Scheduling Strategies to Maximize LLM Utilization During Scaling

When you run a large language model at scale, your GPUs aren’t just sitting idle-they’re wasting money. A lot of it. Studies show that without smart scheduling, 65-75% of your GPU capacity vanishes during inference because LLMs don’t work like traditional models. They generate text one token at a time, and if you batch requests poorly, your hardware spends more time waiting than computing. That’s not inefficiency-it’s financial leakage. But the right scheduling strategy can turn that waste into a 3.7x boost in throughput and cut costs by up to 86.92%, according to Latitude’s 2024 benchmarks.

Why Traditional Batching Fails for LLMs

Traditional deep learning models process inputs in fixed-size batches. You gather 32 prompts, run them together, and get 32 outputs. Simple. Efficient. But LLMs are autoregressive. Each response is built token by token, and no one knows how long it’ll take. One user might ask for a one-sentence summary. Another might demand a 2,000-word report. If you wait for all requests to arrive before batching, your GPU sits half-empty for seconds-or minutes-while it waits for the slowest request to start. That’s called underutilization. And it’s expensive.

Imagine a restaurant where you only seat groups when everyone arrives. One party has 4 people. Another has 1. You wait 20 minutes for the second party to show up, even though the table could’ve been filled immediately. That’s what naive batching does. LLMs need continuous batching, where requests are added to an active batch as they come in, even mid-generation. Systems like vLLM is an open-source inference engine that uses continuous batching and PagedAttention to maximize GPU utilization for LLMs do exactly this. They track each request’s progress and slot new ones into unused space in the batch. Result? GPU utilization jumps from 30-40% to 70-85%.

Sequence Scheduling: Grouping by Predicted Length

Not all continuous batching is equal. The real magic happens when you group requests not just by when they arrive, but by how long they’re expected to take. This is called sequence scheduling. You don’t just throw everything into a pot-you sort them by predicted output length.

How do you predict length? A lightweight model, often trained as a classifier head on the LLM itself, estimates how many tokens each prompt will generate. It’s not perfect, but it’s good enough. Zheng et al. (2023) showed that binning requests into 50-token chunks (e.g., 0-50, 51-100, 101-150) reduces padding waste by 22.3%. Padding waste is the empty space left when you pad shorter sequences to match the longest one in the batch. Less padding = more tokens processed per GPU cycle = higher throughput.

Systems like Sarathi-Serve is a scheduling framework that uses sequence scheduling and work-conserving policies to achieve near-optimal GPU utilization for LLM inference and Orca is a scheduling system designed for high-throughput LLM serving with dynamic batching and prediction-based grouping use this method. They don’t just batch-they cluster. Requests with similar predicted lengths get grouped together. This keeps batches tight and efficient. If you have five requests predicted to generate 80 tokens each, you can fill a batch perfectly. If you mix one 80-token request with one 1,200-token request, the whole batch waits for the long one, and the others are slowed down.

The Token Budget Trade-Off

Every scheduling system has a knob: the token budget. This is the maximum number of tokens a batch can contain. Too low, and you underutilize the GPU. Too high, and you get long tail latencies.

Agrawal et al. (2023) tested budgets from 256 to 2048 tokens. At 2048, prefill latency (the initial processing of the prompt) dropped by 31.5% because more prompts could be processed together. But end-to-end latency-the time from request to full response-got worse. Why? The long decode phase dragged everything down. The sweet spot? Around 512 tokens. It balances prefill efficiency with decode speed. For latency-sensitive apps like chatbots or customer service bots, 512 is often better than 2048.

Advanced schedulers like vLLM 0.5.0 is a version of vLLM released in June 2025 that uses adaptive token budgeting to optimize performance based on real-time workload patterns now adjust the budget dynamically. If the system detects mostly short responses, it lowers the budget. If long-form content floods in, it increases it. This self-tuning approach reduces the need for manual tuning and keeps utilization high under shifting loads.

Memory Management: PagedAttention and Prefix Caching

LLMs store past tokens in a key-value (KV) cache. If you handle 1,000 requests simultaneously, you’re storing 1,000 separate caches. Traditional memory allocation fragments these caches like a messy desk-gaps everywhere. That’s wasted VRAM.

PagedAttention is a memory management technique in vLLM that treats the KV cache like virtual memory, reducing fragmentation and increasing usable GPU memory by up to 40.2% fixes this. It splits the cache into fixed-size pages, like pages in an operating system. You can reuse pages across requests. If two users start with the same prompt, you don’t store it twice-you share the pages. Red Hat’s May 2025 case study found this cuts memory waste by 40.2% compared to legacy systems.

