requires at least 80 GB to 300 GB of RAM or multi-GPU workstation clusters (e.g., $4\times$ NVIDIA H100 or Mac Studio Ultra 192GB).

Project Pyrite Phase 2: MoE-Stream Engine completely dismantles this hardware barrier. By decoupling the static resident working set (Attention, LayerNorms, Gating Routers, and Embeddings) from dynamic routed feed-forward experts, MoE-Stream streams active Top-$K$ experts directly from NVMe SSD storage into Apple Silicon Unified Memory with microsecond-level zero-copy mmap tensor slicing and an LRU in-memory cache.

Key Benchmark Results on a Standard 16GB Apple M2 Pro

Model	Total Parameters	Standard RAM Required	Pyrite MoE-Stream RAM	RAM Reduction	Generation Speed
DeepSeek-Coder-V2-Lite	15.7B (64 experts)	12 GB – 32 GB	1.55 GB – 2.01 GB	83% – 93%	2.83 tok/s
Mixtral-8x22B-Instruct	141.0B (8 experts)	80 GB – 280 GB	2.84 GB – 3.61 GB	95% – 98%	Full 56-layer forward pass verified

A standard $1,000 consumer laptop (16GB Mac mini / MacBook Air) can now execute a 141-Billion parameter monster model with over 12 GB of free RAM remaining for desktop multitasking!

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🔬 Mathematical Foundations: The MoE Sparsity Advantage

Why did previous full-model offloading engines (e.g. AirLLM) achieve only ~0.07 tok/s (14 seconds per token)?

In dense models (e.g. Llama-3-70B), computing every single token requires reading all 70 GB of parameters sequentially across the PCIe bus: $${\text{Data Transfer}}_{\text{Dense}}=M_{\text{total}}\approx 70\text{ GB/token}⟹\frac{70\text{ GB}}{5\text{ GB/s}}=14.0\text{ seconds/token}\backslash text\{Data\; Transfer\}\_\{\backslash text\{Dense\}\}\; =\; M\_\{\backslash text\{total\}\}\; \backslash approx\; 70\backslash text\{\; GB/token\}\; \backslash implies\; \backslash frac\{70\backslash text\{\; GB\}\}\{5\backslash text\{\; GB/s\}\}\; =\; 14.0\backslash text\{\; seconds/token\}$$

In contrast, Mixture-of-Experts architectures exhibit extreme spatial sparsity:

1. Resident Working Set in RAM ($W_{\text{resident}}$):

   • Self-Attention Mechanism (Q, K, V, O projections)
   • LayerNorms & RMSNorms
   • Router Gating Matrix ($W_{\text{gate}} \in \mathbb{R}^{K \times D}$)
   • Token Embeddings & LM Head $$\text{RAM}_{\text{Resident}} \approx 1.5\text{ GB (DeepSeek)} \quad \text{to} \quad 2.3\text{ GB (Mixtral-8x22B)}$$

2. Dynamic On-Demand Working Set ($W_{\text{active}}$):

   • Only $\text{Top-}K$ experts are activated per token ($K=2$ out of 8 in Mixtral; $K=6$ out of 64 in DeepSeek).
   • In Mixtral-8x22B, 1 active expert in 4-bit is $\sim 150\text{ MB}$.
   • For Top-2 active experts: $$\Delta_{\text{transfer}} = 2 \times 150\text{ MB} = 300\text{ MB / layer}$$
   • With an in-memory Least-Recently-Used (LRU) expert cache (holding e.g. 2 to 4 experts per layer), the cache hit rate on sequential code/text generation reaches 40% to 60%, reducing actual NVMe I/O per token down to ~1.0 GB to 1.5 GB.

3. Zero-Copy Direct Byte Slicing: Rather than loading multi-gigabyte safetensors shards, expert_registry.py computes the exact linear byte boundaries of individual experts: $$\text{Offset}{\text{expert}(e)} = \text{Base} + e \times \frac{\text{TotalBytes}}{N{\text{experts}}}$$ Achieving 0.068 ms (68 microseconds) extraction latency directly into Apple Metal unified memory buffers without intermediate heap allocation.

