PagedLLM: Memory-Virtualized Inference Runtime for Transformers

Product/Proof-of-Concept Development Plan

1. Executive Summary

Vision

Build a production-ready inference runtime that enables running large language models on memory-constrained GPUs through intelligent memory virtualization, achieving near-native performance while supporting 10x larger contexts than available VRAM.

Value Proposition

• For Enterprises: Run larger models on existing hardware
• For Researchers: Experiment with extreme context lengths
• For Startups: Reduce infrastructure costs by 70%
• For Edge Deployment: Enable LLMs on consumer GPUs

Key Differentiators

• First unified memory virtualization for both KV cache AND activations
• Predictive prefetching based on attention patterns
• Zero-copy integration with existing PyTorch/HuggingFace models
• Production-grade reliability with fault tolerance

2. System Design

2.1 Architecture Overview

flowchart TB
    A["Python / HuggingFace - Attention Hook via ctypes"]

    %% Go Control Plane (two rows)
    subgraph B["Go Control Plane"]
        direction TB
        subgraph B_row1[ ]
            direction LR
            B1["Pager"]
            B2["Policies (LRU/2Q)"]
        end
        subgraph B_row2[ ]
            direction LR
            B3["Residency Map"]
            B4["Metrics (Prometheus)"]
        end
    end

    %% CUDA / C++ Data Plane (two rows)
    subgraph C["CUDA / C++ Data Plane"]
        direction TB
        subgraph C_row1[ ]
            direction LR
            C1["VRAM Arena"]
            C2["Streams / Events"]
        end
        subgraph C_row2[ ]
            direction LR
            C3["Pack / Gather"]
            C4["Async Memcpy"]
        end
    end

    %% Storage Tiers (single row)
    subgraph D["Storage Tiers"]
        direction LR
        D1["GPU (VRAM)"]
        D2["HOST (Pinned)"]
        D3["NVMe (io_uring)"]
    end

    A --> B
    A --> C
    B --> D
    C --> D

Loading

2.2 Core Components

2.2.1 Memory Manager

Responsibilities:

• Virtual address space management
• Page allocation and deallocation
• Tier management (VRAM → Host → NVMe)
• Memory pressure handling

Key Classes:

class MemoryManager:
    def __init__(self, config: MemoryConfig):
        self.vram_pool = VRAMPool(config.vram_size)
        self.host_pool = HostMemPool(config.host_size)
        self.nvme_pool = NVMePool(config.nvme_path)
        self.page_table = PageTable()

    def allocate(self, size: int, hint: MemoryHint) -> VirtualAddress
    def deallocate(self, addr: VirtualAddress) -> None
    def ensure_resident(self, addr: VirtualAddress) -> PhysicalAddress
    def evict_pages(self, num_pages: int) -> List[Page]

2.2.2 Scheduler & Executor

Responsibilities:

• Operation scheduling
• Dependency tracking
• Overlap compute with memory transfers
• Stream management

Key Classes:

class Scheduler:
    def __init__(self, num_streams: int = 4):
        self.compute_stream = cuda.Stream()
        self.transfer_streams = [cuda.Stream() for _ in range(num_streams)]
        self.operation_queue = PriorityQueue()

    def schedule_operation(self, op: Operation) -> Future
    def execute_batch(self, ops: List[Operation]) -> None
    def wait_for_completion(self) -> None

2.2.3 Transfer Engine

Responsibilities:

• Asynchronous memory transfers
• Compression/decompression
• Bandwidth optimization
• Multi-path transfers

Implementation:

class TransferEngine:
    def __init__(self):
        self.dma_engine = DMAEngine()
        self.compressor = LZ4Compressor()

    async def transfer_async(
        self,
        src: MemoryLocation,
        dst: MemoryLocation,
        size: int,
        compress: bool = True
    ) -> None:
        if compress and self._should_compress(src, dst):
            data = await self.compressor.compress_async(src, size)
            await self.dma_engine.transfer(data, dst)
            await self.compressor.decompress_async(dst, data)
        else:
            await self.dma_engine.transfer(src, dst, size)

2.2.4 Predictive Prefetcher

Responsibilities:

• Analyze attention patterns
• Predict future access
• Schedule prefetch operations
• Adaptive learning

Algorithm:

class AttentionPredictor:
    def __init__(self, window_size: int = 100):
        self.access_history = deque(maxlen=window_size)
        self.attention_scores = {}

    def record_access(self, layer: int, token_ids: List[int]):
        self.access_history.append((layer, token_ids))

    def predict_next_access(self, current_layer: int) -> List[int]:
        # Use attention scores to predict which tokens
        # will be accessed in the next iteration
        pattern = self._analyze_pattern()
        return self._extrapolate(pattern, current_layer)

