AITOPAI热报
How-toOfficialSelected

Curvine在SageMaker HyperPod上实现分层KV缓存

Tiered KV cache for large LLMs on Amazon SageMaker HyperPod with Curvine

Primary sourceAWS Machine Learning Blog@Qingyuan Tang
Blog/Media
Read the original
Why we picked it

AWS官方教你用Curvine在HyperPod上搞分层KV缓存，省GPU内存还能提速，跑大模型推理的可以看看。

AWS博客介绍了一种在Amazon SageMaker HyperPod上使用Curvine构建分层KV缓存的方法。

Image · AWS Machine Learning Blog
Body

该方法将KV缓存扩展到共享分布式NVMe池中，使副本能以接近本地磁盘的速度复用缓存。这解决了大规模LLM推理中GPU实例过大或首token延迟高的问题。

通过使用成本更高效的实例，该方法在保持性能的同时降低了推理成本。

Original · AWS Machine Learning Blog

Tiered KV cache for large LLMs on Amazon SageMaker HyperPod with Curvine

Running large language model inference at scale forces a KV cache trade-off: oversized GPU instances or slow time-to-first-token. This post builds a tiered KV cache on Amazon SageMaker HyperPod that extends the cache into a shared, distributed NVMe pool with Curvine, so replicas reuse cache at near-local-disk speeds on cost-efficient instances.

Industry Coordinates & Source Verification

This report is categorized under AITOP's "How-to" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include SageMaker HyperPod, Curvine, KV缓存, 推理优化.

Primary source verification: published directly by AWS Machine Learning Blog with first-party authority.

#SageMaker HyperPod#Curvine#KV缓存#推理优化

Keep reading

Recent stories with the same tag or section
How-toBuildersOfficial07:33
Chinese content在 Amazon SageMaker HyperPod 上检测与恢复静默数据损坏

大规模训练最怕的不报错的 GPU 静默故障，这篇 AWS 博客直接给你三段能塞进 PyTorch DDP 循环的检测代码，还能自动定位坏节点并触发 HyperPod 替换。跑多节点训练的工程师建议照着改一下自己的训练脚本。

#SageMaker HyperPod#PyTorch#GPU故障排查#分布式训练
Event
Primary sourceAWS Machine Learning Blog@Siddharth SengerSource
How-toBuildersOfficialSelected01:52
GPT-OSS-20B fine-tuned for multilingual reasoning on SageMaker HyperPod

The real lesson is the decision framework: pick SFT when accuracy targets are clear, and add GRPO only when benchmarks show remaining headroom, avoiding wasted RL compute. Practitioners building multilingual models on their own data can replicate the workflow on SageMaker HyperPod with open weights, no dependency on a closed API.

#GPT-OSS-20B#Amazon SageMaker#fine-tuning#reasoning models
Primary sourceAWS Machine Learning Blog@Mijanur PalashSource
How-toBuildersOfficialSelected00:24
AWS shows SkyRL recipe for post-training Qwen3-VL-8B with GRPO

Multimodal RL post-training of open VLMs is becoming a reproducible recipe rather than a research one-off, with the trained LoRA adapter ready to deploy directly on SageMaker. Teams building vision-language applications can follow the steps to fine-tune Qwen3-VL-8B on their own data using GRPO without managing infrastructure manually. It also gives Qwen a concrete deployment path on AWS infrastructure.

#SkyRL#Qwen3-VL-8B#SageMaker HyperPod#GRPO
Primary sourceAWS Machine Learning Blog@Nilesh PSSource
How-toBuildersOfficialSelected00:00
Amazon SageMaker HyperPod enables cross-Region training with Qumulo

The non-obvious finding is that cross-Region data locality is no longer a hard blocker: after NeuralCache warms up, remote throughput converges with co-located performance, letting teams decouple storage placement from GPU availability. ML engineers choosing AWS infrastructure can use the published architecture to build across Regions when local GPU capacity is scarce.

#Amazon SageMaker HyperPod#Qumulo#AWS#fine-tuning
Primary sourceAWS Machine Learning Blog@Bryan BerezdivinSource