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BentoML introduces llm-optimizer, an open-source tool for systematic LLM benchmarking, performance tuning, and reproducible comparisons

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.

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.

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.

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.

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.

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.

Origin

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

github_stars pypi_status actions_status documentation_status join_slack BentoML is a Unified Inference Platform for deploying and scaling AI systems with any model, on any cloud. Featured examples:...

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機械学習モデルのデプロイに利用されるフレームワーク「BentoML」に脆弱性が明らかとなった。アップデートで修正されている。 ：Security NEXT

58 new CVEs published on 2025-07-30 (CVSS: 7.0 - 9.9)

BentoML is a Python library for building online serving systems optimized for AI apps and model inference. In versions 1.4.0 until 1.4.19, the file upload processing system contains an SSRF vulnerability that allows unauthenticated remote attackers to force the server to make arbitrary HTTP requests. The vulnerability stems from the …