Mlx
PulseAugur coverage of Mlx — every cluster mentioning Mlx across labs, papers, and developer communities, ranked by signal.
- developed by Muse Glimmer 95%
- developed by Muse Spark Open 90%
- developed by Omlx Local Ai Models 90%
- uses Unsloth Studio 90%
- used by Apple Neural Engine 90%
- competes with Omlx Local Ai Models 80%
- used by Muse Spark Open 70%
- used by M5 Ultra 70%
- used by Muse Glimmer 70%
- instance of Qwen3 8.27B 70%
- used by Qwen3.8 70%
- used by mlx-lm 70%
26 day(s) with sentiment data
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Apple Mac Studio with M5 Ultra challenges Nvidia DGX Spark in AI benchmarks
Apple's Mac Studio, equipped with the M5 Ultra SoC, is being benchmarked against Nvidia's DGX Spark for local AI performance. The Mac Studio, featuring a 36-core CPU and 80-core GPU with Neural Accelerators, and up to 5…
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Local LLM benchmark numbers vary widely due to hardware and measurement differences
Local LLM inference benchmarks can be misleading due to numerous variables, including hardware differences, model file variations, and how different servers measure performance. The author discovered that server-reporte…
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Developer fine-tunes 2B model to play Doom on Mac using MLX
A developer has successfully fine-tuned a 2-billion parameter decision model to play the game Doom on a Mac using the MLX framework. The model, trained on approximately 3,000 frames, was able to achieve an average of 23…
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Local LLMs: Understanding score variability with temperature and sampling
Local large language models (LLMs) can produce inconsistent scores for the same input due to default sampling parameters like temperature, top-k, and top-p. The common solution of setting the temperature to 0 addresses …
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Zyphra releases ZONOS3-Mini open-source TTS for real-time local use
Zyphra has released ZONOS3-Mini, an open-source text-to-speech model designed for real-time performance on local devices. This model boasts 100 million active parameters and supports zero-shot voice cloning and word-lev…
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Optimize Local LLMs on Mac with Ollama and MLX Settings
This article provides a guide for optimizing local Large Language Models (LLMs) on Mac devices using Ollama and MLX. It details memory usage for various settings within Ollama and llama.cpp, offering insights into defau…
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Mac mini M5 Pro user seeks advice on local AI model performance
A user is seeking advice on effectively utilizing local AI capabilities on their Mac mini equipped with an M5 Pro chip and 48GB of unified memory. They are interested in running models for coding assistance, image gener…
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Liquid AI's LFM3-1.7B model fits in 1GB, claims benchmark win over Qwen3.5-9B
Liquid AI has released LFM3-1.7B, a new 1.73 billion parameter text model that can fit into 1 GB of memory and process up to 256K tokens of context. While Liquid AI's own benchmarks suggest LFM3-1.7B outperforms Qwen3.5…
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Ollama releases v0.40.16-rc0 with Ripple model support
Ollama has released version v0.40.16-rc0, introducing support for the Ripple model and its associated renderer, parser, and import registration. This update also includes enhancements for per-layer NoPE in DFlash drafts…
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KlangAI releases state-of-the-art Danish ASR model pianissimo-da
KlangAI has released pianissimo-da, a new Danish Automatic Speech Recognition (ASR) model. This model is available in multiple formats including GGUF, ONNX, MLX, and CoreML, and is hosted on Hugging Face. It is describe…
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New open-source tool simplifies LLM quantization for desktop users
A new open-source desktop workspace called iQuantize has been released, designed to streamline the process of quantizing large language models. This tool supports conversions to GGUF, MLX, and ONNX formats, allowing use…
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Ripple-65B LLM requires custom builds for local execution
The Ripple-65B large language model is now available for local execution, though it requires specific configurations and patched software. Standard tools like Ollama and the default llama.cpp build cannot load the model…
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Qwen3.6 LLM runs locally on MacBook Pro via oMLX, achieving fast speeds
A user has successfully run the Qwen3.6-35B-A3B large language model locally on a MacBook Pro using the oMLX framework and openCode. This setup allows for fast token output, estimated at around 100 tokens per second, en…
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Claude Code accessible locally on Mac or remotely via iPhone
Two new methods allow users to run Claude Code, a coding assistant, either locally on their Mac or remotely via an iOS app. The local setup, named Claude Code Local, uses Python and Apple's MLX framework to run various …
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Unsloth AI interface adds browser interaction, faster generation, and new themes
Unsloth has released updates enhancing customization and performance for its AI model interface. Version v0.1.915-beta introduces Qwen3.8-Flash-Next for improved accuracy, adds browser file interaction capabilities for …
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Run LLMs locally on Apple Silicon with MLX, while critics question AI product design
A new open-source project, navikt/mlx-workspace, enables users to run large language models locally on Apple Silicon using MLX. This setup allows for integration with AI coding tools without relying on cloud services. S…
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64GB RAM viable for local LLMs with agents, user reports
A user on Reddit's r/LocalLLaMA community has found that 64GB of RAM is no longer a "dead zone" for running local large language models, particularly when utilizing agents for coding tasks. Their experience with a 27B p…
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Spark AI releases DSpark draft model for faster inference
Spark AI has released DSpark, a new draft model designed to accelerate inference speeds for their Spark-X2.6-4B model. Benchmarks indicate DSpark can more than double BF16 inference throughput, achieving up to 2.14x spe…
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Anthropic SDKs and Claude Code see major updates, Ollama adds new models
Anthropic has released several updates across its SDKs and tools, including new features for its Python SDK and Claude Code. The Python SDK now supports more detailed analytics, error handling, and integration with clou…
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LiquidAI d1-Omni-2B: A decision model that hears but doesn't speak
The LiquidAI d1-Omni-2B model, designed as a "decision model" rather than a traditional chat assistant, can process various inputs including audio but does not generate spoken or written responses. It answers a set of p…