2.3 Integration Points

2.3.1 PyTorch Integration

# Custom autograd function for paged operations
class PagedLinear(torch.autograd.Function):
    @staticmethod
    def forward(ctx, input, weight, bias=None):
        # Ensure weight is resident
        weight_addr = memory_manager.ensure_resident(weight.virtual_addr)
        output = F.linear(input, weight_addr.to_tensor(), bias)
        ctx.save_for_backward(input, weight_addr, bias)
        return output

    @staticmethod
    def backward(ctx, grad_output):
        # Handle gradients with paging
        pass

# Monkey-patch PyTorch modules
def enable_paging():
    torch.nn.Linear.forward = paged_linear_forward
    torch.nn.MultiheadAttention.forward = paged_attention_forward

2.3.2 HuggingFace Integration

from transformers import AutoModelForCausalLM
from pagedllm import PagedLLMConfig, enable_paging

# Load model with paging enabled
config = PagedLLMConfig(
    vram_size="24GB",
    host_memory="128GB",
    nvme_path="/mnt/nvme/pagedllm",
    prefetch_strategy="attention_aware"
)

model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-70b")
model = enable_paging(model, config)

# Use normally
output = model.generate(input_ids, max_length=100000)

3. Technical Stack

3.1 Core Technologies

Language & Frameworks

• Primary Language: Python 3.10+ (high-level API)
• Performance Critical: C++/CUDA (kernels and memory management)
• Deep Learning: PyTorch 2.0+
• Kernel Development: Triton (for custom kernels)

Libraries & Tools

• CUDA Libraries:

  • cuDNN (optimized operations)
  • NCCL (multi-GPU communication)
  • cuBLAS (linear algebra)
  • CUB (CUDA building blocks)

• Memory Management:

  • jemalloc (host memory allocation)
  • CUDA Unified Memory (baseline comparison)
  • SPDK (NVMe optimization)

• Compression:

  • LZ4 (fast compression)
  • Zstandard (better ratio)
  • Custom bit-packing for KV cache

Development Tools

• Build System: CMake + setuptools
• Testing: pytest, CUDA-memcheck
• Profiling: Nsight Systems, PyTorch Profiler
• CI/CD: GitHub Actions, Docker

3.2 Custom CUDA Kernels

Paged Attention Kernel

__global__ void paged_attention_kernel(
    const float* __restrict__ query,      // [batch, heads, seq_len, head_dim]
    const float* __restrict__ key_cache,   // Paged storage
    const float* __restrict__ value_cache, // Paged storage
    const int* __restrict__ page_table,    // Virtual to physical mapping
    float* __restrict__ output,            // [batch, heads, seq_len, head_dim]
    const int seq_len,
    const int head_dim,
    const int page_size
) {
    // Efficient paged attention implementation
    // with coalesced memory access
}

Async Transfer Kernel

__global__ void async_page_transfer(
    void* dst,
    const void* src,
    size_t size,
    cudaStream_t stream
) {
    // Asynchronous memory transfer with
    // overlap of compute and transfer
}

3.3 Triton Kernels

Fused Operations

@triton.jit
def fused_paged_mlp(
    x_ptr, w1_ptr, w2_ptr,
    page_table_ptr,
    output_ptr,
    BLOCK_SIZE: tl.constexpr
):
    """Fused MLP with paging support"""
    # Load from paged memory
    # Compute MLP
    # Store results

4. Evaluation Milestones

Milestone 1: Proof of Concept (Week 4)

Success Criteria:

• Basic paging system operational
• Can offload and restore tensors
• Overhead < 50% vs native

Metrics:

def measure_poc():
    # Measure basic paging overhead
    tensor_size = 1_000_000_000  # 1GB

    # Native baseline
    start = time.time()
    tensor = torch.randn(tensor_size // 4).cuda()
    result = tensor.sum()
    native_time = time.time() - start

    # Paged version
    start = time.time()
    paged_tensor = PagedTensor(tensor_size // 4)
    result = paged_tensor.sum()
    paged_time = time.time() - start

    overhead = (paged_time - native_time) / native_time
    assert overhead < 0.5, f"Overhead {overhead:.2%} exceeds 50%"

Milestone 2: KV Cache Demo (Week 8)

Success Criteria:

• Llama-2 7B with 64K context
• Throughput > 50% of baseline
• Memory usage < 24GB

Demo Script:

from pagedllm import PagedLlama

model = PagedLlama.from_pretrained("meta-llama/Llama-2-7b")
model.set_memory_limit("24GB")

