vLLM
vLLM on Beyond Market Intelligence: a running collection of 6 stories we have gathered and hand-picked because they are worth your time. Every post here touches on vllm in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around vllm, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
Speculative Decoding on CPUs: Nearly 4x Faster Token Generation with DFlash
Unlock significantly faster token generation on your CPUs with DFlash, a novel speculative decoding technique. Our vLLM tests demonstrate a remarkable 3.92x increase in autoregressive throughput using Qwen3.5-9B on Intel Xeon 6 processors—effectively repurposing idle compute. This approach accelerates processing without altering model output. We detail the underlying performance gains, acceptance metrics, and factors influencing speculation’s effectiveness. Explore the full analysis in our post, and for broader context on the AI landscape, see our coverage of recent developments at Hugging Face.
Meta returns to open source with Muse Glimmer, an Apache 2.0 licensed 30B parameter AI model optimized for agents — available now
Meta’s return to open source with Muse Glimmer marks a significant shift in the AI landscape. This 30-billion-parameter model, licensed under the permissive Apache 2.0, is specifically optimized for autonomous AI agents and designed to run directly on consumer hardware like Macs and PCs. Unlike previous Meta releases, Glimmer offers unrestricted commercial use and redistribution. The model's ability to operate locally, without cloud dependency, enhances data privacy and reduces costs, as demonstrated by its efficient performance on just 24GB of VRAM.
No cloud, no GPUs, no problem: Liquid AI's new model LFM2.5-2.6B brings powerful AI agents to devices as small as a Raspberry Pi
Liquid AI has unveiled LFM2.5-2.6B, a new open-weight language model designed to bring powerful AI agents to devices as small as a Raspberry Pi – a significant step toward accessible edge AI. This model, boasting 2.6 billion parameters and a 128,000-token context window, runs entirely on local hardware without cloud inference or GPUs, ideal for high-volume tasks like automation and connectivity-limited environments. Explore how this innovative solution transforms data management and expands possibilities for enterprises, as highlighted in our recent coverage of Qwen 3.8-Max.
Kimi K3's full weights are here, but they're 'open' with a caveat: What enterprises should know
Moonshot AI has released the full weights for Kimi K3, its powerful new AI model, marking a significant step for open-weight AI. While broadly accessible, enterprises should carefully review the custom Kimi K3 usage license. Larger organizations operating a "Model as a Service" exceeding $20 million in revenue, or those with products impacting over 100 million users, face specific commercial obligations, including potential licensing agreements and prominent attribution.
Netflix Details Its In-House LLM Serving Platform with Triton and vLLM
Netflix has detailed its sophisticated in-house platform for Large Language Model (LLM) inference, leveraging Triton and vLLM to address the complexities of scaling AI. The platform’s design reflects key production lessons learned, specifically managing diverse model sizes, hardware demands, and the accelerated evolution of inference engines. This architecture allows Netflix to rapidly deploy and optimize LLMs internally. For a deeper understanding of adapting to AI’s rapid pace of change, explore our related article, "An Evolutionary Architecture Pattern for Managing AI’s Pace of Change."
Reproducing OpenAI’s “persistently beneficial models” - GRPO trait install barely moves. Ideas? [P] [R]
Reproducing OpenAI’s trait-persistence results presents a significant challenge, particularly at smaller scales. Our attempt to install a "traditionalism" trait (low Openness) using GRPO on Qwen2-7B achieved a minimal improvement of just +2.4 points, falling far short of the ~+15 needed. Despite rigorous debugging—ruling out reward hacking, memorization, and gradient issues—the install remains stubbornly flat. We’re seeking guidance from those with experience in small-scale RLHF/GRPO for trait or persona installation. See "The qlora 2e-4 default is wrong under 10k samples and nobody