# benchmarking Computing discipline of measuring computer performance with defined workloads and metrics. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## TensorRT Edge-LLM Completes the MLPerf Edge Agentic Benchmark 6.4x Faster on Jetson AGX Thor DevFeed: [TensorRT Edge-LLM Completes the MLPerf Edge Agentic Benchmark 6.4x Faster on Jetson AGX Thor]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-16T20:37:07Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Jetson AGX Thor Developer Kit](), [Inference Performance](), [benchmarking](), [TensorRT](), [Large Language Model](), [quantization](), [Artificial Intelligence]() Tags: [agentic-ai-generative-ai](), [ai-agent](), [ai-inference](), [evaluation](), [jetson](), [jetson-agx-thor-developer-kit](), [llm](), [llm-benchmarking](), [mlperf](), [nvidia](), [performance](), [quantization](), [tensorrt](), [tensorrt-llm](), [thor]() ### AI overview This article reports that NVIDIA TensorRT Edge-LLM ran Qwen3.6-27B on a single NVIDIA Jetson AGX Thor Developer Kit for the MLPerf Inference v6.1 Edge Agentic benchmark. Using NVFP4 quantization, tree-based multi-token prediction, and KV cache reuse, it achieved 52.33 tokens per second and completed 1,007 turns in 24 minutes and 36 seconds, 6.4 times faster than the llama.cpp reference submission. ### Source excerpt AI agents are moving from cloud data centers to vehicles, robots, and other edge devices. Unlike a chatbot that answers a single prompt, an agent works through... ## First VMmark 4.1 Power-Performance and VMware Cloud Foundation 9.1 Results DevFeed: [First VMmark 4.1 Power-Performance and VMware Cloud Foundation 9.1 Results]() Original publisher: [Read original article]() Author: vmwareblogs Published: 2026-09-16T18:20:25Z Content type: release Language: en Sources: [VMware Blogs]() Topics: [vcf 9.1](), [benchmarking](), [Benchmark](), [virtualization](), [Hardware]() Tags: [benchmarking](), [benchmarks](), [cloud-infrastructure](), [dell](), [efficiency](), [home-page](), [performance](), [vcf-9-1](), [vmmark](), [vmware](), [vmware-cloud-foundation](), [vsphere](), [vsphere-9-1]() ### AI overview VMware reports Dell Technologies benchmark results using VMware Cloud Foundation 9.1 and VMmark 4.1. The article describes VMmark 4.1's power-performance measurement and reports higher performance and tile count for VCF 9.1 than VCF 5.2 in the tested environment. ### Source excerpt We're excited to announce two new VMmark results today from Dell Technologies: First VCF 9.1 Benchmarks: These are the first results using VMware Cloud Foundation (VCF) 9.1. VCF 9.1 maximizes hardware efficiency using a Next-Gen Topology-Aware CPU Scheduler that optimizes memory and cache locality for intensive enterprise workloads. A separate VCF 9.1 evaluation demonstrated a ... Continued The post First VMmark 4.1 Power-Performance and VMware Cloud Foundation 9.1 Results appeared first on VMware Blogs. ## Translating CUDA Tile Operations from Python to Rust Using Agentic AI DevFeed: [Translating CUDA Tile Operations from Python to Rust Using Agentic AI]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-09-16T16:28:59Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [CUDA Tile](), [Rust](), [Agent Skill](), [Artificial Intelligence](), [AI Agent](), [GPU](), [benchmarking](), [Compiler]() Tags: [agent-skill](), [agentic-ai](), [agentic-ai-generative-ai](), [ai-agent](), [benchmarking](), [cuda](), [cuda-tile](), [cutile](), [developer-tools-techniques](), [gpu](), [mixture-of-experts-moe](), [python](), [rust]() ### AI overview This NVIDIA developer article explains a multi-agent workflow for translating cuTile Python and Triton-TileIR GPU kernels into cuTile Rust. The team ported 24 public TileGym operators, covering about 40 kernels, and achieved 99.5% of cuTile Python performance on average, with correctness and performance checks at each stage. ### Source excerpt cuTile Rust (cutile-rs) is a tile-based system for safe, idiomatic GPU kernel authoring in the Rust programming language. Extending the Rust ownership model to... ## NVIDIA Vera Rubin NVL72 Delivers Leading Performance in MLPerf Inference v6.1 Debut DevFeed: [NVIDIA Vera Rubin NVL72 Delivers Leading Performance in MLPerf Inference v6.1 Debut]() Original publisher: [Read original article]() Author: Zhihan Jiang Published: 