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Showing 1-6 of 6Results matching "vLLM"

Yesterday

1 story
LaunchModels

IFM Releases Six K2 Horizon Models With Weights, Code and Training Data

The new lineup gives developers models from 0.9B to 375B parameters under Apache 2.0, while putting IFM’s performance claims and unusually broad training record in public view. Developers seeking to move from a small local model to a larger enterprise system now have a single newly released family to test. A dense 32B model and sparse 36B-A4B model are intended for local hosting and on-premise servers.

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Earlier this week

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Open releaseModels

DeepSeek Releases 305B Vision Weights Under MIT License, but Local Use Needs Serious Hardware

The release gives developers the model files and serving references needed to run or adapt DeepSeek-V4-Flash-Vision-Exp independently. Its 168GB size means that freedom is aimed more at infrastructure-equipped teams than typical individual users. The shift gives developers more control over a model that accepts images, but the 168GB checkpoint sets a high infrastructure threshold for anyone seeking to run it locally.

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Last week

3 stories
Open releaseModels

IBM Releases Granite 4.2 Open Weights, Reserving Agentic Training for 8B and 30B

The new family gives self-hosted deployments a choice between a smaller tool-capable model and larger variants trained for code, search and other tool-use tasks. IBM has released Granite 4.2, an open-weight language-model family built for download and self-hosting. The central distinction is not just size: its 8B and 30B models receive specialized training for tool use, while the 3B version does not.

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Open releaseModels

MiniMax Maps H3 to 24GB PCs and Server Stacks, but Keeps Context Layer Hosted

The index gives developers more routes to run and adapt H3, yet the layer that turns mixed media into model-ready context remains an API product—and local use is restricted in several major markets. MiniMax has published an H3 integrations index that points developers from a 24GB local setup to ComfyUI workflows, Apple Silicon, fine-tuning, and multi-GPU serving through SGLang and vLLM-Omni.

3 min readRead story

August 2026

1 story
BenchmarkModels

Open Models Are Catching the Frontier Faster. Benchmark Scores Aren’t the Whole Contest.

SemiAnalysis sees the lag from closed-model breakthroughs to open-model benchmark parity shrinking across three AI eras. But a comparable model score still leaves a harder product question: who can turn capability into dependable agent work? In the reasoning and agentic eras, open AI models reached closed-model benchmark parity in months, according to SemiAnalysis ’s cross-era analysis.

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