The Inference-Compute Frontier and a Latency-Efficient Architecture for Limit Order Book Prediction
Researchers have discovered that inference-compute tradeoffs in financial prediction follow power-law scaling patterns similar to those observed in large language models, suggesting fundamental principles govern efficiency across domains. By testing models from decision trees to specialized neural architectures on limit order book data, they achieved 0.941 R2 fit predicting high-compute performance from low-compute regimes. Critically, latency does not scale with compute in the same way, motivating a new hardware-aware architecture (FastBiNLOB) that decouples these constraints. This work bridges ML scaling theory with real-world inference optimization, relevant to anyone deploying models under strict latency requirements.
Modelwire context
ExplainerThe key insight is that latency and compute don't scale together in financial prediction models. Prior work assumed they moved in lockstep; this paper shows they decouple, enabling a new architectural approach (FastBiNLOB) that optimizes for wall-clock time rather than just FLOP reduction.
This connects directly to the HiReLC compression framework from earlier today. Both papers tackle the same deployment problem: getting models fast enough for production without manual tuning. Where HiReLC automates the search for pruning and quantization parameters across hardware targets, this work goes upstream and asks whether the model architecture itself should change when latency is the constraint. The power-law scaling discovery also echoes the broader pattern the field is seeing across domains (LLMs, quantum chemistry solvers in the VMC paper from today), suggesting these efficiency laws are fundamental rather than domain-specific accidents.
If FastBiNLOB achieves comparable prediction accuracy to standard architectures while hitting sub-millisecond latency on real exchange data within the next two quarters, that validates the latency-decoupling thesis. If the same power-law fit holds when tested on other financial prediction tasks (not just FI-2010 and MLPLOB), the scaling law generalizes beyond order book forecasting.
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MentionsFI-2010 · MLPLOB · FastBiNLOB
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