Xema is presented, a memory-efficient diffusion serving system that exploits predictable tensor lifetimes for trace-guided memory optimization and introduces an offline planner that jointly selects parallelism, concurrency, and memory control under GPU memory and SLO constraints.
Abstract
Diffusion models are increasingly deployed as production visual-generation services, where serving high-resolution image and long video generation is often limited by GPU memory. Popular memory-saving techniques such as weight offloading, sharding, and VAE slicing are often not practical because they tend to introduce significant performance overhead. In this paper, we present Xema, a memory-efficient diffusion serving system that exploits predictable tensor lifetimes for trace-guided memory optimization. For each request template, Xema derives an offline memory trace to identify short memory-pressure intervals and applies memory mitigation only within these intervals and only by the amount needed to fit the target GPU budget. Xema further constructs a static memory layout for tensors with predictable lifetimes, reducing fragmentation-induced reserved memory and making offline memory reasoning reliable at runtime. Built on this memory optimization layer, Xema introduces an offline planner that jointly selects parallelism, concurrency, and memory control under GPU memory and SLO constraints. The selected plan is stored in a plan table and directly used by the online serving runtime. We implement Xema on production diffusion pipelines and evaluate it with Flux.2, CogVideoX-5B, and LTX-2. Compared with existing serving configurations, Xema improves SLO attainment by up to 3.7x and reduces planning cost from 6.3 hours to 197 seconds compared with grid search.
This paper proposes LayUp, a layer-wise training optimization framework for edge devices with unified memory architectures that achieves speedup and energy reduction compared to baseline GPU-only training for GPT-2 models on the NVIDIA Jetson Orin NX, while conventional offloading increases latency.
Bang-San Lee, Young-Ho Gong· Proceedings of the ACM/IEEE...· 0 citations
High-resolution video diffusion models built on Diffusion Transformers (DiTs) deliver strong fidelity but quickly exhaust the memory budget of a single workstation. A 100 billion-plus parameter DiT easily requires over a terabyte of persistent state, while naive spatiotemporal self-attention grows quadratically in sequence length. These two walls -- parameter memory and activation memory -- prevent researchers from adapting massive generative models without large GPU clusters. We revisit this problem from a systems perspective and introduce MegaSlide-DiT, a prototype that demonstrates how a pre-trained 105B DiT can be adapted on a single H200 GPU with 1.5 TB of host RAM. Our key insight is that the GPU need not own the model state: all persistent weights, master weights and optimizer moments remain in host memory, while only transient shards are streamed to the GPU on demand. Simultaneously, we replace quadratic global attention with 3D Deformable Slide Attention (3D-DSA), a motion-adaptive local attention operator that reduces both memory and computational complexity to linear in the sequence length. We report detailed memory accounting, execution traces and evaluation results to substantiate our design. MegaSlide-DiT does not claim to train a 105B model from scratch on a single GPU, nor does it magically solve bandwidth limits; rather, it offers a pragmatic path for full-parameter adaptation of massive video diffusion models on high-end workstations.
Scaling inference for large language models is increasingly constrained by limited GPU memory, primarily due to the expanding intermediate states (KV caches) required for long-context generation and multi-user workloads. Once the KV cache exceeds the capacity of high-bandwidth memory, it must be offloaded to host memory and reloaded on demand, a workflow severely bottlenecked by the CPU–GPU interconnect, typically PCIe. Existing approaches exploiting offload KV caches to CPU memory and selectively reload partial segments for attention computation often underutilize CPU compute resources and suffer from accuracy degradation. We present Beyond, a drop-in runtime that integrates a smart offloading scheme to selectively identify and retain salient KV entries across continuous decoding sessions, together with a hybrid CPU–GPU attention mechanism for scalable inference. Beyond executes dense attention over recent KV entries stored in GPU memory while performing parallel, per-head sparse attention on salient contextual KV entries residing in CPU memory. The outputs are fused efficiently through a log-sum-exp scheme. During the bandwidth-constrained decoding phase, oversized KV caches are processed cooperatively by the aggregated CPU and GPU memory bandwidth, with only minimal PCIe data movement. Experiments across diverse models and workloads demonstrate that Beyond improves scalability, supports longer sequences and larger batch sizes, and outperforms existing sparse attention baselines in both efficiency and accuracy—all on commodity GPU hardware.
Weishu Deng, Yujie Yang, Peiran Du et al.· IEEE International Symposium...· 1 citation
The rapid growth of Large Language Models has outpaced GPU memory capacity, making state-of-the-art models unfit for most GPUs. This necessitates evicting model parameters to CPU memory or disk, further away from the processing elements. In these scenarios, inference throughput is restricted by interconnect bandwidth, and while quantization schemes like FP8 reduce memory usage and enable larger models to fit smaller memories, they compromise generation quality and rely on specialized hardware support for efficient inference. To this end, we propose an inference pipeline that utilizes lossless entropy encoding (rANS) alleviating the bottleneck while maintaining standard floating-point portability or output precision. We demonstrate the potential of this approach by integrating the pipeline into vLLM, with a hardware-aware parallelization strategy. Our method increases the effective GPU memory capacity up to 30% in inference workloads with evicted weights, with no loss of quality and at the same time achieving speed up of 2.5–4.4%.
Väinö-Waltteri Granat, Jakub Žádník, M. Mäkitalo et al.· IEEE International Conferenc...· 0 citations
Prism abstracts the highly dynamic diffusion workload into a predictable, static execution flow transparent to the compiler, and achieves this via three techniques: spatial regularization, temporal stabilization, and specialized kernels that selectively bypass padding data.
Jianian Zhu, Hang Wu, Yinghui Li et al.· International Conference on...· 0 citations
This work introduces a tile-aware scheduling framework for efficient sparse Vision Transformer execution on GPUs and introduces a training-aware extension that reuses the inference tile schedule and augments it with backward computation and activation-memory strategies.
Changxin Li· IEEE International Symposium...· 0 citations