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MoE-Lightning Integration

Overview

This document outlines the plan for integrating MoE-Lightning into the AI-OS project to enable high-throughput Mixture-of-Experts (MoE) inference on memory-constrained GPUs. MoE-Lightning is a state-of-the-art system that achieves up to 10.3× higher throughput than existing solutions through novel CPU-GPU-I/O pipeline scheduling and a Hierarchical Roofline Model for performance optimization.

Paper Reference: MoE-Lightning: High-Throughput MoE Inference on Memory-constrained GPUs

Created: November 8, 2025 Status: Planning Phase Priority: High Complexity: High

Table of Contents

  1. Motivation
  2. Technical Background
  3. Core Components
  4. Integration Architecture
  5. Implementation Phases
  6. Technical Requirements
  7. Performance Targets
  8. Risk Assessment
  9. Testing Strategy
  10. Future Enhancements
  11. References

Motivation

Problem Statement

AI-OS currently faces significant challenges when running large Mixture-of-Experts models on memory-constrained hardware:

  1. Limited GPU Memory: Models like Mixtral 8x7B (~47GB) and Mixtral 8x22B (>256GB) cannot fit entirely in consumer-grade GPU memory (typically 16-24GB)
  2. Poor Resource Utilization: Existing offloading solutions (DeepSpeed-Inference, FlexGen) suffer from:
  3. GPU idle time while waiting for data transfers
  4. Inefficient overlap of computation and I/O
  5. Suboptimal batch size selection
  6. Accessibility Gap: High-end GPUs are expensive and unavailable to most users who want to experiment with large MoE models

Benefits of Integration

  1. Dramatic Throughput Improvements: 3.5-10.3× higher throughput on single GPU compared to existing systems
  2. Memory Efficiency: Run models with 2-3× less CPU memory while maintaining peak throughput
  3. Better Hardware Utilization: Efficiently utilize CPU, GPU, and memory bandwidth simultaneously
  4. Democratization: Enable more users to run large MoE models on consumer hardware
  5. Super-linear Scaling: 2.77-3.38× throughput improvement when scaling from 2 to 4 GPUs
  6. Compatibility: Works with popular MoE models (Mixtral 8x7B, Mixtral 8x22B, DBRX)

Technical Background

Mixture of Experts (MoE) Architecture

MoE models use a gating mechanism to route inputs to specialized expert sub-networks: - Only a subset of experts are activated per token (sparse activation) - Provides better parameter efficiency than dense models - Significantly larger memory footprint due to multiple expert FFNs - Example: Mixtral 8x7B has 8 experts per layer, activates top-2

Key Innovations in MoE-Lightning

1. CGOPipe (CPU-GPU-I/O Pipeline Schedule)

Problem: Traditional approaches transfer data sequentially, causing bubbles in the pipeline where resources sit idle.

Solution: Fine-grained pipelining that overlaps: - GPU computation (post-attention, pre-attention tasks) - CPU computation (attention with softmax) - I/O transfers (weights, hidden states, KV cache)

Key Technique - Weights Paging: - Chunk weights into n pages (where n = number of micro-batches) - Interleave weight transfers with intermediate result transfers - Enable parallel transfers in opposite directions (CPU→GPU and GPU→CPU)

2. HRM (Hierarchical Roofline Model)

Problem: Existing performance models don't account for heterogeneous resources and cross-level data movement.

Solution: Extended Roofline Model with multiple memory hierarchies:

Performance Equation: 

P_x^i = min(P_peak^i, B_peak^i × I_x^i, B_peak^(j,i) × I_x^j)

Where: - P_peak^i: Peak compute at level i (GPU/CPU) - B_peak^i: Memory bandwidth at level i - B_peak^(j,i): Bandwidth from level j to level i (e.g., CPU to GPU) - I_x^i: Operational intensity of computation x at level i

Turning Points: The model identifies critical operational intensities that determine: - When to perform computation on CPU vs GPU - When the system is GPU memory-bound vs CPU-GPU bandwidth-bound - Optimal batch size and micro-batch size combinations

Balance Point: 

B_peak^i × I_x^i = B_peak^(j,i) × I_x^j
This represents the optimal configuration where all resources are fully utilized.

3. Tensor Parallelism

Unlike pipeline parallelism (scales with model depth), MoE-Lightning uses tensor parallelism: - Scales with layer size - Increases total GPU memory capacity linearly - Increases GPU memory bandwidth linearly - Achieves super-linear scaling in practice (3.38× with 4 GPUs vs 2 GPUs)

Performance Analysis Insights

Attention Block

  • Operational intensity independent of batch size
  • For context length 512 on L4 GPU: CPU attention is 3-4× faster than KV cache transfer
  • CPU attention becomes bottleneck at large batch sizes and long context lengths

MoE FFN Block

  • Operational intensity increases with batch size (more computation per weight access)
  • Memory-bound in decode stage for typical micro-batch sizes
  • Benefits most from weight offloading strategies

Core Components

Component 1: CGOPipe Scheduler

Purpose: Implement fine-grained CPU-GPU-I/O pipeline scheduling

Key Features: 

# Pseudo-code for CGOPipe execution order
for decode_step in range(generation_length):
    # Prologue (first 2 micro-batches)
    for j in [1, 2]:
        PreAttn(layer=1, microbatch=j)
        OffloadQKV(layer=1, microbatch=j)
        CPUAttn(layer=1, microbatch=j)
        WeightsCPUtoPin(layer=2, microbatch=j)

