# mlx Published articles for mlx. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Native-speed vLLM transformers modeling backend DevFeed: [Native-speed vLLM transformers modeling backend]() Original publisher: [Read original article]() Author: Harry Mellor; Lysandre Published: 2026-07-08T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [vllm](), [Inference Performance](), [Inference](), [LLMs](), [Machine learning](), [Open Source]() Tags: [architecture](), [batching](), [gpu](), [inference](), [inference-performance](), [llama](), [llama-cpp](), [llms](), [mixture-of-experts](), [mlx](), [open-source](), [optimization](), [sglang](), [transformers](), [vllm]() ### AI overview This article describes the transformers modeling backend for vLLM and its role in serving Hugging Face models without requiring model authors to port their implementations. It explains how transformers supplies modeling code while vLLM contributes optimized inference techniques, and outlines comparisons with vLLM's native implementations across several Qwen3 model configurations. It also discusses inference-performance factors including GPU parallelization, compilation, fused kernels, and custom attention kernels. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Viability of local models for coding DevFeed: [Viability of local models for coding]() Original publisher: [Read original article]() Author: Martin Fowler (martin@martinfowler.com) Published: 2026-07-07T12:34:00Z Content type: article Language: en Sources: [Martin Fowler]() Topics: [coding](), [Large Language Model](), [agentic-coding](), [Hardware](), [quantization](), [MLX](), [Tooling]() Tags: [agentic-coding](), [coding](), [hardware](), [llms](), [local](), [mlx](), [quantization](), [tooling]() ### AI overview This memo examines how viable local language models are for coding, with particular attention to agentic coding rather than autocomplete. It discusses hardware constraints, model size, context windows, response speed, quantization, runtimes, and tooling. The author reports that tool calling remains unreliable but that models can often recover from failures. ### Source excerpt Birgitta Böckeler recently spent some time trying out running local LLMs for some programming tasks. In this memo she outlines the factors that influence how viable they are for the job. more... ## What's new in Swift: May 2026 Edition DevFeed: [What's new in Swift: May 2026 Edition]() Original publisher: [Read original article]() Author: Dave Lester Published: 2026-06-03T21:45:00Z Content type: article Language: en Sources: [Swift.org]() Topics: [Swift](), [SDKs](), [Back end](), [iOS](), [MLX](), [wasm](), [WebAssembly](), [AWS Lambda]() Tags: [aws](), [aws-lambda](), [backend](), [community](), [conferences](), [ios](), [local](), [mlx](), [news](), [releases](), [serverless](), [swift](), [wasm](), [webassembly]() ### AI overview A curated May 2026 digest of Swift community activity, including meetup videos, backend development resources, the Temporal Swift SDK, AWS Lambda, MLX for iOS, and Swift for WebAssembly updates. ### Source excerpt Welcome to "What's new in Swift," a curated digest of releases, videos, and discussions in the Swift project and community. To start, we're focusing on some of the many local meetup groups sharing Swift content: Around the world, local meetup groups and conferences bring Swift developers together, and some even predate Swift itself! Many have YouTube channels where they share videos from their events, so you can tune in remotely. Let's highlight a few. In May the SF Swift meetup hosted a talk by Dan Federman, Agentify Your Swift Repo, which covers building an agent for CI and review feedback. Swift Barcelona has a dedicated YouTube presence, while some groups, like the new MLX India meetup, post event playlists, including a recent talk about using MLX Swift in iOS apps that's worth checking out. And there's even a community-organized Swift Community Meetups YouTube channel, which hosts meetups online and is home to a series of cross-platform Swift talks. These groups support developers in a few ways worth calling out: Local community: they're building local connections among Swift developers and