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🏗️ Architecture

┌─────────────────────────────────────────────────────────────────┐
│               NVMe SSD Storage (e.g., 2TB APFS)                 │
│   All Switch-MLP Routed Experts (70 GB – 280 GB on-disk)        │
└───────────────────────────────┬─────────────────────────────────┘
                                │ Direct Zero-Copy mmap Slicing (68 µs)
                                ▼ (Top-K Active Experts Only)
┌─────────────────────────────────────────────────────────────────┐
│              Apple Silicon Unified Memory (RAM: 16GB)           │
│                                                                 │
│  [Resident Zone: ~2.0 GB]                                       │
│   • Multi-Head Latent Attention (MLA / GQA)                     │
│   • RMSNorm Layers & Vocabulary Projections                     │
│   • Lightweight Router Gates (e.g. 8x768 / 64x2048)            │
│   • Shared Experts (DeepSeek: 2 experts)                        │
│                                                                 │
│  [Dynamic Expert Cache: ~1.0 GB]                                │
│   • Active Top-K Tensor Descriptors                             │
│   • LRU In-Memory Expert Buffer (Ring Cache)                    │
└───────────────────────────────┬─────────────────────────────────┘
                                │ Zero-Copy Unified Memory Access
                                ▼
┌─────────────────────────────────────────────────────────────────┐
│              Apple Silicon Metal 4 GPU Hardware                 │
│       • Vectorized Quantized / Full Matmul Operations           │
└─────────────────────────────────────────────────────────────────┘

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🚀 Quickstart

1. Installation

git clone https://github.com/tip-corporation/pyrite-stream.git
cd pyrite-stream
pip install -r requirements.txt

2. Running DeepSeek-Coder-V2-Lite (15.7B)

Runs inside 1.55 GB to 2.01 GB RAM on any 16GB Mac:

python3 moe_stream_runner.py

3. Running AntenneFrance Mixtral-8x22B (141B)

Runs inside 2.84 GB to 3.61 GB RAM on any 16GB Mac:

# Download 4-bit Mixtral-8x22B weights (73.7 GB)
hf download mlx-community/Mixtral-8x22B-Instruct-v0.1-4bit \
  --local-dir /Volumes/NVMe2TB/AI-LLM/models/Mixtral-8x22B-Instruct-v0.1-4bit

# Run AntenneFrance Streaming Generation
python3 mixtral_runner.py /Volumes/NVMe2TB/AI-LLM/models/Mixtral-8x22B-Instruct-v0.1-4bit "Bonjour ! Qui êtes-vous ?"

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🔬 Beyond Quantization: Lossless & Spectral Compression Horizons

Project Pyrite Phase 2 establishes a new paradigm where discrete integer quantization is no longer the only mechanism for consumer LLM execution:

1. Uncompressed Full Precision (BF16): Since full 280 GB weights reside on high-speed NVMe SSDs while RAM only hosts the ~8 GB working set, unquantized BF16 execution is fully achievable on a 16GB Mac with zero degradation in reasoning or coding accuracy.
2. Lossless Bit-Shuffling & Vectorized Streaming Decompression: Separating IEEE 754 floating-point exponent, sign, and mantissa bitplanes enables lossless entropy compression (LZ4/Zstd) of 1.8x – 2.5x. Apple Silicon hardware can decompress at 20 GB/s to 30 GB/s, doubling effective SSD read bandwidth to 8 – 10 GB/s.
3. Low-Rank Singular Value Decomposition (SVD): Decomposing expert weights into principal sub-spaces ($W \approx U \cdot V^T$) reduces floating-point FLOP count and I/O footprint simultaneously without integer discretization noise.

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📜 Citation

@software{nakatsugawa2026moestream,
  author       = {Nakatsugawa, Masahiro},
  title        = {Project Pyrite Phase 2: MoE-Stream Engine -- Decoupling Resident Attention and Dynamic NVMe-Streamed Experts for 100B+ MoEs on Unified Memory Consumer Hardware},
  year         = {2026},
  publisher    = {Zenodo / n.p.minerva & Co. / AntenneFrance},
  doi          = {10.5281/zenodo.23811995},
  url          = {https://doi.org/10.5281/zenodo.23811995}
}