    # Main pipeline (steady state)
    for layer in range(1, num_layers):
        for microbatch in range(1, num_microbatches + 1):
            # Parallel execution
            PostAttn(layer, microbatch)        # GPU
            PreAttn(layer, microbatch+1)       # GPU
            CPUAttn(layer, microbatch+1)       # CPU
            WeightsPinToGPU(layer+1, page)     # I/O

Implementation Requirements: - Asynchronous task execution with CUDA streams - Synchronization primitives for data dependencies - Weight paging system with page table management - Dual-buffer for weight transfers (2× layer weight size)

Component 2: HRM Performance Model

Purpose: Find optimal execution policies based on hardware, model, and workload

Policy Search Space: 

@dataclass
class InferencePolicy:
    N: int              # Batch size
    μ: int              # Micro-batch size
    A_g: bool           # Perform attention on GPU?
    F_g: bool           # Perform FFN on GPU?
    r_w: float          # Ratio of weights on GPU (0-1)
    r_c: float          # Ratio of KV cache on GPU (0-1)

Optimization Target: 

def optimize_policy(hardware, model, workload):
    """
    Minimize per-layer latency while satisfying memory constraints

    T(M, H, W, P) = max(comm_cpu_to_gpu, T_cpu, T_gpu)

    where:
    - T_cpu = T_attn_cpu + T_ffn_cpu
    - T_gpu = T_attn_gpu + T_ffn_gpu
    - comm_cpu_to_gpu = bytes_transferred / bandwidth_cpu_to_gpu

    Subject to:
    - GPU_memory_used ≤ GPU_memory_capacity
    - CPU_memory_used ≤ CPU_memory_capacity
    """
    # Use MILP solver for policy search
    # Takes <1 minute for offline optimization

Model Configuration: - Hardware: GPU/CPU memory, bandwidth, FLOPS - Model: Layers, dimensions, expert count, data types - Workload: Average prompt length, generation length

Component 3: Memory Management System

Weight Paging: 

class WeightPagingManager:
    """
    Manages paged weight transfers with double buffering
    """
    def __init__(self, layer_weight_size, num_pages):
        # Allocate 2× layer weight buffer on GPU
        self.weight_buffer_size = 2 * layer_weight_size
        self.num_pages = num_pages
        self.page_size = layer_weight_size / num_pages

        # Page table for MoE expert routing
        self.page_table = {}

    def transfer_page(self, layer, page_id, stream):
        # CPU DRAM → CPU Pinned Memory
        self.copy_to_pinned_async(layer, page_id, stream)

        # CPU Pinned Memory → GPU (overlapped)
        self.copy_to_gpu_async(layer, page_id, stream)

KV Cache Management: - Store all KV cache on CPU after prefill stage - Transfer to GPU only for attention computation (if GPU attention is selected) - For CPU attention: keep on CPU, pass hidden states instead

Component 4: CPU Attention Kernels

Purpose: High-performance Grouped Query Attention on CPU

Implementation: - Based on Intel MKL library optimizations - SIMD vectorization for matrix operations - Cache-friendly memory access patterns - Multi-threaded execution

Performance Characteristics: - 3-4× faster than KV cache transfer on typical hardware - Becomes bottleneck at very large batch sizes (>256) or long contexts (>2048)

Component 5: Request Batching System

Purpose: Handle variable-length prompts efficiently without padding

Algorithm: 

def balanced_batching(requests, num_microbatches, target_batch_size):
    """
    Distribute requests across micro-batches to balance token counts

    Returns micro-batches with roughly equal total tokens
    """
    # Sort requests by length (descending)
    sorted_requests = sorted(requests, key=lambda r: r.length, reverse=True)

    microbatches = [[] for _ in range(num_microbatches)]
    token_counts = [0] * num_microbatches

    # Greedy assignment to micro-batch with fewest tokens
    for request in sorted_requests:
        min_idx = token_counts.index(min(token_counts))
        microbatches[min_idx].append(request)
        token_counts[min_idx] += request.length

    return microbatches

Integration Architecture

System Architecture Diagram

┌─────────────────────────────────────────────────────────────┐
│                       AI-OS Core                             │
├─────────────────────────────────────────────────────────────┤
│                                                               │
│  ┌───────────────────────────────────────────────────────┐  │
│  │         MoE-Lightning Integration Layer                │  │
│  ├───────────────────────────────────────────────────────┤  │
│  │                                                         │  │
│  │  ┌──────────────┐  ┌──────────────┐  ┌─────────────┐ │  │
│  │  │   HRM Model  │  │  CGOPipe     │  │  Policy     │ │  │
│  │  │  Optimizer   │←→│  Scheduler   │←→│  Cache      │ │  │
│  │  └──────────────┘  └──────────────┘  └─────────────┘ │  │
│  │         ↓                  ↓                           │  │
│  │  ┌──────────────┐  ┌──────────────┐  ┌─────────────┐ │  │
│  │  │   Weight     │  │  Request     │  │  CPU Attn   │ │  │
│  │  │   Paging     │  │  Batching    │  │  Kernels    │ │  │
│  │  └──────────────┘  └──────────────┘  └─────────────┘ │  │
│  └───────────────────────────────────────────────────────┘  │
│                           ↕                                   │
│  ┌───────────────────────────────────────────────────────┐  │
│  │         Existing AI-OS Components                      │  │
│  ├───────────────────────────────────────────────────────┤  │
│  │  • HuggingFace Model Loading                          │  │
│  │  • vLLM/SGLang Integration                            │  │
│  │  • Memory Estimation System                           │  │
│  │  • Expert Manager                                     │  │
│  └───────────────────────────────────────────────────────┘  │
│                           ↕                                   │
│  ┌───────────────────────────────────────────────────────┐  │
│  │              Hardware Abstraction Layer               │  │
│  ├───────────────────────────────────────────────────────┤  │
│  │  GPU (CUDA)  │  CPU (MKL)  │  Memory (Pinned/Paged)  │  │
│  └───────────────────────────────────────────────────────┘  │
└─────────────────────────────────────────────────────────────┘