organizations. Room for experimentation: by being less formal and sometimes featuring works in progress, meetups create space to share ideas, get feedback, and learn from one another. I encourage more folks to get involved in their local Swift meetup. And if your group starts publishing content and videos, please share on the Swift Forums in the Community Showcase category. -- Dave Lester Now on to other news about Swift: Videos to watch Interested in using Swift for backend server development? Mohammad Azam posted a livestream recording of Introduction to Hummingbird, a walkthrough of the web framework covering installation and development basics. Meet the Temporal Swift SDK, from Replay 2026, introduces the SDK that brings Temporal's durable workflows (long-running processes that survive crashes, retries, and restarts without losing state) to Swift. The SDK recently reached ## The PR you would have opened yourself DevFeed: [The PR you would have opened yourself]() Original publisher: [Read original article]() Author: Pedro Cuenca; Awni Hannun Published: 2026-04-16T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Pull Request](), [Open Source](), [Artificial Intelligence](), [AI Development](), [Language models](), [MLX]() Tags: [agent](), [agents](), [announcement](), [artificial-intelligence](), [best-practices](), [bugs](), [claude](), [code](), [community](), [contributors](), [language-models](), [llm](), [mlx](), [open-source](), [performance]() ### AI overview The article examines how agent-generated pull requests are changing open-source contribution, using the Transformers library as an example. It explains why maintainers must preserve human-readable design, implicit contracts, performance, and project context, and introduces a Skill and test harness to help port language models from Transformers to mlx-lm while supporting contributors and reviewers rather than automating contribution. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Welcome Gemma 4: Frontier multimodal intelligence on device DevFeed: [Welcome Gemma 4: Frontier multimodal intelligence on device]() Original publisher: [Read original article]() Author: merve; Pedro Cuenca; Sergio Paniego; ben burtenshaw; Steven Zheng; Alvaro Bartolome; Nathan Habib Published: 2026-04-02T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [gemma4](), [multimodal](), [Open Source](), [long-context](), [quantization](), [llama.cpp](), [MLX](), [Rust](), [webgpu]() Tags: [audio](), [benchmarks](), [fine-tuning](), [gemma](), [gemma4](), [generation](), [google](), [guide](), [llama](), [llama-cpp](), [local](), [long-context](), [mlx](), [models](), [moe](), [multimodal](), [on-device](), [open](), [open-source](), [quantization](), [science](), [speed]() ### AI overview Gemma 4 is presented as an open, Apache 2-licensed family of multimodal models supporting image, video, text, and, for selected variants, audio inputs. The article highlights on-device deployment, long-context support, compatibility across libraries and devices, quantization, benchmark results, and implementation options including llama.cpp, MLX, WebGPU, and Rust. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Upcoming Livestream: GPUs for AI (Shaped by You) DevFeed: [Upcoming Livestream: GPUs for AI (Shaped by You)]() Original publisher: [Read original article]() Author: Alex Razvant Published: 2026-01-27T09:30:49Z Content type: article Language: en Sources: [Neural Bits]() Topics: [GPU](), [Artificial Intelligence](), [Hardware](), [CUDA](), [pcie](), [Nvidia](), [rocm](), [MLX](), [TensorRT](), [Google](), [groq]() Tags: [ai](), [cuda](), [google](), [gpu](), [groq](), [hardware](), [mlx](), [nvidia](), [pcie](), [rocm](), [tensorrt]() ### AI overview An upcoming livestream will discuss how GPUs and other accelerators support AI workloads. The author invites audience feedback to shape coverage of GPU hardware, PCIe, CUDA, ASICs, TPUs, LPUs, model optimization, and related technologies. ### Source excerpt You can choose the topics for a Live Session on GPUs in AI ## Introducing AnyLanguageModel: One API for Local and Remote LLMs on Apple Platforms DevFeed: [Introducing AnyLanguageModel: One API for Local and Remote LLMs on Apple Platforms]() Original publisher: [Read original article]() Author: Mattt Published: 2025-11-20T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [SDKs](), [AI Chat](), [Google AI]() Tags: [anthropic](), [api](), [apple](), [community](), [coreml](), [gemini](), [llama-cpp](), [llm](), [llms](), [local](), [mlx](), [open-source](), [openai](), [remote](), [swift]() ### AI overview AnyLanguageModel is a Swift package that lets Apple-platform developers use a common Foundation Models-style API across local and remote LLM providers. