How to build faster inference for open-source models

The race to production AI isn’t just about model quality—it’s about delivery.

Enterprises today are realizing that even the best-performing LLMs are useless without fast, scalable, and cost-efficient serving infrastructure.

The growing popularity of small open-source language models (SLMs) has helped reduce the burden of expensive, high-end GPUs, making it easier to achieve low-latency without massive investments. And, thanks to fine-tuning, it's now possible to deliver GPT-4o-level accuracy for your use case and any specialized task with models that are 10-100x smaller than large frontier models. However, unlocking your AI stack's full potential involves more than deploying the latest SLM.

AI teams need to address three key inference challenges when serving LLMs in production:

1. Scaling efficiently without overprovisioning GPUs

2. Maximizing throughput on existing infrastructure

3. Increasing serving capacity without incurring massive costs

The Challenge of Provisioning GPUs in a Dynamic Environment

Managing spiky, unpredictable production traffic is one of the toughest challenges when trying to build an efficient LLM serving infrastructure. Customer-facing AI applications often experience uneven traffic patterns—with sharp spikes during peak hours and dips during off-peak times. Unlike traditional web services, scaling LLM workloads dynamically is far more complex due to:

• Limited GPU availability especially for high-demand models like H100s

• Inflexible node configurations imposed by GPU providers (e.g., 8-GPU minimum per node)

• Slow cold starts caused by large model weights and container images that need to be loaded before serving

This lack of flexibility leads organizations to adopt two main provisioning strategies for GPUs:

1. Provisioning for average utilization: This reduces costs but may lead to latency issues during peak times.

^{Provisioning GPUs for average traffic patterns can lead to poor latency during peak loads}

1. Provisioning for maximum capacity: This ensures performance but results in idle GPUs during low-traffic periods. While this approach ensures constant availability, it can lead to significant waste during periods of low demand. This is a hidden cost that businesses often don’t think about when they start using AI.

^{Provisioning GPUs for maximum traffic leads to costly unused capacity.}

These factors make it challenging for organizations to implement efficient, cost-effective inference for production AI.

Production-Grade Infra with Smart Autoscaling Optimized for Throughput

To address these challenges, there are several key strategies that enable teams to cost-efficiently accelerate inference without stockpiling mountains of GPUs.

1. Smart Autoscaling with Minimal Cold Start Times

GPU autoscaling is a critical feature for managing AI workloads, ensuring that resources are dynamically adjusted based on real-time demand. During peak traffic, the system can preempt lower-priority batch jobs (like training jobs) to reallocate GPUs to higher priority inference jobs, ensuring rapid responsiveness when demand surges.

One of the most significant pain points in GPU autoscaling is the cold start delay. Launching a new LLM instance can take 10–14 minutes due to:

• Large model weights (often tens of GBs)

• Heavy container images (8–10 GB)

• Initialization and load times

Through a series of optimizations, there are already some solutions to cut this time to under 1 minute for most enterprise replicas by using:

• Smart caching strategies that keep model weights and containers warm

• Dedicated caches for enterprise users on their own instances

• A cache manager that proactively ensures readiness

The combination of smart GPU autoscaling with sub-1-minute cold start times means LLMs can be reallocated and start serving at scale almost instantly—even during traffic spikes—without the need to overprovision GPUs.

^{Smart GPU autoscaling and cold start time reduction for LLM inference with Predibase infrastructure provides massive inference gains.}

2. Maximizing Inference Performance on Existing Infrastructure

Serving models at scale isn’t just about managing costs—it’s also about speed. Latency directly impacts the end user experience as slow response times lead to frustrated users and less engagement. However, improving throughput typically means sacrificing quality or throwing additional costly hardware at the problem.

People are tackling this problem head-on with Turbo LoRA, a novel innovation that breaks the trade-off between throughput and cost. By combining speculative decoding with LoRA fine-tuning in a parameter-efficient way, Turbo LoRA generates multiple tokens per step without bloating memory or sacrificing precision. Unlike alternatives like Medusa, Turbo LoRA’s lightweight adapters (just megabytes in size) make it ideal for both small and large batch serving.

Paired with the FP8 quantization, the performance lift becomes dramatic:

• 50% memory savings over FP16

• Up to 4x throughput gains

• Maintained or improved output quality, even at scale

3. Fitting More Models on a Single GPU with Multi-LoRA Serving

One of the most overlooked costs in AI inference is running a dedicated GPU for every model variant. For enterprises deploying multiple fine-tuned models across customers, languages, or domains, this quickly becomes unsustainable.

There is a solution called LoRA Exchange (LoRAX), designed to:

• Dynamically load and unload fine-tuned adapters on demand

• Batch requests across different adapters, maximizing GPU utilization

• Cache model weights at the GPU, CPU, and disk level for lightning-fast access

With these innovations, LoRAX enables you to serve hundreds of fine-tuned models from a single GPU, with near-instant response times and minimal overhead.

^{Serve multiple fine-tuned LLMs on one base model with dynamic swapping.}

LoRAX really shines by sustaining high throughput even when dozens of adapters are hot‑swapped onto a single base model.