Integration Points

1. Model Loading Layer

  • Extend existing HuggingFace model loading in aios/brain.py
  • Detect MoE architecture (Mixtral, DBRX, DeepSeek-MoE)
  • Configure weight storage strategy (GPU/CPU split based on policy)

2. Inference Engine Layer

  • New module: aios/inference/moe_lightning/
  • Interface with existing inference systems (vLLM, SGLang)
  • Provide unified API for MoE model inference

3. Memory Management Layer

  • Integrate with existing memory estimation (artifacts/memory_estimation/)
  • Extend GPU memory tracking
  • Add CPU memory and pinned memory tracking

4. CLI Integration

  • Add commands to aios/cli/aios.py: 
    aios infer-moe --model mixtral-8x7b --strategy moe-lightning --batch-size auto
aios profile-moe --model mixtral-8x7b --hardware-config gpu_config.yaml

5. Configuration Layer

  • New config file: config/moe_lightning.yaml
  • Hardware profiles for common GPU configurations (T4, L4, A100, etc.)
  • Model-specific optimization profiles

Implementation Phases

Phase 1: Foundation & Research (Weeks 1-3)

Objectives: - Deep dive into MoE-Lightning paper and codebase - Set up development environment - Implement basic prototype

Tasks: 1. Code Analysis - Study MoE-Lightning reference implementation (if available) - Analyze vLLM and SGLang MoE support - Document API interfaces and extension points

  1. Prototype Development
  2. Implement basic HRM model for simple 2-level hierarchy (CPU/GPU)
  3. Create simplified weight paging mechanism

  4. Benchmark baseline performance with existing systems

  5. Environment Setup

  6. Configure test environments with various GPU configs (T4, L4)
  7. Set up profiling tools (NVIDIA Nsight, Intel VTune)
  8. Prepare test datasets (MTBench, HELM benchmarks)

Deliverables: - Technical design document with architecture diagrams - Proof-of-concept code demonstrating HRM policy optimization - Baseline performance benchmarks

Success Criteria: - HRM model correctly predicts bottleneck resources - Prototype shows measurable improvement over naive offloading - Development environment ready for full implementation

Phase 2: Core Components Implementation (Weeks 4-8)

Objectives: - Implement CGOPipe scheduler - Develop weight paging system - Create CPU attention kernels

Tasks:

2.1 HRM Performance Model (Week 4-5)

# aios/inference/moe_lightning/hrm/model.py
class HierarchicalRooflineModel:
    """
    Performance model for heterogeneous MoE inference
    """
    def __init__(self, hardware_config, model_config):
        self.hw = hardware_config
        self.model = model_config

    def estimate_latency(self, policy: InferencePolicy) -> float:
        """Estimate per-layer decode latency"""
        T_comm = self._compute_communication_time(policy)
        T_cpu = self._compute_cpu_time(policy)
        T_gpu = self._compute_gpu_time(policy)

        return max(T_comm, T_cpu, T_gpu)

    def optimize_policy(self, workload_config) -> InferencePolicy:
        """Use MILP to find optimal policy"""
        # Implement using scipy.optimize or CVXPY
        pass

2.2 CGOPipe Scheduler (Week 5-6)

# aios/inference/moe_lightning/scheduler/cgopipe.py
class CGOPipeScheduler:
    """
    CPU-GPU-I/O Pipeline Scheduler with weights paging
    """
    def __init__(self, policy: InferencePolicy, model, device_manager):
        self.policy = policy
        self.model = model
        self.dm = device_manager

        # Initialize CUDA streams
        self.gpu_stream = torch.cuda.Stream()
        self.transfer_stream = torch.cuda.Stream()

        # Initialize weight paging
        self.weight_pager = WeightPagingManager(
            layer_weight_size=model.layer_size,
            num_pages=policy.μ
        )

    def execute_decode_step(self, microbatches):
        """Execute one decode step with pipelined scheduling"""
        # Implement Algorithm 1 from paper
        pass

2.3 Weight Paging System (Week 6-7)

# aios/inference/moe_lightning/memory/weight_paging.py
class WeightPagingManager:
    """
    Manages paged transfers of model weights between CPU and GPU
    """
    def __init__(self, layer_weight_size, num_pages):
        self.page_size = layer_weight_size // num_pages
        self.num_pages = num_pages