2026-09-16T15:00:48Z Content type: article Language: en Sources: [NVIDIA Blog]() Topics: [NVIDIA Vera Rubin](), [Vera Rubin NVL72](), [benchmarking](), [Inference Performance](), [Dynamo](), [vllm](), [Nvidia](), [TensorRT-LLM]() Tags: [ai-infrastructure](), [benchmarks](), [dynamo](), [efficiency](), [hardware](), [inference](), [mlperf](), [networking](), [nvidia](), [nvidia-vera-rubin](), [nvl72](), [optimization](), [performance](), [software](), [tensorrt](), [tensorrt-llm](), [vera-rubin-nvl72](), [vllm]() ### AI overview NVIDIA reports MLPerf Inference v6.1 preview results for Vera Rubin NVL72 and GB300 NVL72 systems. Vera Rubin NVL72 delivered up to 3.7x higher throughput than GB300 NVL72 on Qwen3-VL and up to 2.5x higher throughput on DeepSeek-R1, while a four-rack GB300 NVL72 submission achieved 99% scaling efficiency. The results used vLLM, NVIDIA Dynamo, and TensorRT-LLM. ### Source excerpt System performance, efficient infrastructure scaling and continuous software optimization are key levers that determine AI inference economics. Higher system performance means more tokens generated, resulting in higher revenue. Efficient scaling means throughput grows proportionally as hardware gets added, requiring fewer resources to serve users at scale. Continuous optimization means generating more value from infrastructure investments. [...] ## Linux Randomly Picking Idle CPU Cores Shows Modest Win On High Core Count Systems DevFeed: [Linux Randomly Picking Idle CPU Cores Shows Modest Win On High Core Count Systems]() Original publisher: [Read original article]() Author: Michael Larabel Published: 2026-09-16T13:30:04Z Content type: news Language: en Sources: [Phoronix]() Topics: [Linux](), [Linux Kernel](), [cpu](), [benchmarking](), [systems]() Tags: [benchmarking](), [cpu](), [desktop-linux](), [linux](), [linux-benchmarking](), [linux-hardware-benchmarks](), [linux-hardware-reviews](), [linux-how-to](), [linux-kernel](), [linux-performance](), [linux-server-benchmarks](), [open-source-graphics](), [patches](), [phoronix](), [phoronix-test-suite](), [scheduler](), [ubuntu-benchmarks](), [ubuntu-hardware]() ### AI overview A Linux kernel patch series proposes randomizing the selection among equally shallow idle CPU cores to reduce scan-order bias and prevent concurrent tasks from converging on the same core. Testing on a 160-core Ampere Altra system showed throughput improvements of up to a few percent in Stress-NG benchmarking, while the benefits on smaller systems remain uncertain. ### Source excerpt Arm engineer Christian Loehle sent out a patch series today proposing that the Linux scheduler code randomize its picking of equally shallow idle CPU cores. This randomization is showing to deliver a win at least for high CPU core counts for better distributing workloads... ## Emerald AI, Google and NVIDIA Launch Alliance to Advance Flexible AI Data Centers DevFeed: [Emerald AI, Google and NVIDIA Launch Alliance to Advance Flexible AI Data Centers]() Original publisher: [Read original article]() Author: Josh Parker Published: 2026-09-16T13:00:33Z Content type: news Language: en Sources: [NVIDIA Blog]() Topics: [data centers](), [AI Infrastructure](), [Google](), [Nvidia](), [Artificial Intelligence](), [datacenter](), [benchmarking](), [Requirements]() Tags: [ai](), [ai-data-centers](), [ai-infrastructure](), [capacity](), [corporate](), [data-centers](), [energy](), [google](), [infrastructure](), [launch](), [metrics](), [nvidia](), [performance-metrics](), [requirements](), [resource](), [systems]() ### AI overview Emerald AI, Google and NVIDIA announced the AI Energy Management Alliance, a coalition focused on flexible AI data centers that can dynamically adjust electricity use in response to grid conditions. The article describes technology-neutral, performance-based requirements covering response speed, duration, predictability and emergency behavior. ### Source excerpt AI factories are the infrastructure of the intelligence era. Scaling them responsibly will depend as much on innovation across the grid as inside the data center. Today, Emerald AI, Google and NVIDIA announced the launch of the AI Energy Management Alliance (AEMA), a first-of-its-kind coalition advancing data centers that can dynamically manage their electricity use [...] ## Introducing TIN: full-text search for Postgres