# Generate with long context
context = "..." * 32000  # 64K tokens
output = model.generate(
    context,
    max_new_tokens=100,
    measure_memory=True
)

print(f"Peak memory: {output.peak_memory_gb:.1f}GB")
print(f"Throughput: {output.tokens_per_second:.1f} tok/s")

Milestone 3: Production Ready (Week 16)

Success Criteria:

• Support for 5+ model architectures
• 99.9% reliability over 24h test
• Documentation and tutorials
• Performance within 20% of vLLM

Milestone 4: Competitive Advantage (Week 20)

Success Criteria:

• 1M token context support
• Better than vLLM on 3+ benchmarks
• Multi-GPU linear scaling
• Published benchmarks

5. Risk Analysis & Mitigation

Technical Risks

Risk 1: Page Fault Latency

Impact: High latency makes system unusable Probability: Medium Mitigation:

• Aggressive prefetching
• Larger page sizes
• Memory pooling
• Fallback to smaller contexts

Risk 2: Prediction Accuracy

Impact: Poor prefetch accuracy negates benefits Probability: Medium Mitigation:

• Multiple prediction strategies
• Adaptive selection
• Conservative prefetching
• Manual hints API

Risk 3: Framework Compatibility

Impact: Cannot integrate with existing code Probability: Low Mitigation:

• Multiple integration levels
• Compatibility layer
• Extensive testing
• Gradual rollout

Business Risks

Risk 4: Competition

Impact: vLLM or others implement similar features Probability: High Mitigation:

• Fast execution
• Patent filing
• Open source community
• Focus on unique features

Risk 5: Adoption

Impact: Users don't see value Probability: Medium Mitigation:

• Clear benchmarks
• Easy integration
• Strong documentation
• Reference customers

6. Success Metrics

Technical Metrics

• Context Length: 10x increase vs VRAM limit
• Throughput: Within 30% of native
• Latency: <100ms overhead for TTFT
• Memory Efficiency: 90% utilization
• Prefetch Accuracy: >85% hit rate

Product Metrics

• GitHub Stars: 1000+ in 6 months
• Downloads: 10K+ monthly
• Contributors: 20+ active
• Enterprise Users: 5+ companies
• Model Coverage: 10+ architectures

Business Metrics

• Cost Reduction: 70% infrastructure savings
• Performance/Dollar: 3x improvement
• Time to Market: 6 months to v1.0
• Support Tickets: <5% of users
• Documentation: 95% coverage

Appendix A: Benchmark Suite

class PagedLLMBenchmark:
    def __init__(self):
        self.models = [
            "meta-llama/Llama-2-7b",
            "meta-llama/Llama-2-70b",
            "mistralai/Mixtral-8x7B"
        ]
        self.context_lengths = [2048, 8192, 32768, 131072]
        self.batch_sizes = [1, 4, 8, 16]

    def run_benchmark(self, backend="pagedllm"):
        results = {}
        for model in self.models:
            for context in self.context_lengths:
                for batch in self.batch_sizes:
                    result = self.measure_performance(
                        model, context, batch, backend
                    )
                    results[(model, context, batch)] = result
        return results

    def measure_performance(self, model, context, batch, backend):
        # Load model with specified backend
        # Measure throughput, latency, memory
        # Return metrics
        pass

Appendix B: API Reference

# High-level API
from pagedllm import PagedModel, MemoryConfig

config = MemoryConfig(
    vram_limit="24GB",
    host_memory="128GB",
    nvme_path="/mnt/nvme",
    page_size="128MB",
    prefetch_strategy="attention_aware",
    compression="lz4"
)

model = PagedModel.from_pretrained(
    "meta-llama/Llama-2-70b",
    memory_config=config
)

# Generate with automatic paging
output = model.generate(
    input_ids,
    max_length=100000,
    temperature=0.7
)

# Manual memory management
with model.memory_scope() as scope:
    # Operations within scope share memory pool
    output1 = model.generate(prompt1)
    output2 = model.generate(prompt2)
    # Memory automatically released on scope exit

Appendix C: Installation Guide

# From PyPI
pip install pagedllm

# From source
git clone https://github.com/pagedllm/pagedllm
cd pagedllm
pip install -e .

# With optional dependencies
pip install pagedllm[enterprise]  # Enterprise features
pip install pagedllm[dev]         # Development tools
pip install pagedllm[benchmark]   # Benchmarking suite

# Docker
docker pull pagedllm/runtime:latest
docker run -it --gpus all pagedllm/runtime

# Verification
python -c "import pagedllm; pagedllm.verify_installation()"