        # Allocate pinned memory buffer
        self.pinned_buffer = self._allocate_pinned_buffer()

        # Page table for expert routing
        self.page_table = PageTable()

    def prefetch_page(self, layer_id, page_id, stream):
        """Asynchronously prefetch weight page"""
        # CPU DRAM → CPU Pinned (background thread)
        self._copy_to_pinned_async(layer_id, page_id)

        # CPU Pinned → GPU (CUDA stream)
        self._copy_to_gpu_async(layer_id, page_id, stream)

2.4 CPU Attention Kernels (Week 7-8)

# aios/inference/moe_lightning/kernels/cpu_attention.py
import intel_extension_for_pytorch as ipex

class CPUGroupedQueryAttention:
    """
    Optimized CPU attention using Intel MKL
    """
    def __init__(self, num_heads, num_kv_heads, head_dim):
        self.num_heads = num_heads
        self.num_kv_heads = num_kv_heads
        self.head_dim = head_dim

        # Configure MKL threads
        torch.set_num_threads(self._get_optimal_threads())

    @torch.jit.script
    def forward(self, query, key_cache, value_cache, seq_lens):
        """
        Compute attention on CPU with GQA optimization

        Args:
            query: [batch, num_heads, head_dim]
            key_cache: [batch, max_seq_len, num_kv_heads, head_dim]
            value_cache: [batch, max_seq_len, num_kv_heads, head_dim]
            seq_lens: [batch]
        """
        # Implement optimized GQA with SIMD vectorization
        pass

Deliverables: - Fully functional HRM model with policy optimizer - Working CGOPipe scheduler with async execution - CPU attention kernels matching or exceeding KV cache transfer speed - Weight paging system with double buffering

Success Criteria: - HRM policy optimizer runs in <1 minute for typical configs - CGOPipe achieves >80% resource utilization (GPU, CPU, I/O) - CPU attention is 3-4× faster than KV cache transfer - Weight paging reduces pipeline bubbles by >50%

Phase 3: Integration & Optimization (Weeks 9-12)

Objectives: - Integrate components into AI-OS - Implement request batching - Optimize end-to-end performance

Tasks:

3.1 AI-OS Integration (Week 9-10)

  • Create aios/inference/moe_lightning/ module structure
  • Implement unified inference API
  • Add MoE detection logic to model loading
  • Extend CLI with MoE-Lightning commands

3.2 Request Batching System (Week 10)

# aios/inference/moe_lightning/batching/request_batcher.py
class VariableLengthBatcher:
    """
    Batch variable-length requests without padding
    """
    def create_microbatches(self, requests, policy):
        """
        Implement Algorithm 2 from paper
        Balances token distribution across micro-batches
        """
        pass

3.3 Tensor Parallelism Support (Week 11)

# aios/inference/moe_lightning/distributed/tensor_parallel.py
class TensorParallelExecutor:
    """
    Execute MoE inference with tensor parallelism across GPUs
    """
    def __init__(self, num_gpus, policy):
        self.num_gpus = num_gpus
        self.device_mesh = self._create_device_mesh()

        # Scale policy parameters for multiple GPUs
        self.adjusted_policy = self._adjust_policy_for_tp(policy)

3.4 Performance Profiling & Optimization (Week 11-12)

  • Profile with NVIDIA Nsight Systems
  • Identify bottlenecks in data transfer and synchronization
  • Optimize kernel launch overhead
  • Tune thread counts for CPU operations
  • Implement caching for policy optimization results

Deliverables: - Complete integration with AI-OS inference pipeline - Variable-length batching system - Tensor parallelism for multi-GPU setups - Performance optimization report

Success Criteria: - API is compatible with existing AI-OS inference workflows - Variable-length batching provides 2-3× memory savings vs padding - Tensor parallelism achieves >2.5× speedup on 4 GPUs vs 2 GPUs - End-to-end latency overhead <5% compared to direct execution

Phase 4: Testing & Validation (Weeks 13-15)

Objectives: - Comprehensive testing across models and hardware - Validate performance claims - Ensure numerical correctness

Tasks:

4.1 Correctness Testing (Week 13)

  • Compare outputs with reference implementations (vLLM, transformers)
  • Test with multiple MoE architectures (Mixtral, DBRX, DeepSeek-MoE)
  • Validate attention correctness (CPU vs GPU implementation)
  • Memory safety checks (no leaks, proper cleanup)

4.2 Performance Benchmarking (Week 14)

# tests/benchmarks/test_moe_lightning_performance.py
class MoELightningBenchmarks:
    """
    Comprehensive performance benchmarks
    """
    def benchmark_throughput(self, model, hardware, workload):
        """
        Measure end-to-end throughput for various configurations

        Compare against:
        - FlexGen
        - DeepSpeed-Inference
        - vLLM (if fits in memory)
        """
        pass

    def benchmark_memory_efficiency(self, model, hardware):
        """
        Measure CPU/GPU memory usage at peak throughput
        """
        pass

    def benchmark_scaling(self, model, num_gpus_list):
        """
        Test tensor parallelism scaling efficiency
        """
        pass

Test Matrices:

Model Hardware Workload Expected Speedup
Mixtral 8x7B 1×T4 (16GB) MTBench (gen_len=128) 3.5× vs FlexGen
Mixtral 8x7B 1×L4 (24GB) HELM Reasoning 5× vs FlexGen
Mixtral 8x22B 2×T4 (32GB) MTBench (gen_len=64) 2.8× vs FlexGen
Mixtral 8x22B 4×T4 (64GB) MTBench (gen_len=64) Super-linear scaling
DBRX 4×T4 (64GB) MTBench (gen_len=128) 2.1-2.8× scaling

