NVIDIA’s latest technical blog shows how to speed up mixture-of-experts (MoE) inference on one DGX Spark. The test model is Ant Group inclusionAI’s Ling-3.0-flash, a 124B-parameter MoE with 5.1B active parameters. NVIDIA stacked three optimizations: a new MoE kernel backend, an FP8 output layer and built-in speculative decoding. On all 1,319 GSM8K questions, decode throughput
Baseten has released joey-1, a 2B model from its in-house research lab, Base Labs. The joey-1 decision model returns a label and a calibrated probability instead of generated text. It targets high-volume routing, classification and guardrail checks. Baseten prices it at $0.01 per million input tokens, with open weights on Hugging Face under MIT. TL;DR
Baseten Releases joey-1, a 2B Decision Model at $0.01 per 1M Tokens Read More »
NVIDIA has released Nemotron 3.5 Transcribe, an open-weights speech LLM built for transcription. One model handles 40 languages and recordings up to 1 hour. A text instruction picks the output style for each request. Options include verbatim, written form, speaker-attributed, contextual and translated output. For developers, it can replace separate ASR, diarization and alignment tools.
Liquid AI has released LFM3, its new generation of Liquid Foundation Models. Liquid AI LFM3 is a family of 6 open-weight, 2B-class models built on one shared backbone. The family covers text, vision, audio, omni and decision tasks. Every model can be fine-tuned on a single GPU. Developers can then deploy on phones, laptops, private
Liquid AI Releases LFM3: 6 Open-Weight 2B-Class Multimodal Models Read More »
Mirendil has released Fern, its first AI model. Mirendil Fern is a from-scratch coding model with a context window of up to 10M tokens. Mirendil explains how that equals about 23 MB of code, or roughly half a million lines. That is a codebase on the scale of Megatron-LM, held in context at once. Fern
Mirendil Launches Fern 0.1, Its First Model, With 10M-Token Context Read More »
Tsinghua University and CUHK researchers open-sourced AKV, a serving system for KV cache engineering in LLM agents. Instead of summarizing old history, agents drop selected KV cache entries and keep reusing the rest. Across 3 models and 4 agent benchmarks, average cost falls 27.7%. Average task score rises 1.7 points. The code is Apache-2.0 licensed
Tsinghua AI Open-Sources AKV, a Cache Engineering Server for LLM Agents on SGLang Read More »
Nanbeige LLM Lab has released Nanbeige4.3-33B on Hugging Face under the Apache 2.0 license. Nanbeige4.3-33B is a 33B dense language model built for agentic coding and office-style ‘cowork’ tasks. The lab sits inside the recruitment platform BOSS Zhipin. It also published Nanbeige4.3-33B-Base for custom post-training. The model supports a 524,288-token context window. In the lab’s
Nanbeige Releases Nanbeige4.3-33B, a 33B Open Agentic Coding Model Read More »
In this tutorial, we build a stateful engineering copilot with Letta that demonstrates how agentic memory extends beyond conventional prompt-based conversations. We begin by creating inspectable core memory blocks that the agent can autonomously update, then expose those blocks for programmatic state synchronization and shared access across multiple agents. We further extend the architecture with
NVIDIA has just released Nemotron 3.5 Bolt E4B 4GB, an open small language model for fast conversational agents. It targets voice assistants, game NPCs and robot interfaces running locally on RTX, Jetson and Spark hardware. The model is post-trained from Google’s Gemma 4 E4B using reinforcement learning only. For developers, this is agent behavior in
Moonshot AI has just released Kimi K3.1 Pro, the first model in its K3.1 series. Kimi K3.1 Pro is a 3T-class open-weight* model with a 1-million-token context window. Moonshot team positions it against the strongest proprietary models on coding, agentic and knowledge benchmarks. For developers, it is cheaper API pricing than K3 and open weights
