In the fast-paced world of AI, large language models (LLMs) like GPT-4 and Llama are powering everything from chatbots to code assistants. But here’s a dirty secret: your LLM inference—the process of generating responses—might be running up to five times slower than necessary. The culprit? A overly cautious approach to handling uncertainty in output lengths.
A new paper from researchers at Stanford University and HKUST reveals a game-changing algorithm that could slash latency and boost throughput without touching your model or hardware. By shifting from pessimism to adaptive optimism, it achieves performance nearly identical to a “perfect” scheduler that knows the future. Let’s dive into why this matters and how it works.
The Hidden Bottleneck in LLM Inference
LLM inference isn’t just about crunching numbers; it’s an operational puzzle. When a prompt arrives, the model processes it in two phases: a quick “prefill” to handle the input, followed by a token-by-token “decode” phase where the output is generated autoregressively. The input length is known upfront, but the output length? That’s a wild card— it could be a short “yes” or a rambling essay.
This uncertainty wreaks havoc on scheduling. LLMs run on GPUs with limited KV (key-value) cache memory, which stores intermediate computations to speed up generation. To avoid overflows, schedulers must predict and allocate memory wisely. But predictions aren’t perfect; they often come as intervals (e.g., “between 50 and 500 tokens”) from ML models or heuristics.
The standard fix? Be conservative. Algorithms like the research’s benchmark “Amax” assume every request will hit the maximum predicted length. This prevents crashes but leads to massive underutilization: batches stay small, GPUs idle, and latency balloons. In experiments on real datasets like LMSYS-Chat-1M, Amax’s performance degraded sharply as prediction uncertainty grew, sometimes resulting in latencies 5x higher than optimal.
Why does this matter? Inference is energy-hungry and costly. With billions of requests hitting services daily, even small inefficiencies add up to millions in wasted compute and frustrated users.
Amin: The Optimistic Scheduler That Learns on the Fly
The research team from Peking University, Stanford and HKUST propose “Amin,” an algorithm that flips the script. Instead of fearing the worst, Amin starts optimistic: it assumes each request’s output is the predicted minimum length (the lower bound of the interval). This maximizes initial batch sizes, packing more requests into the KV cache right away.
But optimism alone could cause overflows if outputs run long. Amin’s secret sauce is adaptability:
- Dynamic Refinement: As tokens generate, Amin updates its “pseudo” lower bound for each request in real-time. If a request has already produced, say, 100 tokens, it knows the true length is at least that much—refining future scheduling decisions.
- Ordered Eviction: When memory gets tight, Amin doesn’t panic. It sorts active jobs by their current pseudo lower bounds and evicts those with the least progress first (breaking ties randomly). This protects jobs that are further along, minimizing wasted work from restarts.
- No Upper Bounds Needed: Crucially, Amin ignores the upper bound entirely. Predicting tight upper bounds is notoriously hard and error-prone, but lower bounds are easier and more reliable. This makes Amin practical for real-world deployment.
The algorithm runs in O(M log M) time per step (where M is the KV cache size), making it efficient even on large systems. In pseudocode, it looks like this: initialize with lower bounds, sort and batch greedily, monitor for overflows, evict smartly, and repeat.
The Proof Is in the Performance: Near-Optimal and Robust
What sets Amin apart isn’t just intuition—it’s rigorous math and experiments.
The research team analyzes Amin’s “competitive ratio,” comparing its latency to a hindsight optimal scheduler (H-SF) that knows all true output lengths in advance. They prove Amin achieves an O(log(α⁻¹)) ratio, where α is the ratio of lower to upper bound (a measure of prediction uncertainty). As uncertainty grows (α shrinks), Amax’s ratio explodes unboundedly—think O(α⁻¹⁵) in the worst case. Amin stays logarithmic, ensuring bounded inefficiency.
For specific distributions:
- Under two-point outputs (all short or all long), Amin’s ratio is at most 1.5.
- For geometric distributions (exponential decay, common in real data), it’s bounded by 1.7.
- For linearly weighted geometrics, it’s tightly 1.56.
Numerical tests on 2,000 samples from LMSYS-Chat-1M tell the story:
- With crude predictions ([1000] for all), Amin matched H-SF’s latency, while Amax lagged 2x behind.2508.14544v1.pdf
- With binned intervals (e.g., , ), Amin halved Amax’s latency gap.2508.14544v1.pdf
- Under varying accuracy (intervals like [0.9x true, 1.1x true]), Amin stayed robust, delivering up to 5x better latency than Amax when predictions were noisy.
In one simulation, Amin handled high-uncertainty workloads with latencies approaching the theoretical minimum, proving it’s not just fast—it’s resilient.
Conclusion
Pessimism has held back LLM inference for too long. By embracing adaptive optimism, Amin shows we can squeeze near-perfect performance from imperfect predictions. As AI workloads explode, tools like this will be essential for sustainable scaling.
If you’re building or deploying LLMs, skim the paper—it’s a quick read with pseudocode ready to adapt. Your inference pipeline might just get a 5x speed boost. What’s stopping you?
FAQs
1) What makes the Amin algorithm faster than the standard conservative scheduler?
Amin leverages optimistic scheduling: it initially contends that each request’s output will be the minimum predicted length, which allows for more jobs to be packed into the GPU’s KV cache, maximizing concurrency and throughput. As decoding progresses, Amin dynamically updates the lower bound for each job and smartly evicts jobs with the least progress if memory is running low, achieving near-optimal latency even under high uncertainty.
2) Why is using only the lower bound prediction practical for real-world inference?
Lower bounds are easier and more reliable to predict: Amin requires only the lower bound of each output length, bypassing the computational and statistical difficulties associated with upper bound prediction. This makes it robust and practical for deployment in production scenarios where prediction precision can vary.
3) How does Amin’s performance compare to traditional pessimistic scheduling?
Amin’s competitive ratio scales logarithmically with prediction uncertainty: In contrast to conservative schedulers that become extremely inefficient as uncertainty grows, Amin guarantees robust performance with up to 5x lower latency in realistic workloads. It often matches the performance of a hindsight-optimal scheduler, establishing a new benchmark for inference efficiency under uncertainty
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Michal Sutter is a data science professional with a Master of Science in Data Science from the University of Padova. With a solid foundation in statistical analysis, machine learning, and data engineering, Michal excels at transforming complex datasets into actionable insights.
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