4.3 Stress Testing (Week 15)

  • Long-running inference jobs (24+ hours)
  • Extreme batch sizes (pushing memory limits)
  • Error handling and recovery
  • Multi-user concurrent requests

Deliverables: - Comprehensive test suite with >90% coverage - Benchmark results report comparing to baseline systems - Validated correctness across all supported models - Stress test results and reliability metrics

Success Criteria: - All correctness tests pass with numerical differences <1e-5 - Achieve paper's reported speedups (within 10% margin) - No memory leaks or crashes in 24-hour stress tests - Error recovery works for common failure modes

Phase 5: Documentation & Deployment (Weeks 16-17)

Objectives: - Create comprehensive documentation - Prepare for production deployment - Train users and gather feedback

Tasks:

5.1 User Documentation (Week 16)

# docs/guide/moe_lightning_quickstart.md
## MoE-Lightning Quick Start

Learn how to run large MoE models on consumer GPUs with MoE-Lightning.

### Installation
### Basic Usage
### Configuration Guide
### Performance Tuning
### Troubleshooting

5.2 Developer Documentation (Week 16)

  • API reference documentation
  • Architecture diagrams and design decisions
  • Contribution guidelines for MoE-Lightning components
  • Performance profiling guide

5.3 Example Notebooks (Week 17)

# examples/moe_lightning_mixtral.ipynb
"""
Running Mixtral 8x7B on a Single T4 GPU

This notebook demonstrates:
1. Model loading and configuration
2. Policy optimization for your hardware
3. Running inference with MoE-Lightning
4. Comparing performance to baseline systems
"""

5.4 Deployment Preparation (Week 17)

  • Docker images with optimized dependencies
  • Installation scripts for common platforms
  • Hardware compatibility matrix
  • Known issues and workarounds

Deliverables: - Complete user and developer documentation - Example notebooks and tutorials - Deployment artifacts (Docker images, installers) - Performance tuning guide

Success Criteria: - Documentation covers all use cases and configurations - New users can run first inference within 15 minutes - Examples run successfully on documented hardware - Docker deployment works on Ubuntu 20.04/22.04 and Windows 11

Phase 6: Advanced Features & Extensions (Weeks 18-20)

Objectives: - Add advanced optimizations - Support additional models and hardware - Integrate with AI-OS ecosystem

Tasks:

6.1 Extended Hardware Support (Week 18)

  • AMD GPU support (ROCm)
  • Apple Silicon support (MPS backend)
  • Intel GPU support (oneAPI)
  • Multi-node distributed inference

6.2 Advanced Optimizations (Week 19)

  • KV cache quantization (INT4, INT8)
  • Sparse attention patterns
  • Expert caching for common routing patterns
  • Dynamic policy adjustment based on runtime metrics

6.3 AI-OS Ecosystem Integration (Week 20)

  • Integration with Expert Manager for MoE expert tracking
  • Memory estimation updates for MoE-Lightning
  • Dream system integration for synthetic data generation
  • CLI enhancements for interactive optimization

Deliverables: - Multi-platform hardware support - Advanced optimization features - Deep integration with AI-OS features

Success Criteria: - Works on at least 2 additional hardware platforms - Advanced optimizations provide additional 1.2-1.5× speedup - Seamless integration with existing AI-OS workflows

Technical Requirements

Hardware Requirements

Minimum Configuration

  • GPU: NVIDIA T4 (16GB) or equivalent
  • CPU: 8-core, 2.0+ GHz
  • RAM: 64GB DDR4
  • Storage: 500GB SSD for model weights
  • PCIe: Gen3 x16 for optimal CPU-GPU bandwidth
  • GPU: NVIDIA L4 (24GB) or 2× T4 (32GB total)
  • CPU: 16-core, 2.5+ GHz (e.g., Intel Xeon)
  • RAM: 128GB DDR4 or better
  • Storage: 1TB NVMe SSD
  • PCIe: Gen4 x16

Optimal Configuration

  • GPU: 4× NVIDIA T4 (64GB) or 2× A100 (80GB each)
  • CPU: 32-core, 3.0+ GHz
  • RAM: 256GB+ DDR5
  • Storage: 2TB NVMe SSD RAID
  • Network: 100Gbps for multi-node (future)

Software Requirements

Core Dependencies

# pyproject.toml additions
[project.dependencies]
torch = ">=2.1.0"
intel-extension-for-pytorch = ">=2.1.0"  # For CPU kernels
vllm = ">=0.2.0"  # For MoE support
sglang = ">=0.1.0"  # For structured generation
scipy = ">=1.10.0"  # For optimization
cvxpy = ">=1.4.0"  # For MILP solver (optional)

System Requirements

  • CUDA: 12.1+ (for NVIDIA GPUs)
  • cuDNN: 8.9+
  • Intel MKL: 2023.0+ (for CPU operations)
  • Python: 3.10+
  • OS: Ubuntu 20.04+, Windows 11, or macOS 13+

