Research Paper

Core Innovation

This paper presents TileMix, a novel tile-centric mixed-precision routing method that partitions attention matrices into hardware-aligned tiles and dynamically routes each tile group through FP16 or INT8 computation. Unlike prior uniform low-precision or token-selective methods, TileMix preserves dense token connectivity and supports various LLM features without retraining, enabling a controllable accuracy-efficiency tradeoff.

Why It Matters

Large language models face high computational and memory costs during long-context inference due to quadratic attention complexity. TileMix reduces these costs by selectively applying mixed precision at the tile level, improving throughput and preserving accuracy without retraining. This approach enables scalable, efficient LLM deployment in real-world applications requiring long-context understanding.

Potential Customers & Pain Points

• Cloud providers – Need to reduce LLM inference cost and latency
• AI platform developers – Require scalable efficient long-context model serving
• Enterprises using LLMs – Demand improved throughput without accuracy loss in large-scale deployments

Business Model

Open-source core technology with enterprise licensing for optimized integration, support, and custom feature development targeting cloud providers and AI platform vendors.

Competitive Landscape

• NVIDIA TensorRT
• Intel OpenVINO
• Hugging Face Optimum
• Google TPU Mixed Precision
• Microsoft DeepSpeed

Implementation Challenges

• Integration complexity with diverse LLM architectures and hardware
• Balancing precision routing overhead with performance gains
• Adoption resistance due to existing optimized inference pipelines

Validation Strategy

• Benchmark TileMix on diverse LLMs and hardware against FP16 and INT8 baselines
• Pilot deployments with cloud providers to measure cost and latency improvements
• Collect user feedback on accuracy-efficiency tradeoffs in real-world applications