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Open Mixture-of-Experts (MoE) models share one idea but disagree on almost every routing detail. Expert counts range from 8 to 384. Top-k ranges from 1 to 8. Some models keep an always-on shared expert; others drop it. TL;DR Size: 10 models, from Qwen3.6-35B-A3B (35B total, 3B active) to DeepSeek-V4-Pro (1.6T total, 49B active). Runs on:
Every Mixture-of-Experts (MoE) model repeats one decision for every token: which experts should process it. That decision belongs to the router. MoE routing strategies shape quality, load balance and serving cost in every major open model. This guide explains the 7 routing strategies used in production, and the models that use each one. TL;DR Size:
Nous Research has released Arete-27B, a supervised fine-tune of Alibaba’s Qwen3.8-27B. Arete-27B is a 27B open-weight model that strips Chinese state framing from Qwen’s answers. On Nous’s own 803-prompt eval, answers containing propaganda fell from 22% to 0.9%. Developers get Qwen-class agentic skill without state-aligned answers. The weights, eval and training data are all public.
Nous Research Releases Arete-27B that Cuts Qwen3.8-27B Propaganda From 22% to 0.9% Read More »
Buoyant, the company behind Linkerd, has released Kinference, an open source project for building inference platforms on Kubernetes. Kinference is an inference control plane for Kubernetes and vLLM. It sits in front of a fleet of vLLM workers and decides where each request goes. It applies routing policies, token rate limits, KV-cache-aware load balancing and
Zyphra has released ZYFA (Zyphra FlashAttention on AMD), a set of FP8 and BF16 prefill attention kernels for AMD Instinct MI355X GPUs. ZYFA is Zyphra’s FP8 attention engine for long-context prefill, and the code is open source under Apache 2.0. On real model activations, its FP8 kernels run 1.16-1.76x faster than AITER, AMD’s default kernel
Zyphra ZYFA: FP8 FlashAttention for AMD MI355X, Up to 1.76x Faster Read More »
Thinking Machines Lab has released Inkling 2 Small, the first model of its second Inkling generation. The Inkling 2 Small model is a sparse Mixture-of-Experts (MoE) with 594B total and 17B active parameters. It accepts text and images, and supports variable reasoning effort. On the Artificial Analysis Intelligence Index, it scores 31. Its predecessor, Inkling
Thinking Machines Releases Inkling 2 Small, a 594B MoE With 17B Active Read More »
Baidu has released ERNIE 5.5 Flash Base, the raw pre-training checkpoint of its new sparse Mixture-of-Experts language model. ERNIE 5.5 Flash Base is a 280B-parameter text model from Baidu that activates 14B parameters per token. The weights stop at the end of pre-training, with no mid- or post-training applied. This as a clean starting point
Baidu Releases ERNIE 5.5 Flash Base: A 280B MoE Base Model With 14B Active Parameters Read More »
webAI has released TwIL-8B, an 8.8B reasoning model for math, code and instruction following. The TwIL-8B reasoning model is post-trained from IBM’s Granite 4.2 8B. Weights are on Hugging Face under a non-commercial license. webAI’s launch post frames the pitch around cost per task, not raw scale. The 4-bit build is 4.98 GiB, sized for
webAI Releases TwIL-8B, an 8.8B Reasoning Model Scoring 98.2 on MATH-500 Read More »
Ricursive Intelligence just released Ripple-65B Preview. Ripple-65B Preview is a 65B dense open-weight model from Ricursive, pretrained from scratch for agentic coding. It is a general-purpose variant of the proprietary model behind Ricursive’s AI chip design platform. The main claim: 80.52 on Terminal-Bench 2.1, the top self-reported score among US open-weight models. Every MoE rival
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