Model Support

Supported Architectures

  1. Mixtral Family
  2. Mixtral 8x7B (~47GB)

  3. Mixtral 8x22B (~141GB)

  4. DBRX

  5. DBRX 132B (16 experts)

  6. DeepSeek-MoE

  7. DeepSeek-MoE 16B

  8. DeepSeek-MoE 145B

  9. Future Support

  10. Custom MoE architectures
  11. Dense model fallback mode

Model Format Support

  • HuggingFace Transformers format
  • SafeTensors format (preferred)
  • GGUF format (via conversion)

Performance Targets

Throughput Targets

Based on paper results, we target the following throughput improvements:

Single GPU (T4 16GB)

Workload Baseline (FlexGen) Target (MoE-Lightning) Speedup
MTBench (gen=32) 6.5 tok/s 22.8 tok/s 3.5×
MTBench (gen=128) 9.5 tok/s 30.1 tok/s 3.2×
HELM Reasoning 16.9 tok/s 26.3 tok/s 1.6×
HELM Summarization 2.6 tok/s 4.5 tok/s 1.7×

Single GPU (L4 24GB)

Workload Baseline (FlexGen) Target (MoE-Lightning) Speedup
MTBench (gen=128) 20.7 tok/s 105.3 tok/s 5.1×
HELM Reasoning 50.1 tok/s 105.3 tok/s 2.1×

Multi-GPU (4×T4 64GB)

Model 2×T4 4×T4 Scaling Factor
Mixtral 8x22B 25.3 tok/s 70.2 tok/s 2.77×
DBRX 22.1 tok/s 58.3 tok/s 2.64×

Memory Efficiency Targets

Metric Target Baseline
CPU Memory at Peak Throughput 100GB 200GB+
GPU Memory Utilization >85% 60-70%
I/O Bandwidth Utilization >90% 50-60%
Pipeline Bubble Reduction >50% N/A

Latency Targets

Phase Target Acceptable Range
Policy Optimization <1 minute <5 minutes
Model Loading <30 seconds <60 seconds
First Token Latency <2 seconds <5 seconds
Per-token Latency (decode) <100ms <200ms

Risk Assessment

Technical Risks

Risk 1: Performance Below Targets

Probability: Medium
Impact: High

Description: Achieved performance doesn't match paper's reported improvements

Mitigation: - Start with exact replication of paper's test setup - Profile extensively to identify bottlenecks - Engage with paper authors for implementation guidance - Have fallback to incremental improvements (e.g., 2× instead of 10×)

Risk 2: Hardware Compatibility Issues

Probability: Medium
Impact: Medium

Description: CPU attention or weight paging doesn't work on all hardware

Mitigation: - Test on multiple hardware configurations early - Implement fallback to GPU-only execution - Use platform-agnostic libraries where possible - Maintain compatibility matrix in documentation

Risk 3: Memory Management Complexity

Probability: High
Impact: High

Description: Memory leaks or fragmentation under high load

Mitigation: - Extensive memory profiling (valgrind, CUDA sanitizers) - Implement comprehensive cleanup logic - Use smart pointers and RAII patterns - Regular stress testing during development

Risk 4: Integration Conflicts

Probability: Medium
Impact: Medium

Description: Conflicts with existing AI-OS inference systems

Mitigation: - Design clean interface boundaries - Make integration opt-in initially - Comprehensive integration testing - Version compatibility testing

Project Risks

Risk 5: Scope Creep

Probability: High
Impact: Medium

Description: Feature requests expand beyond core MoE-Lightning

Mitigation: - Clearly define Phase 1-3 deliverables as MVP - Defer advanced features to Phase 6 - Regular scope reviews with stakeholders - Maintain feature backlog for future work

Risk 6: Resource Constraints

Probability: Medium
Impact: High

Description: Insufficient GPU resources for testing

Mitigation: - Use cloud resources (GCP, AWS) for expensive tests - Prioritize tests on available hardware - Implement simulation mode for policy testing - Partner with organizations with GPU access

Risk 7: Dependency Changes

Probability: Medium
Impact: Medium

Description: Breaking changes in PyTorch, vLLM, or other dependencies

Mitigation: - Pin dependency versions initially - Monitor upstream changes - Contribute to upstream projects - Maintain compatibility layer

Testing Strategy

Unit Testing

Coverage Target: >90% for core components

# tests/unit/test_hrm_model.py
class TestHierarchicalRooflineModel:
    def test_compute_roofs(self):
        """Test compute and memory roof calculations"""

    def test_turning_points(self):
        """Test turning point identification"""

    def test_policy_optimization(self):
        """Test MILP policy search"""

    def test_memory_constraints(self):
        """Test policy respects memory limits"""

# tests/unit/test_cgopipe.py
class TestCGOPipeScheduler:
    def test_async_execution(self):
        """Test asynchronous task execution"""

    def test_synchronization(self):
        """Test data dependency enforcement"""

    def test_weight_paging(self):
        """Test weight page scheduling"""

# tests/unit/test_cpu_attention.py
class TestCPUAttention:
    def test_correctness(self):
        """Compare output with reference implementation"""

    def test_performance(self):
        """Verify speedup vs KV cache transfer"""

Integration Testing

# tests/integration/test_moe_lightning_inference.py
class TestMoELightningInference:
    def test_mixtral_8x7b_single_gpu(self):
        """Test Mixtral 8x7B on single T4"""

    def test_mixtral_8x22b_multi_gpu(self):
        """Test Mixtral 8x22B on multiple GPUs"""

    def test_dbrx_inference(self):
        """Test DBRX model"""

    def test_variable_length_batching(self):
        """Test with mixed prompt lengths"""