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Granite 4.0 Nano: Just how small can you go? DevFeed: [Granite 4.0 Nano: Just how small can you go?]() Original publisher: [Read original article]() Author: Kate Soule; Rameswar Panda Published: 2025-10-28T14:59:38Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [ibm](), [Large Language Model](), [Model Development](), [Open Source Models & Datasets](), [llama.cpp](), [MLX](), [vllm](), [Transformer](), [Loop Engineering](), [gemma](), [qwen](), [Google]() Tags: [apache](), [architecture](), [benchmarks](), [code](), [data](), [development](), [gemma](), [google](), [ibm](), [language-models](), [llama](), [llama-cpp](), [math](), [mlx](), [model](), [model-development](), [models](), [on-device](), [qwen](), [training]() ### AI overview IBM introduces Granite 4.0 Nano, a family of compact language models for edge and on-device applications. The release includes hybrid-SSM and traditional transformer variants ranging from roughly 350M to 1.5B parameters, supports vLLM, llama.cpp, and MLX, and is released under the Apache 2.0 license. The article reports strong performance across knowledge, math, code, safety, instruction-following, and tool-calling benchmarks. ### Source excerpt Today we are excited to share Granite 4.0 Nano, our smallest models yet, released as part of IBM's Granite 4.0 model family. Designed for the edge and on-device applications, these models demonstrate excellent performance for their size and represent IBM's continued commitment to develop powerful, useful, models that don't require hundreds of billions of parameters to get the job done. ## SOTA OCR with Core ML and dots.ocr DevFeed: [SOTA OCR with Core ML and dots.ocr]() Original publisher: [Read original article]() Author: Christopher Fleetwood; Pedro Cuenca Published: 2025-10-02T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [MLX](), [PyTorch]() Tags: [apple](), [architecture](), [battery](), [coreml](), [cpu](), [developers](), [framework](), [gpu](), [high-performance](), [images](), [mlx](), [model](), [ocr](), [on-device](), [parameter](), [precision](), [pytorch](), [repo](), [tools](), [vision]() ### AI overview This tutorial explains how to convert dots.ocr from PyTorch to Core ML for on-device execution on Apple hardware. It discusses the roles of the Neural Engine, GPU, MLX, and Core ML, then outlines a staged conversion process beginning with GPU execution, FLOAT32 precision, and static shapes. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Swift Transformers Reaches 1.0 - and Looks to the Future DevFeed: [Swift Transformers Reaches 1.0 - and Looks to the Future]() Original publisher: [Read original article]() Author: Pedro Cuenca; Christopher Fleetwood; Mattt; Vaibhav Srivastav Published: 2025-09-26T00:00:00Z Content type: release Language: en Sources: [Hugging Face - Blog]() Topics: [swift-transformers](), [MLX](), [Swift](), [Inference](), [Local AI](), [hugging face](), [Large Language Model](), [Open Source]() Tags: [apple](), [asr](), [coreml](), [hugging-face](), [inference](), [llms](), [local](), [mlx](), [open-source](), [swift](), [swift-transformers](), [transformers]() ### AI overview swift-transformers reaches version 1.0 as a Swift library for running local models on Apple Silicon, including iPhones. It combines input preparation, Hugging Face Hub access, local caching and offline downloads, and wrappers for running Core ML-converted LLMs. The release establishes a stable foundation for Apple-focused local inference and future MLX and agentic use cases. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Gemma 3n fully available in the open-source ecosystem! DevFeed: [Gemma 3n fully available in the open-source ecosystem!]