DevFeed: [Introducing TIN: full-text search for Postgres]() Original publisher: [Read original article]() Author: Patrick Reynolds Published: 2026-09-16T00:00:00Z Content type: release Language: en Sources: [Blog -- PlanetScale]() Topics: [Databases](), [benchmarking]() Tags: [backups](), [benchmark](), [benchmarks](), [bm25](), [engineering](), [full-text-search](), [index](), [performance](), [postgres](), [reddit](), [replication](), [search](), [text](), [wikipedia]() ### AI overview PlanetScale announces TIN, a full-text search extension for Postgres and Neki databases. The article describes supported query and matching features, transaction and update behavior, and benchmark workloads and corpora used to assess performance. ### Source excerpt TIN is a fast, full-featured, full-text search index for Postgres ## Linux 7.3 Delivering Some Performance Gains On Intel Panther Lake / Framework Laptop 13 Pro DevFeed: [Linux 7.3 Delivering Some Performance Gains On Intel Panther Lake / Framework Laptop 13 Pro]() Original publisher: [Read original article]() Author: Michael Larabel Published: 2026-09-14T14:42:00Z Content type: article Language: en Sources: [Phoronix]() Topics: [Linux](), [benchmarking](), [intel](), [Hardware](), [Ubuntu]() Tags: [benchmarking](), [desktop-linux](), [hardware](), [intel-core](), [kernel](), [linux](), [linux-benchmarking](), [linux-hardware-benchmarks](), [linux-hardware-reviews](), [linux-how-to](), [linux-performance](), [linux-server-benchmarks](), [open-source-graphics](), [performance](), [phoronix](), [phoronix-test-suite](), [ubuntu](), [ubuntu-benchmarks](), [ubuntu-hardware]() ### AI overview The article benchmarks the in-development Linux 7.3 kernel against stable Linux 7.2 on an Intel Core Ultra X9 388H in a Framework Laptop 13 Pro running Ubuntu 26.04 LTS. It reports performance gains in some areas without concerning regressions. ### Source excerpt Linux 7.1 brought some performance improvements for Intel Core Ultra Series 3 "Panther Lake", Linux 7.2 enhanced the performance for the integrated Arc B390 Xe3 graphics, and now for the in-development Linux 7.3 kernel are various performance improvements at large. Today's article is looking at the performance of the in-development Linux 7.3 kernel compared to Linux 7.2 stable using the Core Ultra X9 388H within the Framework Laptop 13 Pro. ## Coding Challenge #136 - Hyperfine DevFeed: [Coding Challenge #136 - Hyperfine]() Original publisher: [Read original article]() Author: John Crickett Published: 2026-09-12T08:03:11Z Content type: tutorial Language: en Sources: [Coding Challenges]() Topics: [benchmarking](), [coding](), [Command-line interface](), [Processes]() Tags: [benchmarking](), [coding](), [command-line](), [performance](), [process](), [programming]() ### AI overview A coding challenge asks readers to build a command-line benchmarking tool modeled on hyperfine. The tool should run commands repeatedly, measure execution time and variation, compare commands, export results, and warn about unreliable measurements. ### Source excerpt This challenge is to build your own benchmarking tool. ## What Go Taught Us About Java Garbage Collection DevFeed: [What Go Taught Us About Java Garbage Collection]() Original publisher: [Read original article]() Author: Shai Almog Published: 2026-09-12T00:00:00Z Content type: opinion Language: en Sources: [CodeName One]() Topics: [Go Language](), [Java](), [benchmarking]() Tags: [comparison](), [garbage-collection](), [go](), [java](), [latency](), [memory](), [performance]() ### AI overview A comparison between ParparVM and Go prompts an investigation into Java garbage-collection pacing, allocation thresholds, and memory use. Lowering ParparVM's allocation threshold reduced measured process resident memory from 98 MB to 38 MB, while throughput and p99 latency changed little in the reported test. ### Source excerpt A Go performance comparison led us from stack allocation to GC pacing, parallel marking, and image caches. ParparVM can explore those choices while keeping ordinary Java APIs. ## CachyOS vs. Windows 11 vs. Ubuntu 26.04 LTS On Intel Wildcat Lake + 8GB RAM DevFeed: [CachyOS vs. Windows 11 vs. Ubuntu 26.04 LTS On Intel Wildcat Lake + 8GB RAM]() Original publisher: [Read original article]() Author: Michael Larabel Published: 2026-09-11T15:08:55Z Content