Essential Insights
- Prefill and decode disaggregation boosts throughput at scale, but often introduces network overhead and operational complexity, making it less beneficial for small teams or workloads.
- Chunked prefill, which interleaves smaller prefill chunks with decode batches on the same GPU, effectively reduces scheduling interference without extra network costs.
- Disaggregation’s advantages only materialize under specific conditions: large GPU counts, fast interconnects, and capacity for infrastructure management; otherwise, it can cause bottlenecks and failures.
- Default to chunked prefill for most teams, as it solves scheduling issues with minimal overhead, reserving full disaggregation for hyperscalers or large-scale deployments with sufficient infrastructure.
Disaggregation: A Challenging Solution for Most Teams
Disaggregation separates prefill and decode onto different GPUs. Many frameworks now support this. Companies like NVIDIA and SGLang have made it a default for large deployments. The idea sounds good: increase throughput by splitting tasks. However, for smaller teams or fewer GPUs, disaggregation often fails to deliver. Analysis shows that at low GPU counts, the gains are minor or vanish. The main impact is better control over service levels, not faster processing. More GPUs and faster networks are needed for disaggregation to work well. Otherwise, it becomes less effective or even problematic.
How Disaggregation Addresses Hardware Interference
Prefill and decode have opposite hardware needs. Prefill uses compute power: running many matrix multiplications at once. Decode relies on memory bandwidth: reading cache and producing tokens sequentially. When both share a GPU, they compete for resources. For example, a large prefill request during decoding can slow down token output by up to 30 times. This interference causes delays and stalls. The solution? Dividing these tasks onto separate pools has proven effective at scale. It allows prefill and decode to run without fighting each other. But at smaller scales, a simpler method works better.
Chunked Prefill: A Practical Alternative
Most teams do not need full disaggregation. Instead, they can enable chunked prefill. This method breaks long prefill requests into smaller pieces. These chunks are interleaved with decode batches on the same GPU. It avoids extra network transfer, requires no complex tuning, and reduces interference. For example, this approach has shown a 50% increase in throughput with standard software. It works like a toll plaza: trucks pass through one lane at a time, keeping traffic flowing smoothly. Chunked prefill is a cost-effective way to improve performance for most workloads. It bounds interference and maintains simplicity, making it the best default for many teams.
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