From 71a125428b6f85502b3f9ba042d33e81472605d4 Mon Sep 17 00:00:00 2001 From: Andrey Cheptsov Date: Thu, 20 Aug 2026 15:56:17 +0200 Subject: [PATCH] [Blog] Tighten the presets post intro Co-Authored-By: Claude Opus 5 (1M context) --- mkdocs/blog/posts/presets.md | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/mkdocs/blog/posts/presets.md b/mkdocs/blog/posts/presets.md index aff7436e2..c5bca9ad3 100644 --- a/mkdocs/blog/posts/presets.md +++ b/mkdocs/blog/posts/presets.md @@ -1,7 +1,7 @@ --- title: "Presets: a toolkit for agent-based inference optimization" date: 2026-08-20 -description: "A preview of presets: a toolkit that streamlines agent-based inference optimization, and a portable format that deploys the result to any cloud or datacenter." +description: "A preview of presets: an open-source toolkit that streamlines inference optimization with agents, and a portable preset format." slug: presets image: https://dstack.ai/static-assets/static-assets/images/dstack-presets.png categories: @@ -12,15 +12,13 @@ categories: Optimizing model inference is agent work now. Every inference provider does it inside its own process, on its own serving stack, with its own harness around the optimization agent. Despite the progress in open-source serving frameworks, what gets published is a benchmark, often without the workload, the concurrency, and the hardware behind it. The optimized deployment itself stays tied to the stack that produced it. -Today we're introducing a preview of presets: an open-source toolkit that streamlines inference optimization with agents, and a portable preset format that can be deployed to any cloud, Kubernetes cluster, or bare-metal fleet. +Today we're introducing a preview of presets: an open-source toolkit that streamlines inference optimization with agents, and a portable preset format. -Despite the progress in open-source serving frameworks, there is still no way to get optimization and deployment that delivers portable performance. That work happens inside each inference provider's own proprietary stack, with its own harness around the agent. This has to change. - -Deploying Kimi K3 should be as simple as deploying a Docker image, on any silicon and in any datacenter. Taking a model someone has already optimized and running it on your own hardware should not mean rebuilding the stack or repeating the optimization, whichever silicon you have. +Deploying Kimi K3 to any cloud, Kubernetes cluster, or bare-metal fleet should be as simple as deploying a Docker image. Taking a model someone has already optimized and running it on your own hardware should not mean rebuilding the stack or repeating the optimization, whichever silicon you have. ## Toolkit and portable format