Even better: llm-d is a scheduling framework that uses prefix-aware routing to detect and reuse previously processed context, reducing time-to-first-token by 63.4ms on average can spot when a new request shares the first 100 tokens of a previous one. It reuses the cached KV values for those tokens. That’s called prefix caching. Red Hat saw time-to-first-token drop by 63.4ms on average. For users, that’s the difference between a chatbot feeling responsive and laggy.

Work-Conserving vs. Conservative Scheduling

Not all schedulers are created equal. Some are greedy. Some are cautious.

Work-conserving schedulers, like Sarathi-Serve and Orca, never let a GPU sit idle if there’s work to do. They’ll break up a batch mid-run to slot in a new request. They’re complex, but they hit 98.7% of theoretical maximum throughput, according to April 2025 research. Conservative schedulers wait for full batches. They’re simpler but only hit 76-82% efficiency. Under moderate load, they become unstable. Requests pile up. Latency spikes.

Chen et al. (August 2025) tested two prediction models: one that assumed the worst-case output length (conservative), and one that started low and adjusted upward (adaptive). The adaptive model achieved 15.8% higher utilization. Why? Because it didn’t waste space waiting for outputs that never came. It trusted the prediction and moved on.

For high-traffic apps, work-conserving is non-negotiable. For low-volume internal tools? Maybe not. But if you’re scaling beyond 500 concurrent requests, you’re already in the zone where conservative scheduling costs you money.

Latency-Sensitive Applications and Hierarchical Scheduling

Not all requests are equal. A customer service bot needs sub-100ms responses. A document summarizer can wait 2 seconds. Hierarchical scheduling gives priority to the urgent ones.

Clarifai’s March 2025 benchmark compared FIFO (first-in, first-out) with hierarchical scheduling. FIFO gave a 99.9th percentile latency of 214ms. Hierarchical scheduling? 87ms. How? It creates queues: one for critical tasks, one for background. Even if the critical queue is full, the system reserves a portion of GPU capacity for it. The rest of the hardware handles the slower stuff. This isn’t just about speed-it’s about reliability. If your app’s SLA demands 99% of requests under 100ms, you need this structure.

Cost and Implementation Realities

You might be thinking: “This sounds great, but how hard is it to set up?”

Basic dynamic batching with vLLM is an open-source inference engine that uses continuous batching and PagedAttention to maximize GPU utilization for LLMs takes 2-3 weeks to deploy. You get 2.1-3.4x throughput improvement right away. No magic. Just better batching.

Full sequence scheduling with prediction models? That’s 6-8 weeks. You need engineers who understand distributed systems, transformer architecture, and performance profiling. NVIDIA’s training course reports a 78% success rate for teams with this background.

But the ROI? Fast. Red Hat found that for workloads over 500 concurrent requests, the cost of implementing llm-d is a scheduling framework that uses prefix-aware routing to detect and reuse previously processed context, reducing time-to-first-token by 63.4ms on average pays for itself in 8.2 days. Why? Because a single idle GPU costs $1,200/month. Multiply that by 50 GPUs, and you’re burning $60,000/month. Even a 10% gain saves $6,000/month. That’s not an upgrade-it’s a profit center.

What’s Next? AI-Native Scheduling

The next frontier? Schedulers that use AI to schedule better.

Meta’s internal tests show a scheduler powered by a lightweight LLM can adjust parameters in real time based on traffic patterns, prediction accuracy, and hardware load. It achieved 12.7% more efficiency than rule-based systems. It’s not science fiction-it’s happening now.

Cloud providers are catching up. AWS launched its own scheduling layer in October 2025 for SageMaker. Now, 47% of their LLM deployments use it. Customers don’t need to build it-they just flip a switch. That’s the future: scheduling becomes invisible, built into the platform. But until then, if you’re scaling LLMs, you’re either optimizing-or losing money.

Final Thought: It’s Not Optional Anymore

Gartner predicts that by 2026, 85% of enterprise LLM deployments will use specialized scheduling. In 2024, it was 32%. The gap is closing fast. The companies winning now aren’t the ones with the biggest models-they’re the ones who schedule the smartest. If you’re still using vanilla batching, you’re running on a flat tire. The upgrade isn’t about performance. It’s about survival.