An Efficient Hybrid Sparse Attention with CPU-GPU Parallelism for Long-Context Inference

Fluxion, a new hybrid CPU-GPU system, optimizes long-context inference by efficiently managing key-value caches split between host and GPU memory. The approach delivers 1.5x-3.7x speedup over existing baselines while maintaining near-baseline accuracy, addressing a critical bottleneck in modern large language model deployment.

Long-context inference has become a critical challenge in deploying large language models, particularly as sequence lengths push beyond GPU memory capacity. Fluxion addresses this by introducing a hybrid architecture that smartly distributes computation between CPU and GPU resources, solving a problem that has plagued production deployments. The system recognizes that block-sparse attention alone cannot achieve efficiency gains—the real bottleneck emerges from PCIe bandwidth limitations and coordination overhead between devices.

The technical approach centers on three coordinated mechanisms: output-aware budgeting that allocates KV cache space intelligently, head-specific sparse configurations that adapt to different attention patterns, and cross-device scheduling that minimizes GPU idle time. This multi-faceted optimization reflects a mature understanding of modern inference constraints. Traditional GPU-only designs waste expensive accelerator resources waiting for data transfer, while naive CPU-GPU splits create synchronization bottlenecks.

For the AI infrastructure industry, Fluxion's results carry significant implications. The 1.5x-3.7x speedup with minimal accuracy loss (only -0.26 relative degradation) suggests that disaggregated inference systems using consumer-grade interconnects can approach the efficiency of tightly-coupled architectures. This matters for cloud providers managing inference at scale and for edge deployment scenarios where GPU memory remains constrained.

The validation across multiple models and benchmarks strengthens the findings, though practical adoption depends on implementation complexity and framework integration. The work exemplifies how systems-level optimization—rather than algorithmic innovation alone—continues to unlock efficiency gains in LLM deployment, a trend likely to accelerate as context windows expand beyond current limits.

• →Fluxion achieves 1.5x-3.7x speedup over hybrid sparse baselines for long-context inference with minimal accuracy loss.
• →Hybrid CPU-GPU design with coordinated execution outperforms GPU-only sparse attention when KV caches exceed GPU memory.
• →Output-aware budgeting and head-specific sparse configuration enable fine-grained optimization of inference efficiency.
• →PCIe bandwidth and CPU-side bottlenecks remain critical constraints that architecture-level coordination can address.
• →Production LLM deployments may reduce inference costs significantly by leveraging CPU-resident KV caches efficiently.