type: comparison Language: en Sources: [Phoronix]() Topics: [benchmarking](), [Linux](), [Ubuntu](), [Windows](), [cpu](), [Hardware](), [intel](), [Linux performance]() Tags: [benchmarking](), [desktop-linux](), [hardware](), [intel](), [intel-core](), [laptop](), [linux](), [linux-benchmarking](), [linux-hardware-benchmarks](), [linux-hardware-reviews](), [linux-how-to](), [linux-performance](), [linux-server-benchmarks](), [lts](), [memory](), [microsoft](), [nvme](), [open-source-graphics](), [performance](), [phoronix](), [phoronix-test-suite](), [release](), [review](), [ssd](), [testing](), [ubuntu](), [ubuntu-benchmarks](), [ubuntu-hardware](), [windows](), [windows-11]() ### AI overview This comparison measures Ubuntu 26.04 LTS, CachyOS, and Windows 11 performance on a $449 CHUWI UniBook laptop with an Intel Core 3 Wildcat Lake processor, 8GB of memory, and a 256GB NVMe SSD across various workloads. ### Source excerpt Often times when testing different Linux distributions or comparing Windows vs. Linux it's on leading flagship desktop or server hardware, but today we are looking at the Ubuntu vs. CachyOS vs. Windows performance at the opposite end of the spectrum. With the new CHUWI UniBook $449 laptop powered by Intel Core 3 Wildcat Lake and with 8GB of system memory, here is a look at how those three operating systems compare across a variety of workloads. ## 118 million queries per second on Neki DevFeed: [118 million queries per second on Neki]() Original publisher: [Read original article]() Author: Hirad Pourtahmasbi Published: 2026-09-11T00:00:00Z Content type: article Language: en Sources: [Blog -- PlanetScale]() Topics: [Database](), [benchmarking](), [Latency]() Tags: [benchmark](), [database](), [engineering](), [errors](), [latency](), [neki](), [postgres]() ### AI overview The article reports a Neki benchmark that sustained 118 million queries per second across 512 primary-only Postgres shards holding 1.22 PiB of data. The read-only, single-shard point-select workload ran for 16 minutes, with reported router and client p99 latency plus fleet IOPS, network throughput, and error rate. ### Source excerpt We ran a massive, sharded Postgres database at 118.5 million queries per second, with 200k queries per second on each shard across 512 shards. ## IBM releases SOTA Granite Time Series PatchTST-FM-r2 model with commercial-friendly license DevFeed: [IBM releases SOTA Granite Time Series PatchTST-FM-r2 model with commercial-friendly license]() Original publisher: [Read original article]() Author: Roman Vaculin; Wesley M Gifford; Jiri Navratil; Chandra Reddy; Ayhan Sebin Published: 2026-09-09T15:36:24Z Content type: release Language: en Sources: [Hugging Face - Blog]() Topics: [foundation-models](), [Temporal data](), [releases](), [benchmarking](), [Open Source Models & Datasets](), [Inference]() Tags: [apache](), [benchmarking](), [foundation-models](), [ibm](), [inference](), [model-architecture](), [open-source](), [releases](), [time-series](), [zero-shot]() ### AI overview IBM released Granite Time Series PatchTST-FM-r2, a roughly 385M-parameter time-series foundation model for zero-shot forecasting. The article covers its architecture, probabilistic forecasting, missing-value imputation, benchmark results, licensing, and available reproducibility resources. ### Source excerpt Time-series foundation models are changing the way forecasting systems are built. Instead of training and maintaining a separate model for every dataset, users can use a pretrained model and generate forecasts zero-shot. IBM has released Granite Time Series PatchTST-FM-r2, the latest model in the Granite TSFM family (github, blog). ## Python Timer Functions DevFeed: [Python Timer Functions]() Original publisher: [Read original article]() Author: Real Python Published: 2026-09-08T14:00:00Z Content type: tutorial Language: en Sources: [Real Python]() Topics: [Python](), [benchmarking](), [Code]() Tags: [benchmarking](), [python](), [tutorial](), [video]() ### AI overview This video course teaches how to measure Python code execution time with the time module. It builds a reusable Timer class, extends it for multiple timers, converts it into a data class, and uses it as a context manager. ### Source excerpt Learn how to time your Python code with the time module, then build a reusable Timer class