With regards to real-time AI-driven applications like self-driving cars or healthcare monitoring, even an additional second to process an input could have serious consequences. Real-time AI applications require reliable GPUs and processing power, which has been very expensive and cost-prohibitive for a lot of applications – until now.
By adopting an optimizing inference process, businesses cannot only maximize AI efficiency; they may reduce energy consumption and operational costs (by as much as 90%); enhance privacy and security; and even improve customer satisfaction.
Common inference issues
A number of the commonest issues faced by firms relating to managing AI efficiencies include underutilized GPU clusters, default to general purpose models and lack of insight into associated costs.
Teams often provision GPU clusters for peak load, but between 70 and 80 percent of the time, they’re underutilized on account of uneven workflows.
Moreover, teams default to large general-purpose models (GPT-4, Claude) even for tasks that would run on smaller, cheaper open-source models. The explanations? A lack of information and a steep learning curve with constructing custom models.
Finally, engineers typically lack insight into the real-time cost for every request, resulting in hefty bills. Tools like PromptLayer, Helicone may help to offer this insight.
With an absence of controls on model selection, batching and utilization, inference costs can scale exponentially (by as much as 10 times), waste resources, limit accuracy and diminish user experience.
Energy consumption and operational costs
Running larger LLMs like GPT-4, Llama 3 70B or Mixtral-8x7B requires significantly more power per token. On average, 40 to 50 percent of the energy utilized by a knowledge center powers the computing equipment, with a further 30 to 40 percent dedicated to cooling the equipment.
Subsequently, for a corporation running around-the-clock for inference at scale, it’s more useful to think about an on-premesis provider versus a cloud provider to avoid paying a premium cost and consuming more energy.
Privacy and security
In response to Cisco’s 2025 Data Privacy Benchmark Study, “64% of respondents worry about inadvertently sharing sensitive information publicly or with competitors, yet nearly half admit to inputting personal worker or non-public data into GenAI tools.” This increases the chance of non-compliance if the information is wrongly logged or cached.
One other opportunity for risk is running models across different customer organizations on a shared infrastructure; this may result in data breaches and performance issues, and there’s an added risk of 1 user’s actions impacting other users. Hence, enterprises generally prefer services deployed of their cloud.
Customer satisfaction
When responses take greater than just a few seconds to point out up, users typically drop off, supporting the trouble by engineers to overoptimize for zero latency. Moreover, applications present “obstacles akin to hallucinations and inaccuracy that will limit widespread impact and adoption,” in response to a Gartner press release.
Business advantages of managing these issues
Optimizing batching, selecting right-sized models (e.g., switching from Llama 70B or closed source models like GPT to Gemma 2B where possible) and improving GPU utilization can cut inference bills by between 60 and 80 percent. Using tools like vLLM may help, as can switching to a serverless pay-as-you-go model for a spiky workflow.
Take Cleanlab, for instance. Cleanlab launched the Trustworthy Language Model (TLM) to add a trustworthiness rating to each LLM response. It’s designed for high-quality outputs and enhanced reliability, which is critical for enterprise applications to forestall unchecked hallucinations. Before Inferless, Cleanlabs experienced increased GPU costs, as GPUs were running even after they weren’t actively getting used. Their problems were typical for traditional cloud GPU providers: high latency, inefficient cost management and a fancy environment to administer. With serverless inference, they cut costs by 90 percent while maintaining performance levels. More importantly, they went live inside two weeks with no additional engineering overhead costs.
Optimizing model architectures
Foundation models like GPT and Claude are sometimes trained for generality, not efficiency or specific tasks. By not customizing open source models for specific use-cases, businesses waste memory and compute time for tasks that don’t need that scale.
Newer GPU chips like H100 are fast and efficient. These are especially essential when running large scale operations like video generation or AI-related tasks. More CUDA cores increases processing speed, outperforming smaller GPUs; NVIDIA’s Tensor cores are designed to speed up these tasks at scale.
GPU memory can be essential in optimizing model architectures, as large AI models require significant space. This extra memory enables the GPU to run larger models without compromising speed. Conversely, the performance of smaller GPUs which have less VRAM suffers, as they move data to a slower system RAM.
Several advantages of optimizing model architecture include money and time savings. First, switching from dense transformer to LoRA-optimized or FlashAttention-based variants can shave between 200 and 400 milliseconds off response time per query, which is crucial in chatbots and gaming, for instance. Moreover quantized models (like 4-bit or 8-bit) need less VRAM and run faster on cheaper GPUs.
Long-term, optimizing model architecture saves money on inference, as optimized models can run on smaller chips.
Optimizing model architecture involves the next steps:
- Quantization — reducing precision (FP32 → INT4/INT8), saving memory and speeding up compute time
- Pruning — removing less useful weights or layers (structured or unstructured)
- Distillation — training a smaller “student” model to mimic the output of a bigger one
Compressing model size
Smaller models mean faster inference and cheaper infrastructure. Big models (13B+, 70B+) require expensive GPUs (A100s, H100s), high VRAM and more power. Compressing them enables them to run on cheaper hardware, like A10s or T4s, with much lower latency.
Compressed models are also critical for running on-device (phones, browsers, IoT) inference, as smaller models enable the service of more concurrent requests without scaling infrastructure. In a chatbot with greater than 1,000 concurrent users, going from a 13B to a 7B compressed model allowed one team to serve greater than twice the quantity of users per GPU without latency spikes.
Leveraging specialized hardware
General-purpose CPUs aren’t built for tensor operations. Specialized hardware like NVIDIA A100s, H100s, Google TPUs or AWS Inferentia can offer faster inference (between 10 and 100x) for LLMs with higher energy efficiency. Shaving even 100 milliseconds per request could make a difference when processing hundreds of thousands of requests every day.
Consider this hypothetical example:
A team is running LLaMA-13B on standard A10 GPUs for its internal RAG system. Latency is around 1.9 seconds, they usually can’t batch much on account of VRAM limits. So that they switch to H100s with TensorRT-LLM, Enable FP8 and optimized attention kernel, increase batch size from eight to 64. The result’s cutting latency to 400 milliseconds with a five-time increase in throughput.
In consequence, they can serve requests five times on the identical budget and release engineers from navigating infrastructure bottlenecks.
Evaluating deployment options
Different processes require different infrastructures; a chatbot with 10 users and a search engine serving one million queries per day have different needs. Going all-in on cloud (e.g., AWS Sagemaker) or DIY GPU servers without evaluating cost-performance ratios results in wasted spend and poor user experience. Note that for those who commit early to a closed cloud provider, migrating the answer later is painful. Nevertheless, evaluating early with a pay-as-you-go structure gives you options down the road.
Evaluation encompasses the next steps:
- Benchmark model latency and value across platforms: Run A/B tests on AWS, Azure, local GPU clusters or serverless tools to duplicate.
- Measure cold start performance: This is very essential for serverless or event-driven workloads, because models load faster.
- Assess observability and scaling limits: Evaluate available metrics and discover what the max queries per second is before degrading.
- Check compliance support: Determine whether you’ll be able to implement geo-bound data rules or audit logs.
- Estimate total cost of ownership. This could include GPU hours, storage, bandwidth and overhead for teams.
The underside line
Inference enables businesses to optimize their AI performance, lower energy usage and costs, maintain privacy and security and keep customers completely happy.
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