BentoML has just lately launched llm-optimizer, an open-source framework designed to streamline the benchmarking and efficiency tuning of self-hosted giant language fashions (LLMs). The instrument addresses a standard problem in LLM deployment: discovering optimum configurations for latency, throughput, and price with out counting on handbook trial-and-error. Why is tuning the LLM efficiency troublesome? Tuning LLM inference is a balancing act throughout many transferring elements—batch dimension, framework alternative (vLLM, SGLang, and so forth.), tensor parallelism, sequence lengths, and the way nicely the {hardware} is utilized. Every of those components can shift efficiency in several methods, which makes discovering the appropriate mixture for pace, effectivity, and price removed from easy.

A practical handbook for engineers building, optimizing, scaling and operating LLM inference systems in production.

BentoML udostępniło llm-optimizer, narzędzie open-source, które ma na celu optymalizację i benchmarking self-hosted dużych modeli językowych (LLM). Narzędzie to odpowiada na wyzwania związane z wdrażaniem LLM, umożliwiając znalezienie optymalnych konfiguracji pod kątem latencji, przepustowości i kosztów.

Critical Remote Code Execution (RCE) vulnerability, CVE-2025-27520 with a CVSSv3 base score of 9.8, has been recently discovered in BentoML.

LLM推論に関する技術のメモ記事です。APIを利用するのではなく、どちらかいうと内部の技術に焦点を当てています。

Operating LLMs in production. Contribute to bentoml/OpenLLM development by creating an account on GitHub.

BentoML has recently released llm-optimizer, an open-source framework designed to streamline the benchmarking and performance tuning of self-hosted large language models (LLMs). The tool addresses a common challenge in LLM deployment: finding optimal configurations for latency, throughput, and cost without relying on manual trial-and-error. Why is tuning the LLM performance difficult? Tuning LLM inference is a balancing act across many moving parts—batch size, framework choice (vLLM, SGLang, etc.), tensor parallelism, sequence lengths, and how well the hardware is utilized.

Our stack to go from experimenting to production won't have any more secrets for you.

An open platform for operating large language models (LLMs) in production. Fine-tune, serve, deploy, and monitor any LLMs with ease. - GitHub - bentoml/OpenLLM: An open platform for operating large...

Learn about CVE-2025-27520, a security vulnerability in BentoML that allows remote code execution due to insecure deserialization.

Benchmark and optimize LLM inference across frameworks with ease - bentoml/llm-optimizer

Vulnerability-Lookup - Fast vulnerability lookup correlation from different sources.

Origin

BentoML has recently released llm-optimizer, an open-source framework designed to streamline the benchmarking and performance tuning of self-hosted large language models (LLMs). The tool addresses a common challenge in LLM deployment: finding optimal configurations for latency, throughput, and cost without relying on manual trial-and-error. Why is tuning the LLM performance difficult? Tuning LLM inference is a balancing act across many moving parts—batch size, framework choice (vLLM, SGLang, etc.), tensor parallelism, sequence lengths, and how well the hardware is utilized.

Critical Remote Code Execution (RCE) vulnerability, CVE-2025-27520 with a CVSSv3 base score of 9.8, has been recently discovered in BentoML.

Origin

View detailed information about CVE-2024-9070 on CyberAlerts

BentoML is a Python library for building online serving systems optimized for AI apps and model inference. Prior to 1.4.8, there was an insecure deserialization in BentoML's runner server. By setting specific headers and parameters in the POST request, it is possible to execute any unauthorized arbitrary code on the …

LLM推論に関する技術のメモ記事です。APIを利用するのではなく、どちらかいうと内部の技術に焦点を当てています。

This project offers an API for vectorizing images using OpenAI's CLIP model via BentoML and integrates Milvus for efficient vector storage and search - gordonmurray/bentoml-image-vectorization