AITOPAI热报
ProductsMulti-sourceSelected

vLLM Project Shows KV Cache and Kernel Optimizations at PyTorchCon 2026

官方账号PyTorch@PyTorch
Post· 11 sources
View the post
Why we picked it

vLLM now spans three hardware ecosystems, which reduces the lock-in that previously tied inference stacks to NVIDIA GPUs. Teams running self-hosted inference can benchmark vLLM against vendor-specific runtimes on AMD and TPU infrastructure. The KV cache management work matters most for serving long-context models where memory, not compute, is the bottleneck.

Simon Mo of Inferact presented the state of the vLLM project at PyTorchCon 2026. The talk highlighted optimizations in KV cache management and kernels across TPU, AMD, and NVIDIA hardware. These efforts address performance and scalability challenges as LLMs grow in model size, context length, and architectural complexity.

Original · PyTorch

Simon Mo (@simon_mo_) of @inferact presents the state of @vllm_project at #PyTorchCon 2026, highlighting optimizations in KV cache management and kernels across TPU, AMD, and NVIDIA hardware.

The work addresses performance and scalability challenges as LLMs grow in size, context length, and architectural complexity.

#vLLM#PyTorch#KV cache#inference

Keep reading

Recent stories with the same tag or section
ProductsBuildersMulti-sourceSelected04:07
PyTorchCon Demo Theater: 13 Sessions on Training, Inference and Agents

The lineup reveals where the PyTorch ecosystem is concentrating effort: inference speed for video models, MoE expert parallelism on NVL72, and fault-tolerant training that survives link and node failures live. Engineers choosing accelerator backends can use these demos to compare AWS Trainium, Intel AI PCs, TPUs and Rebellions hardware on real workloads. The Huawei CRCR CI relay is also a practical pattern for teams maintaining forks against upstream PyTorch.

#PyTorch#PyTorchCon#Wan 2.2#vLLM
Event
官方账号PyTorch@PyTorch11 sourcesSource
ProductsBuildersSelected02:54
vLLM-TorchTPU brings native PyTorch serving to Google TPUs

The practical effect is that vLLM's optimizations — prefix caching and speculative decoding — now apply to TPU hardware, giving teams a second accelerator option beyond NVIDIA GPUs for self-hosted inference. Organisations already locked into the PyTorch and vLLM stack can evaluate TPU economics without rewriting their serving code. It also signals Google's continued push to open TPU inference beyond JAX.

#vLLM#TPU#PyTorch#Google
官方账号vLLM@vllm_projectSource
ProductsBuildersSelected02:01
FlyDSL kernel DSL now supports PyTorch on AMD MI350 GPUs

AMD MI350 support matters because GPU kernel tooling has historically centred on NVIDIA's stack, and this gives teams serving open models a Python-native path to tune kernels without CUDA-only tools. Benchmark and deployment engineers running vLLM on AMD accelerators can adopt the DSL directly from PyTorch's ecosystem and measure the 3.01x serving gain against their own baselines.

#FlyDSL#PyTorch#AMD#vLLM
官方账号PyTorch@PyTorchSource
IndustryBuildersSelected01:07
Cohere's Rachad Alao on open science for foundation models

The talk links open model releases to concrete security practices like independent auditing and dynamic safeguards, rather than treating openness and safety as opposing goals. Researchers and ML engineers can follow the named stack — PyTorch, TorchTitan and vLLM — to reproduce or audit open models themselves. Cohere's stance is notable as a US-listed enterprise lab that publicly backs open science. origin

#Cohere#PyTorch#TorchTitan#vLLM
官方账号PyTorch@PyTorchSource