– Slow Response Times (Latency): Enterprise applications need to respond within less than one second, but big LLMs respond in several seconds, especially under utilization. In a high-stakes fintech situation say, approving a credit transaction or reporting a suspicious trade several seconds feels like an eternity. Slow AI reactions irritate customers and even undermine the effectiveness of the system (think about a fraud tool that reacts after the shady transaction has already slipped on through!).
– High Infrastructure Costs: These models are computation beasts. An LLM of today’s level usually has tens of billions of parameters, which means a lot of memory and computation to run. In practice, having a single big model may equate to distributed inference across dozens of GPUs or specialized chips. And the price is astronomical: e.g., an 8×A100 GPU cloud instance costs around $33 per hour (well over $285,000 per year), and a production deployment can necessitate many such servers to meet demand. No surprise that the CTO’s budget spreadsheet is bleeding red.
– Unpredictable Energy Consumption: Those GPUs don’t just cost a small fortune to rent they suck huge amounts of power. Running a high-end GPU at peak load can cost around $60 worth of electricity per month per GPU, and data centers charge for that power. At scale, if your hardware is inefficient or over-provisioned, you’re paying for a lot of watts being wasted. Plus, larger models tend to chew through more energy one study found that the biggest LLMs, while more accurate, incur substantially higher carbon emissions than smaller ones. For fintech firms targeting green programs or just wanting to project operating costs, this volatility is a giant issue.
– Scalability and Usage Issues: Good scaling performance is an exercise in balance act. You can scale up hardware to handle the spikes, but idle GPUs sitting around idle during the slow times burn money unnecessarily. However, if you skew the infrastructure to save money, you’ll experience unacceptable latency when traffic is high[.](http://shashankguda.medium.com/) There’s a classic throughput vs. latency trade-off: give more simultaneous requests through your system and each user might wait longer[.]
(http://shashankguda.medium.com/) Getting that sweet spot right is tricky. Without tuning, you under-provision (slowing down critical services) or over-provision (wasting money and energy). Neither one is a fintech winning approach, where both high efficiency and real-time responsiveness matter.
