LowMemoryLLM

A framework for running large language models on resource-constrained devices with full precision and high speed.

中文文档

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Overview

LowMemoryLLM aims to enable large language models (70B+ parameters) to run on low-end hardware (old smartphones, outdated GPUs, limited memory devices) while maintaining:

• Full precision: Bit-exact output matching FP32/FP16 inference
• High speed: Near-native inference performance
• Minimal memory: Run models that exceed available RAM/VRAM

This requires developing novel algorithms that go beyond existing optimization techniques.

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Problem Statement

Constraint	Current Solutions	Limitation
Memory	Quantization (INT4/INT8)	Precision loss
Memory	Model pruning	Accuracy degradation
Speed	Smaller models	Reduced capability
Speed	Distillation	Not running original model

Our goal: Achieve all three (speed, precision, low memory) simultaneously through new algorithmic approaches.

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Research Directions

1. Neural Subspace Projection

Discover low-dimensional structure in model weights and dynamically synthesize weights on-demand rather than storing them entirely.

• Hypothesis: Model knowledge exists in a compressed manifold
• Approach: Decompose weights into compact basis + input-dependent coefficients
• Target: 10x memory reduction with zero precision loss

2. Input-Adaptive Dynamic Computation

Not all inputs require full model capacity. Automatically determine computation complexity based on input difficulty.

• Simple queries → Minimal computation path
• Complex queries → Full model engagement
• No retraining required for existing models

3. Predictive Asynchronous Pipeline

Predict future computation paths based on attention patterns and overlap I/O with computation completely.

• Analyze attention patterns to predict weight access
• Prefetch weights before they are needed
• Goal: Zero effective memory latency

4. Cross-Request Computation Reuse

Build semantic computation cache to identify similarity between inputs and reuse intermediate computations.

• Cache KV states and intermediate activations
• Semantic similarity matching across requests
• Amortize computation cost over similar queries

5. Lossless Adaptive Precision

Dynamic precision selection per token and per layer with mathematical guarantees for precision preservation.

• Not quantization, but intelligent computation scheduling
• Higher precision for critical computations
• Lower precision where mathematically safe

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Architecture

┌─────────────────────────────────────────────────────────────┐
│                    LowMemoryLLM Framework                    │
├─────────────────────────────────────────────────────────────┤
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────────────┐  │
│  │  Inference  │  │  Training   │  │  Model Adaptation   │  │
│  │   Engine    │  │   Engine    │  │      Engine         │  │
│  └──────┬──────┘  └──────┬──────┘  └──────────┬──────────┘  │
│         │                │                     │             │
│  ┌──────┴────────────────┴─────────────────────┴──────────┐  │
│  │              Core Algorithm Layer                       │  │
│  │  ┌────────────┐ ┌────────────┐ ┌────────────────────┐  │  │
│  │  │ Subspace   │ │  Dynamic   │ │    Predictive      │  │  │
│  │  │ Projection │ │ Computation│ │    Pipeline        │  │  │
│  │  └────────────┘ └────────────┘ └────────────────────┘  │  │
│  └─────────────────────────┬───────────────────────────────┘  │
│                            │                                  │
│  ┌─────────────────────────┴───────────────────────────────┐  │
│  │           Hardware Abstraction Layer (HAL)              │  │
│  │  ┌─────────┐ ┌─────────┐ ┌─────────┐ ┌─────────────┐   │  │
│  │  │   CPU   │ │   GPU   │ │   NPU   │ │   Storage   │   │  │
│  │  │(x86/ARM)│ │(OpenCL) │ │(Future) │ │  (Disk I/O) │   │  │
│  │  └─────────┘ └─────────┘ └─────────┘ └─────────────┘   │  │
│  └─────────────────────────────────────────────────────────┘  │
├─────────────────────────────────────────────────────────────┤
│  ┌─────────────────────────────────────────────────────────┐  │
│  │              Model Integration Layer                     │  │
│  │  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐   │  │
│  │  │ HuggingFace  │  │   Custom     │  │    GGUF      │   │  │
│  │  │   Loader     │  │   Formats    │  │   Support    │   │  │
│  │  └──────────────┘  └──────────────┘  └──────────────┘   │  │
│  └─────────────────────────────────────────────────────────┘  │
└─────────────────────────────────────────────────────────────┘

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Target Performance

Model	Device	Active Memory	Target Speed	Precision
LLaMA-2 7B	Phone (4GB RAM)	<2GB	10+ tokens/s	Lossless
LLaMA-2 70B	Laptop (16GB RAM)	<8GB	2+ tokens/s	Lossless
Mixtral 8x7B	PC (8GB RAM)	<4GB	5+ tokens/s	Lossless
100B+ Models	Desktop (32GB RAM)	<16GB	1+ tokens/s	Lossless

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Implementation Status

Completed

• Hardware Abstraction Layer (HAL)

  • CPU support with AVX2/NEON optimization
  • GPU support via OpenCL
  • Unified memory management interface

• Quantization Infrastructure

  • INT8/INT4/FP16/FP8 quantization
  • Per-channel quantization
  • Dynamic quantization framework
  • Quantization-Aware Training (QAT)

• KV-Cache Management

  • Disk offloading
  • Sliding window rotation
  • Cache compression

• Mixed Precision Training

  • Dynamic loss scaling
  • FP32 weight backup
  • Overflow detection

• Model Integration

  • HuggingFace model loader
  • Tokenizers (BPE, WordPiece, Unigram, SentencePiece)

In Development

• Core Algorithms

  • Neural Subspace Projection
  • Input-Adaptive Dynamic Computation
  • Predictive Asynchronous Pipeline
  • Cross-Request Computation Reuse

• Transformer Implementation

  • Self-Attention mechanism
  • Feed-Forward Networks
  • Layer Normalization
  • Complete inference pipeline

• Memory Management

  • Layer-wise weight streaming
  • Predictive prefetching
  • Memory pool with LRU eviction

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Publication Targets

• Algorithm design: NeurIPS, ICML, ICLR
• Systems optimization: MLSys, OSDI, SOSP

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Installation

git clone https://github.com/2404589803/LowMemoryLLM.git
cd LowMemoryLLM
mkdir build && cd build
cmake ..
make

Requirements

• C compiler with C11 support
• CMake 3.10+
• (Optional) OpenCL for GPU support
• (Optional) AVX2/NEON capable CPU

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Contributing

Contributions welcome in the following areas:

• Algorithm research and implementation
• Performance optimization
• Hardware backend support
• Theoretical analysis

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License

MIT License - see LICENSE file.