that works as a context manager. ## Cybersecurity Benchmarking: Why, Why Not, When and How DevFeed: [Cybersecurity Benchmarking: Why, Why Not, When and How]() Original publisher: [Read original article]() Author: Phil Venables Published: 2026-09-05T15:27:39Z Content type: opinion Language: en Sources: [Risk and Cyber]() Topics: [benchmarking](), [Cybersecurity]() Tags: [benchmarking](), [cybersecurity](), [performance](), [risk]() ### AI overview The article argues that cybersecurity benchmarking is unhelpful when it focuses only on inputs such as budgets instead of outcomes such as control effectiveness. It recommends comparing leading indicators and examining how they influence lagging performance indicators, while noting that budget comparisons may not be meaningfully comparable. ### Source excerpt tl;dr Benchmarking is a waste of time when focused solely on inputs (e.g. budgets) rather than outcomes (e.g. effectiveness of controls). The budget comparisons are never "apples for apples" and may often end up setting risk tolerance only marginally ahead of others who may be in a bad state to begin with. Instead, we need to decouple this and compare leading not lagging indicators of performance to show (i) how those leading indicators drive the lagging indicators in the right direction and... ## Micron 6600 ION 245TB: Swap the Hard Drives, Power an NVL72 for Free DevFeed: [Micron 6600 ION 245TB: Swap the Hard Drives, Power an NVL72 for Free]() Original publisher: [Read original article]() Author: Brian Beeler Published: 2026-09-03T17:00:39Z Content type: article Language: en Sources: [StorageReview.com]() Topics: [Benchmark](), [benchmarking](), [datacenter](), [GB200](), [GPU](), [Server](), [dell]() Tags: [benchmark](), [compute](), [data-center](), [dell](), [efficiency](), [enterprise](), [gb200](), [gpu](), [hdd](), [measurements](), [nvl72](), [performance](), [servers](), [ssd](), [storage]() ### AI overview This article evaluates replacing eight 30TB nearline HDDs with one 245TB Micron 6600 ION SSD. Its measurements indicate lower power consumption, substantially higher read efficiency, and a reduction from 22 racks of HDD storage to six racks of flash at exabyte scale, potentially freeing enough power for a GB200 NVL72. ### Source excerpt For two decades, the SSD-versus-HDD conversation ended the same way: flash wins on performance, disk wins on price per terabyte, and the size of that price gap settled the argument in favor of bulk storage. As storage technology has matured and AI has taken over, that framing is clearly out of date. The largest data The post Micron 6600 ION 245TB: Swap the Hard Drives, Power an NVL72 for Free appeared first on StorageReview.com. ## How we built a benchmarking framework to horizontally accelerate transaction model research DevFeed: [How we built a benchmarking framework to horizontally accelerate transaction model research]() Original publisher: [Read original article]() Author: Nubank Editorial Published: 2026-09-03T13:53:30Z Content type: article Language: en Sources: [Nubank]() Topics: [benchmarking](), [Framework](), [Transformer](), [data]() Tags: [architectures](), [automated](), [benchmarking](), [data](), [data-science-machine-learning](), [experimentation](), [framework](), [model](), [performance](), [pipelines](), [training]() ### AI overview Nubank built an automated benchmarking framework for horizontally evaluating transformer-based transaction representation models across multiple downstream tasks and trials. The framework made experimentation reproducible and statistically rigorous, helping the team identify improvements that generalize across applications. It increased the team's capacity to run experiments by roughly five times per month while reducing operational overhead. ### Source excerpt The framework that transformed weeks of manual experimentation into automated pipelines for horizontal transaction model research The post How we built a benchmarking framework to horizontally accelerate transaction model research appeared first on Building Nubank. ## Expert Evaluation: Identify Problems Before You Recruit Users DevFeed: [Expert Evaluation: Identify Problems Before You Recruit Users]() Original publisher: [Read original article]() Author: William Hudson Published: 2026-09-03T00:00:00Z Content type: article Language: en