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Manus introduces Flex, a new module that allows users to bring their own API key to power Manus's agent infrastructure, with initial partners including OpenRouter, Fireworks, and Modal.
The article argues that specialized, one-off inference engines will outperform general ones like llama.cpp due to optimization for specific models and hardware, predicting their rise as the norm in AI inference.
Appreciation post for tcclaviger adding expert RAM offloading support to vLLM, enabling running large AI models like DeepSeek-V4-Flash-Vision-Exp on local setups with multiple GPUs.
Modal Labs, an AI inference infrastructure provider, is closing in on a $750 million funding round at a $15.75 billion valuation, more than tripling its valuation from four months ago amid soaring demand for AI inference services.
LlamAmpere v0.4 is released with Ampere-specific improvements, achieving over 95 tokens per second and supporting 262K context for the Qwen3.8-27B model on a single NVIDIA 3090 GPU.
The user built a low-cost setup using five ex-mining BC-250 boards to run the Qwen3-Coder-Next AI model, achieving around 40 tokens per second at 30k context with plans to expand.
A tweet criticizing the slow performance of cloud AI models compared to self-hosted solutions.
The article explores the emerging market for compute derivatives and their potential to transform how neoclouds manage GPU rental risks and pricing in the AI inference cloud industry.
A user shares kernel optimizations for Tesla P100 GPUs to improve performance when running the Qwen 3.8 model with llama.cpp, achieving significant speedups in inference.
The article promotes a talk at the PyTorch Conference North America focused on making enterprise agentic inference production-ready using PyTorch and vLLM, covering ecosystem updates and registration details.
Splish is an unofficial fork of Splash that optimizes Metal kernels for Apple M5 Max chips, delivering up to 1.5× faster AI inference speeds for models like Qwen3.8-27B without compromising quality.
The article details a bug fix for the GPT-OSS template from Unsloth, where chat history rendering incorrectly drops model answers during multi-turn inference, causing model degradation. The author shares an updated template that preserves thinking to improve performance.
A user optimized a 4x3060ti GPU rig for AI inference using tensor parallelism with Exl3 and vllm, achieving up to 120 tokens per second with large context windows.
A DIY inference machine built with dual RTX 3090 GPUs, a ThinkPad motherboard, and a VW Golf radiator, capable of running AI models like Qwen3.8-27B via vLLM.
A user built an open-source inference engine called Strata to optimize running Qwen3.8-Flash-Next on modest hardware, achieving up to 65.1 tok/s from an initial 15 tok/s.
The article promotes a presentation on Elastic Expert Parallelism in vLLM at the PyTorch Conference North America 2026, discussing how to dynamically add or remove GPUs in Mixture-of-Experts deployments with minimal downtime.
Achieved 85.6 tokens per second using the Qwen3.8:27b model on a single RTX 5090 GPU.
A user shares their experience switching to the QWEN3.6-27B-MLX-8bit model for local AI tasks, finding it performs comparably to larger models while saving significant RAM on a Mac Studio, improving workflow stability.
A GitHub repository collabosm provides an optimized setup for running the Qwen3.8-Flash-Next model on a Google Colab A100, achieving inference speeds faster than commercial APIs with detailed performance metrics and instructions.
The article compares the inference performance of Apple's Mac Studio M5 Ultra with two NVIDIA DGX Spark units when running the DeepSeek V4 Flash model, showing DGX Sparks are faster in prefill while Mac Studio is slightly faster in generation.