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infrastructure#llm📝 BlogAnalyzed: Jan 16, 2026 17:02

vLLM-MLX: Blazing Fast LLM Inference on Apple Silicon!

Published:Jan 16, 2026 16:54
1 min read
r/deeplearning

Analysis

Get ready for lightning-fast LLM inference on your Mac! vLLM-MLX harnesses Apple's MLX framework for native GPU acceleration, offering a significant speed boost. This open-source project is a game-changer for developers and researchers, promising a seamless experience and impressive performance.
Reference

“Llama-3.2-1B-4bit → 464 tok/s”

Permalinkr/deeplearning
Research#llm📝 BlogAnalyzed: Dec 26, 2025 18:41

GLM-4.7-6bit MLX vs MiniMax-M2.1-6bit MLX Benchmark Results on M3 Ultra 512GB

Published:Dec 26, 2025 16:35
1 min read
r/LocalLLaMA

Analysis

This article presents benchmark results comparing GLM-4.7-6bit MLX and MiniMax-M2.1-6bit MLX models on an Apple M3 Ultra with 512GB of RAM. The benchmarks focus on prompt processing speed, token generation speed, and memory usage across different context sizes (0.5k to 64k). The results indicate that MiniMax-M2.1 outperforms GLM-4.7 in both prompt processing and token generation speed. The article also touches upon the trade-offs between 4-bit and 6-bit quantization, noting that while 4-bit offers lower memory usage, 6-bit provides similar performance. The user expresses a preference for MiniMax-M2.1 based on the benchmark results. The data provides valuable insights for users choosing between these models for local LLM deployment on Apple silicon.
Reference

“I would prefer minimax-m2.1 for general usage from the benchmark result, about ~2.5x prompt processing speed, ~2x token generation speed”

Permalinkr/LocalLLaMA
Research#llm📝 BlogAnalyzed: Dec 29, 2025 06:05

Multimodal AI on Apple Silicon with MLX: An Interview with Prince Canuma

Published:Aug 26, 2025 16:55
1 min read
Practical AI

Analysis

This article summarizes an interview with Prince Canuma, an ML engineer and open-source developer, focusing on optimizing AI inference on Apple Silicon. The discussion centers around his contributions to the MLX ecosystem, including over 1,000 models and libraries. The interview covers his workflow for adapting models, the trade-offs between GPU and Neural Engine, optimization techniques like pruning and quantization, and his work on "Fusion" for combining model behaviors. It also highlights his packages like MLX-Audio and MLX-VLM, and introduces Marvis, a real-time speech-to-speech voice agent. The article concludes with Canuma's vision for the future of AI, emphasizing "media models".
Reference

“Prince shares his journey to becoming one of the most prolific contributors to Apple’s MLX ecosystem.”

PermalinkPractical AI
Research#llm👥 CommunityAnalyzed: Jan 4, 2026 08:49

Mistral LLM on Apple Silicon Using Apple's MLX Framework Runs Instantaneously

Published:Dec 7, 2023 03:09
1 min read
Hacker News

Analysis

The article highlights the impressive performance of the Mistral LLM when running on Apple Silicon devices, specifically noting the use of Apple's MLX framework. The claim of 'instantaneous' execution suggests a significant advancement in the efficiency of running large language models on consumer hardware. The source, Hacker News, indicates a tech-focused audience, suggesting the article is likely to be of interest to developers and tech enthusiasts.
Reference

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PermalinkHacker News