() Original publisher: [Read original article]() Author: Aritra Roy Gosthipaty; Pedro Cuenca; Sergio Paniego; Vaibhav Srivastav; Christopher Fleetwood; Joshua; Steven Zheng; Kashif Rasul Published: 2025-06-26T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [gemma](), [Open Source](), [AI Development](), [llama.cpp](), [MLX](), [Ollama](), [transformers.js](), [GPU](), [Hardware](), [timm](), [asr](), [Embeddings]() Tags: [ai](), [artificial-intelligence](), [audio](), [community](), [embeddings](), [gemma](), [gpu](), [hardware](), [implementation](), [llama-cpp](), [llm](), [mlx](), [multimodal](), [ollama](), [open-source](), [release](), [research](), [speech](), [timm](), [transformers](), [transformers-js](), [translation](), [vision](), [vlm]() ### AI overview This article announces the availability of Gemma 3n in major open-source libraries and presents practical usage and fine-tuning examples. It describes the model variants, memory-efficient hardware requirements, multimodal audio and vision encoders, speech-to-text and translation capabilities, and architectural features including MatFormer and Per-Layer Embeddings. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## The Transformers Library: standardizing model definitions DevFeed: [The Transformers Library: standardizing model definitions]() Original publisher: [Read original article]() Author: Lysandre; Arthur Zucker; Pedro Cuenca; Julien Chaumond Published: 2025-05-15T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Transformer](), [LLMs](), [interoperability](), [sglang](), [vllm](), [llama.cpp](), [tgi](), [MLX]() Tags: [announcement](), [community](), [interoperability](), [library](), [llama-cpp](), [llms](), [mlx](), [open-source](), [sglang](), [tgi](), [transformers](), [vllm](), [vlms]() ### AI overview The article presents Transformers as a standard library for defining and supporting machine learning model architectures. It describes its broad ecosystem integrations, including training frameworks and inference engines, and highlights interoperability with vLLM, SGLang, TGI, llama.cpp, and MLX. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## SmolVLM2: Bringing Video Understanding to Every Device DevFeed: [SmolVLM2: Bringing Video Understanding to Every Device]() Original publisher: [Read original article]() Author: Orr Zohar; Miquel Farré; Andres Marafioti; merve; Pedro Cuenca; Cyril; Joshua Published: 2025-02-20T00:00:00Z Content type: release Language: en Sources: [Hugging Face - Blog]() Topics: [Language models]() Tags: [apis](), [artificial-intelligence](), [benchmark](), [devices](), [images](), [language-models](), [llm](), [memory](), [mlx](), [models](), [multimodal](), [nlp](), [on-device](), [performance](), [python](), [release](), [swift](), [video](), [vision](), [vlm]() ### AI overview SmolVLM2 introduces compact video-language models for video and visual understanding across devices. The article highlights three model sizes, MLX-ready Python and Swift APIs, and benchmark performance including Video-MME. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## SmolVLM Grows Smaller - Introducing the 256M & 500M Models! DevFeed: [SmolVLM Grows Smaller - Introducing the 256M & 500M Models!]() Original publisher: [Read original article]() Author: Andres Marafioti; Miquel Farré; merve Published: 2025-01-23T00:00:00Z Content type: release Language: en Sources: [Hugging Face - Blog]() Topics: [vlm](), [releases]() Tags: [artificial-intelligence](), [benchmarks](), [browser](), [community](), [cost](), [demo](), [devices](), [images](), [inference](), [llm](), [mlx](), [models](), [multimodal](), [on-device](), [open-source](), [optimization](), [performance](), [release](), [research](), [smolvlm](), [tokenization](), [training](), [vision](), [vlm]() ### AI overview Hugging Face announces 256M and 500M SmolVLM vision-language models, including base and instruction-tuned checkpoints. The release emphasizes small-footprint multimodal performance, a smaller vision encoder, larger image resolution, and support for Transformers, MLX, ONNX, and WebGPU demos. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science.