Towards Data Science September 4, 2026 By Mostafa Ibrahim

Disaggregation Is a Thousand-GPU Problem

Read the original on Towards Data Science →

The article discusses the conditions under which separating the prefill phase from the decode phase in large language model inference is beneficial. It explains that only when three specific criteria are met does this split pay off, and it recommends using chunked prefill as the default approach when operating below a certain GPU threshold.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at Towards Data Science.

arXiv AI

Jul 3

Towards Load-Aware Prefill Deflection for Disaggregated LLM Serving

arXiv:2607. 02043v1 Announce Type: cross Abstract: Disaggregated LLM serving runs prefill and decode on separate GPU pools to keep the two phases from interfering.

By Shrikara Arun, Anjaly Parayil, Srikant Bharadwaj, Renee St. Amant, Victor R\"uhle

llms benchmarks

Towards Data Science

Jun 3

I Built a C++ Backend So My GPU Would Stop Eating Air

A comprehensive guide to optimizing LLM inference by eliminating padding overhead with hardware-aware sequence packing. The post I Built a C++ Backend So My GPU Would Stop Eating Air appeared first on Towards Data Science .

By Anubhab Banerjee

llms

arXiv Machine Learning

Sep 23

Disaggregated Quantization: Specializing LLM Prefill and Decode

arXiv:2609.26333v1 Announce Type: new Abstract: Prefill and decode reward different approaches to quantization: low-precision arithmetic accelerates prompt processing, while compact weights reduce me...

By Andrei Panferov, Maximilian Kleinegger, Sweta Priyadarshi, Tijmen Blankevoort, Dan Alistarh

llms efficiency benchmarks

Towards Data Science

Jun 19

GPU-Resident Top-K for Agentic RAG: I Built a CUDA Kernel So My Retrieval Step Would Stop Bouncing Off the GPU

The PCIe transfer latency is silently bottlenecking your agentic inference. Here is how building a custom device-resident vector search kernel bypasses the CPU to unlock deterministic microsecond tail latencies.

By Anubhab Banerjee

rag agents

Hugging Face Trending Papers

Jun 9

Prefilling-dLLM: Predictive Prefilling for Long-Context Inference in Diffusion Language Models

Diffusion large language models (dLLMs) re-encode the entire prefix at every denoising step, causing recomputation that scales quadratically with context length and becomes prohibitive for long-context scenarios. We propose Prefilling-dLLM, a training-free prefill-decode disaggregation framework for dLLMs that partitions the prefix into N chunks, caches their KV representations once, and selects the top-K most relevant chunks with intra-chunk token sparsity for decoding, showing that sparse prefilling can outperform dense attention while reducing per-step complexity from quadratic in the full sequence length to quadratic only in the decode length.

llms diffusion efficiency benchmarks

arXiv Computation and Language

Sep 1

Prefilling-dLLM: Predictive Prefilling for Long-Context Inference in Diffusion Language Models

arXiv:2606.10537v2 Announce Type: replace Abstract: Diffusion large language models (dLLMs) re-encode the entire prefix at every denoising step, causing recomputation that scales quadratically with...

By Jing Xiong, Qi Han, Shansan Gong, Yunta Hsieh, Boyuan Zheng, Chengyue Wu, Chaofan Tao, Chenyang Zhao, Ngai Wong

llms diffusion efficiency benchmarks

One email a morning, machine-written

Related stories

Towards Load-Aware Prefill Deflection for Disaggregated LLM Serving

I Built a C++ Backend So My GPU Would Stop Eating Air

Disaggregated Quantization: Specializing LLM Prefill and Decode

GPU-Resident Top-K for Agentic RAG: I Built a CUDA Kernel So My Retrieval Step Would Stop Bouncing Off the GPU

Prefilling-dLLM: Predictive Prefilling for Long-Context Inference in Diffusion Language Models

Prefilling-dLLM: Predictive Prefilling for Long-Context Inference in Diffusion Language Models