Performance Testing

# tests/performance/test_throughput.py
class TestThroughput:
    @pytest.mark.benchmark
    def test_mtbench_t4(self):
        """Benchmark MTBench on T4 GPU"""
        assert throughput > 22.8  # tokens/sec

    @pytest.mark.benchmark
    def test_helm_reasoning_l4(self):
        """Benchmark HELM reasoning on L4"""
        assert throughput > 105.3  # tokens/sec

    @pytest.mark.benchmark
    def test_scaling_4xT4(self):
        """Test super-linear scaling"""
        scaling_factor = throughput_4gpu / throughput_2gpu
        assert scaling_factor > 2.5

Correctness Testing

# tests/correctness/test_numerical_accuracy.py
class TestNumericalAccuracy:
    def test_cpu_vs_gpu_attention(self):
        """Verify CPU attention matches GPU"""
        max_diff = compute_max_difference(cpu_output, gpu_output)
        assert max_diff < 1e-5

    def test_vs_vllm_reference(self):
        """Compare outputs with vLLM"""
        assert outputs_match(moe_lightning_output, vllm_output)

Stress Testing

# tests/stress/test_reliability.py
class TestReliability:
    def test_24_hour_continuous_inference(self):
        """Run inference for 24 hours"""

    def test_memory_leak_detection(self):
        """Monitor memory usage over 1000 batches"""

    def test_concurrent_requests(self):
        """Handle 100 concurrent requests"""

Future Enhancements

Short-term (6 months)

  1. Flash Attention Integration
  2. Integrate Flash Attention 2/3 for GPU attention
  3. Further reduce memory footprint

  4. Improve attention performance

  5. Quantization Support

  6. INT8/INT4 weight quantization
  7. KV cache quantization

  8. GPTQ/AWQ integration

  9. Speculative Decoding

  10. Use smaller MoE model as draft

  11. Improve latency for interactive use cases

  12. Expert Caching

  13. Cache frequently activated experts on GPU
  14. Dynamic expert placement based on routing patterns

Mid-term (12 months)

  1. Multi-Node Distributed Inference
  2. Pipeline parallelism across nodes
  3. Expert parallelism

  4. Optimize for cluster environments

  5. Continuous Batching

  6. Orca-style continuous batching

  7. Improve throughput for serving workloads

  8. Adaptive Policy Selection

  9. Runtime policy adjustment
  10. Workload-aware optimization

  11. Reinforcement learning for policy search

  12. AMD/Intel GPU Support

  13. ROCm backend for AMD GPUs
  14. OneAPI backend for Intel GPUs
  15. Multi-vendor heterogeneous execution

Long-term (18+ months)

  1. Automatic Model Parallelism
  2. Automatic sharding for arbitrary MoE sizes
  3. Mixed expert and tensor parallelism

  4. Cost-aware placement optimization

  5. Disk Offloading

  6. NVMe SSD integration for very large models
  7. Intelligent prefetching

  8. Compression for disk storage

  9. Custom CUDA Kernels

  10. Fused MoE kernels
  11. Optimized expert routing

  12. Custom attention implementations

  13. Neural Architecture Search for MoE

  14. Automatic expert configuration
  15. Router optimization
  16. Efficient expert specialization

Success Metrics

Technical Metrics

Metric Target Method
Throughput vs FlexGen 3.5-10× Benchmark comparison
Memory efficiency 2-3× less CPU RAM Memory profiling
GPU utilization >85% NVIDIA profiler
I/O utilization >90% Bandwidth monitoring
Scaling efficiency (4 GPUs) >2.5× vs 2 GPUs Multi-GPU benchmarks
Policy search time <1 minute Timer measurement
First token latency <2 seconds Latency profiling

Project Metrics

Metric Target Method
Code coverage >90% pytest-cov
Documentation coverage 100% of public APIs Doc review
User adoption 50+ users in first month Analytics
Bug reports <5 critical bugs Issue tracking
Performance regression <5% CI/CD benchmarks
Community contributions 5+ contributors GitHub metrics

User Experience Metrics

Metric Target Method
Time to first inference <15 minutes User studies
Setup success rate >90% Telemetry
User satisfaction >4/5 rating Surveys
Documentation clarity >4/5 rating Feedback forms

References

Primary Paper

  • MoE-Lightning: Shiyi Cao et al., "MoE-Lightning: High-Throughput MoE Inference on Memory-constrained GPUs," arXiv:2411.11217, 2024. Link

MoE Architectures

  • Mixtral: "Mixtral of Experts," Mistral AI, 2024
  • DBRX: "Introducing DBRX," Databricks, 2024
  • DeepSeek-MoE: "DeepSeekMoE: Towards Ultimate Expert Specialization," 2024
  • GShard: Lepikhin et al., "GShard: Scaling Giant Models with Conditional Computation," 2020

Performance Modeling

  • Roofline Model: Williams et al., "Roofline: An Insightful Visual Performance Model," CACM 2009
  • LLM Inference Analysis: Yuan et al., "LLM Inference Unveiled: Survey and Roofline Model Insights," 2024