Sources: [UX Daily - User Experience Daily]() Topics: [Usability](), [User experience (UX)](), [User interface design](), [Interaction Design](), [benchmarking](), [Artificial Intelligence]() Tags: [ai](), [benchmarking](), [cost](), [evaluation](), [interaction-design](), [testing](), [usability](), [ux]() ### AI overview This article explains expert, or heuristic, evaluation as a cost-effective way to identify usability problems before recruiting users. It describes using structured guidelines such as Nielsen's heuristics, extending the method to benchmarking, and treating AI-generated critiques as preliminary input that requires expert review. ### Source excerpt Before you have a single real user in a testing session, you can already find a significant proportion of the usability problems in a design. Expert evaluation, also known as heuristic evaluation, is one of the most cost-effective methods available to UX practitioners. It requires no recruitment, facilities, or scheduling. What it does require is expertise, judgment, and a structured set of guidelines to work from.Here we show how expert evaluation works, walk through Nielsen's widely used set of 10 heuristics, and explain how the same principles can be extended to benchmarking: a more formal process of scoring designs against detailed criteria and comparing performance across sites or over... ## Best Servers 2026: Lab-Tested Picks by Category DevFeed: [Best Servers 2026: Lab-Tested Picks by Category]() Original publisher: [Read original article]() Author: Brian Beeler Published: 2026-09-02T01:19:39Z Content type: comparison Language: en Sources: [StorageReview.com]() Topics: [Server](), [benchmarking](), [Hardware](), [dell](), [idc](), [intel](), [systems]() Tags: [2025](), [2026](), [dell](), [hardware](), [idc](), [infrastructure](), [intel](), [review](), [scale](), [september-2026](), [servers](), [software]() ### AI overview This September 2026 comparison ranks lab-tested servers by category. It identifies the Dell PowerEdge R7725 as the best overall 2U server, the Dell PowerEdge R770 as the best Intel 2U server, and the Dell PowerEdge R7725xd as the best 2U storage server. It also discusses Lenovo and Supermicro alternatives and provides market context from IDC data, including strong growth driven largely by ODM hardware and AI rack systems. ### Source excerpt Published September 2026. Rankings reflect systems tested in the StorageReview lab; market context reflects IDC 1Q 2026 tracker data. The server market set an all-time record in 2025 at $444 billion and opened 2026 with first-quarter revenue up another 30 percent, and almost none of that tells you which server to buy. Half of the The post Best Servers 2026: Lab-Tested Picks by Category appeared first on StorageReview.com. ## BenchMIRT: What are LLM benchmarks actually measuring? DevFeed: [BenchMIRT: What are LLM benchmarks actually measuring?]() Original publisher: [Read original article]() Author: Kyle Wiggers Published: 2026-09-01T21:39:07Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [LLM evaluation / benchmarking](), [Large Language Model](), [benchmarking](), [Jailbreak]() Tags: [benchmarks](), [jailbreak](), [llm](), [reasoning](), [safety]() ### AI overview BenchMIRT is a multidimensional item-response-theory method for auditing what individual prompts in LLM benchmarks measure. It separates capabilities associated with benchmark performance so aggregate scores do not conceal differences among task groups. ### Source excerpt Today we're introducing BenchMIRT, a new method for auditing LLM benchmarks at the level of individual prompts--the questions and tasks a model is scored on. A benchmark is usually designed to measure a particular ability, such as safety, general reasoning, or instruction following. But the individual tasks inside it may depend on more than that stated goal. Take BBQ, a benchmark designed to test whether models rely on social stereotypes. ## The Agentic Analytics Benchmark: Measuring model accuracy and efficiency in analytical agents DevFeed: [The Agentic Analytics Benchmark: Measuring model accuracy and efficiency in analytical agents]() Original publisher: [Read original article]() Author: Eduardo Vellasques; Al Brown Published: 2026-09-01T19:15:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [LLM evaluation / benchmarking](), [benchmarking](), [text2sql](), [Databases](), [Claude](), [deepseek]() Tags: [agentic](), [agents](), [benchmark](), [claude](), [cost](), [deepseek](), [model](), [speed](), [sql]() ### AI overview The article introduces data-agent-mnist, an open benchmark harness for evaluating analytics agents against a data warehouse. It compares 29 models on correctness, cost, speed, and number of turns, and contrasts agentic analytics with conventional text-to-SQL evaluation. ### Source excerpt We took 201 real analytics questions from our production data warehouse, benchmarked 29 models on correctness, cost, and speed, and released an open harness so you can run the same test on your own. ## Introducing @huggingface/kernels: 200+ WebGPU Kernels for Local AI DevFeed: [Introducing @huggingface/kernels: 200+ WebGPU Kernels for Local AI]() Original publisher: [Read original article]() Author: Nico Martin; Joshua Published: 2026-09-01T00:00:00Z Content type: release Language: en Sources: [Hugging Face - Blog]() Topics: [webgpu](), [Local AI](), [Machine learning](), [Inference](), [benchmarking](), [GPU](), [Optimization]() Tags: [benchmark](), [gpu](), [hub](), [inference](), [javascript](), [kernels](), [local-ai](), [open-source](), [optimization](), [shaders](), [testing](), [webgpu]() ### AI overview Hugging Face releases @huggingface/kernels, a JavaScript library and collection of 207 versioned WebGPU kernel packages for browser-based local AI. It also introduces Fleet, a browser benchmarking and testing suite that gathers opted-in performance and correctness evidence across hardware. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## How we saved 100 terabytes of memory by optimizing 1.1.1.1's DNS cache DevFeed: [How we saved 100 terabytes of memory by optimizing 1.1.1.1's DNS cache]() Original publisher: [Read original article]() Author: Sebastiaan Neuteboom Published: 2026-08-27T17:02:35Z Content type: article Language: en Sources: [Cloudflare Blog]() Topics: [Cache](), [Caching](), [Cloudflare](), [Rust](), [benchmarking](), [Latency](), [datacenter]() Tags: [1-1-1-1](), [benchmarking](), [cache](), [cloudflare](), [data-center](), [deep-dive](), [dns](), [engineering](), [latency](), [memory](), [net-maui](), [optimization](), [performance](), [rust]() ### AI overview Cloudflare describes five Rust-level changes to the memory layout of Big Pineapple, the platform behind 1.1.1.1 and other DNS services. The changes reduced DNS cache entry size by over 50%, freed roughly 100 terabytes of memory across the fleet, increased insert throughput by 43%, and reduced lookup latency by 19%. ### Source excerpt Five Rust-level memory optimizations to the DNS cache layout of Big Pineapple cut per-entry memory by 56%, freeing approximately 100 TB of memory across Cloudflare's fleet. ## Benchmarking vector indexes DevFeed: [Benchmarking vector indexes]() Original publisher: [Read original article]() Author: Evgeniy Patlan Published: 2026-08-27T13:35:32Z Content type: article Language: en Sources: [Blog - Percona]() Topics: [benchmarking](), [Ground truth / benchmark quality](), [Databases](), [ann](), [datasets](), [Sorting]() Tags: [ai-vector](), [ann](), [benchmark](), [benchmarking](), [benchmarks](), [blog](), [blog-post](), [data](), [database-performance](), [database-trends](), [databases](), [datasets](), [embeddings](), [index](), [measurements](), [model](), [open-source](), [percona](), [points](), [report](), [run](), [search](), [sorting](), [vector-search](), [vectorsearch]() ### AI overview This article explains how Percona's vector-bench measures vector indexes by building database engines from pinned versions, running them in identical containers with the same data and hardware, and comparing consistent measurements. It describes embeddings, exact search, approximate nearest neighbour search, and ground truth as the brute-force reference needed to evaluate index accuracy. ### Source excerpt Nearly every database has vector search now, and every one of them has a blog post with a big number in it. Almost none of those numbers can be checked, because the thing that makes them meaningful is usually missing. We built a vector-bench to stop guessing. You name the engines you want, build them ... Continued The post Benchmarking vector indexes appeared first on Percona. [Next page]()