Why GPUs Are Rarely Used in High-Frequency Trading¶

In many fields - AI, simulations, rendering - GPUs offer massive speedups. But in ultra-low-latency trading, they're rarely the right tool.

Here is Why:¶

1. HFT is driven by latency, not compute throughput.¶

Most high-frequency strategies require a response in microseconds or less. Even minimal GPU operations - including kernel launches and memory transfers - typically introduce latencies of several microseconds at minimum, often more.

2. GPU data movement adds unavoidable delay.¶

Transferring data between host and GPU memory via PCIe or NVLink adds significant latency. This overhead is incompatible with strategies that must react in real time to market data.

3. HFT workloads are sequential and stateful.¶

Tasks like market data decoding, order book maintenance, and order routing follow a strict sequence and depend on low-latency memory access. These workloads benefit more from CPU cache locality than from GPU parallelism.

Where GPUs can be useful in trading:¶

1. Historical backtesting and data crunching (Monte Carlo, signal scans)
2. Training machine learning models
3. Risk simulations or portfolio optimization

Conclusion¶

In short: GPUs are excellent for compute-bound problems, but not for the real-time, latency-sensitive execution paths that define HFT.