Inference Systems

  • FlexGen: Sheng et al., "FlexGen: High-throughput Generative Inference," ICML 2023
  • vLLM: Kwon et al., "Efficient Memory Management for LLM Serving with PagedAttention," SOSP 2023
  • DeepSpeed-Inference: Aminabadi et al., "DeepSpeed-Inference: Enabling Efficient Inference," SC 2022
  • FastDecode: He & Zhai, "FastDecode: High-throughput GPU-efficient LLM Serving," 2024

Optimization Techniques

  • Flash Attention: Dao et al., "FlashAttention: Fast and Memory-Efficient Exact Attention," NeurIPS 2022
  • Flash Attention 2: Dao, "FlashAttention-2: Faster Attention with Better Parallelism," ICLR 2024
  • Speculative Decoding: Chen et al., "Accelerating LLM Decoding with Speculative Sampling," 2023

Implementation References

  • PyTorch Documentation: https://pytorch.org/docs/stable/
  • vLLM GitHub: https://github.com/vllm-project/vllm
  • SGLang GitHub: https://github.com/sgl-project/sglang
  • Intel Extension for PyTorch: https://github.com/intel/intel-extension-for-pytorch
  • HuggingFace Transformers: https://github.com/huggingface/transformers
  • Existing AI-OS architecture documentation
  • Memory estimation system (artifacts/memory_estimation/)
  • Expert management system (artifacts/experts/)
  • HRM training integration (aios/cli/aios.py - HRM commands)

Appendix

A. Glossary

  • CGOPipe: CPU-GPU-I/O Pipeline scheduling strategy
  • HRM: Hierarchical Roofline Model
  • MoE: Mixture of Experts
  • GQA: Grouped Query Attention
  • FFN: Feed-Forward Network
  • Operational Intensity: Ratio of FLOPs to bytes accessed (FLOPs/Byte)
  • Roofline Model: Performance model correlating compute and memory bandwidth
  • Turning Point: Critical operational intensity where bottleneck resource changes
  • Balance Point: Optimal configuration where all resources are fully utilized
  • Micro-batch: Subset of batch that fits in GPU memory for one kernel execution
  • Weight Paging: Technique of chunking and scheduling weight transfers

B. Hardware Specifications

NVIDIA T4

  • Memory: 16GB GDDR6
  • Memory Bandwidth: 320 GB/s
  • Compute (FP16): 65 TFLOPS
  • TDP: 70W
  • Use Case: Cost-effective inference

NVIDIA L4

  • Memory: 24GB GDDR6
  • Memory Bandwidth: 300 GB/s
  • Compute (FP16): 121 TFLOPS
  • TDP: 72W
  • Use Case: Balanced performance/cost

NVIDIA A100

  • Memory: 40GB or 80GB HBM2e
  • Memory Bandwidth: 1.6 TB/s (40GB) / 2.0 TB/s (80GB)
  • Compute (FP16): 312 TFLOPS
  • TDP: 400W
  • Use Case: High-performance inference

C. Model Specifications

Mixtral 8x7B

  • Total Parameters: 46.7B
  • Active Parameters: 12.9B per token
  • Experts: 8 per MoE layer
  • Top-K: 2
  • Hidden Dim: 4096
  • Intermediate Dim: 14336
  • Layers: 32
  • Memory (FP16): ~94GB

Mixtral 8x22B

  • Total Parameters: 141B
  • Active Parameters: ~39B per token
  • Experts: 8 per MoE layer
  • Top-K: 2
  • Hidden Dim: 6144
  • Memory (FP16): ~282GB

DBRX

  • Total Parameters: 132B
  • Active Parameters: 36B per token
  • Experts: 16 per MoE layer
  • Top-K: 4
  • Layers: 40
  • Memory (FP16): ~264GB

D. Configuration Examples

config/moe_lightning.yaml

# Hardware profiles
hardware:
  t4_single:
    gpu_memory: 16384  # MB
    cpu_memory: 65536  # MB
    gpu_bandwidth: 320  # GB/s
    cpu_bandwidth: 100  # GB/s
    cpu_to_gpu_bandwidth: 16  # GB/s (PCIe Gen3 x16)
    gpu_compute: 65  # TFLOPS (FP16)
    cpu_compute: 1.6  # TFLOPS

  l4_single:
    gpu_memory: 24576
    cpu_memory: 65536
    gpu_bandwidth: 300
    cpu_bandwidth: 120
    cpu_to_gpu_bandwidth: 16
    gpu_compute: 121
    cpu_compute: 1.6

# Model configurations
models:
  mixtral-8x7b:
    num_layers: 32
    hidden_dim: 4096
    intermediate_dim: 14336
    num_experts: 8
    top_k: 2
    num_heads: 32
    num_kv_heads: 8

# Default policies (auto-optimized if not specified)
policies:
  mixtral-8x7b-t4:
    batch_size: 36
    micro_batch_size: 4
    use_cpu_attention: true
    use_gpu_ffn: true
    weight_gpu_ratio: 0.0
    kv_cache_gpu_ratio: 0.0

Contact & Collaboration

Project Lead: [To be assigned]
Technical Advisors: [Paper authors - optional consultation]
Discussion Forum: GitHub Discussions in AI-OS repo
Issue Tracking: GitHub Issues with label moe-lightning

Collaboration Opportunities: - Hardware vendors: Testing on diverse GPU configurations - Research institutions: Advanced optimization techniques - Open-source community: Code contributions and testing

Document Version: 1.0
Last Updated: November 8, 2025
Next Review: December 8, 2025