Latest AI updates

StepFun Releases Step 5 Preview Open Weights: 600B MoE, 27B Active, 1M Context

StepFun has published open FP8 and BF16 checkpoints for Step 5 Preview, a sparse Mixture-of-Experts model with 600B total parameters, 27B active per token and up to 1M tokens of context, targeting long-horizon agentic work. The release lists data-center-only self-hosting requirements, including 4x B300 or 8x H200 GPUs for the FP8 checkpoint, and an API priced at $1.00 per 1M input tokens and $2.70 per 1M output tokens. StepFun-reported results put it near GLM-5.3 and Kimi K3 on DeepSWE v1.1, whi

Why it matters: It gives developers a concrete new self-hostable open-weight option for agentic and long-context work, with clear hardware requirements, pricing and benchmark caveats.

Liquid AI Releases LFM2.5-24B-A2B: A 2B-Active MoE for On-Device Agents

Liquid AI released LFM2.5-24B-A2B, an open-weight 24B-parameter Mixture-of-Experts reasoning model with about 2B active parameters and 131,072-token context, targeted at local agent loops. It runs via GGUF/MLX on laptops and mini PCs, with cited speeds of 184 tokens/s on Apple M5 Max and 114 tokens/s on AMD Ryzen AI Max+ 395, plus single NVIDIA B300 serving. Liquid AI reports it leads or matches larger MoEs on several agentic, tool-use and instruction-following benchmarks, while trailing Qwen3.6

Why it matters: Provides a concrete open-weight release with on-device performance, agentic benchmark comparisons, licensing and deployment details useful for evaluating local agent models.

AWS details fused Mixture-of-Experts kernel optimizations for SageMaker AI and vLLM

AWS described three kernel-level optimizations for fused Mixture-of-Experts inference on NVIDIA Hopper: a Swap-AB transposition to improve WGMMA utilization, routing FP8 tensor-quantized MoE to the Triton backend, and eliminating Triton JIT recompilations with do_not_specialize annotations. The post reports up to 10% higher end-to-end throughput and up to 9% faster Time to First Token on H200. The optimizations were upstreamed to vLLM and are used in the AWS LMI v28 container on Amazon SageMaker

Why it matters: Provides practical, open-source GPU kernel techniques and measured latency and throughput gains for teams deploying MoE models on Hopper and SageMaker.

Cloudflare Releases Clef-omni, a 30B-A3B Multimodal Decision Model

Cloudflare released Clef-omni, an open-weight Apache-2.0 decision model that scores text, images, audio and video in a single call. It is post-trained from Qwen3-Omni-30B-A3B-Instruct as a 30B-total/3B-active MoE with 64,000-token context on Workers AI, priced at $0.15 per million tokens. Cloudflare reports strong intent classification results (97.7 macro-F1 on CLINC150+OOS) but weaker scores than text-focused Clef on reasoning and workflow evals, and Cloudflare's benchmark numbers were not inde

Why it matters: It documents a concrete new open-weight multimodal model release with architecture, pricing, latency and benchmark details useful for developers building multimodal agent pipelines.

NVIDIA Unveils Nemotron 3 Nano Omni: A Full-Modality Model That Summarizes a 3-Minute Speech in Seco

NVIDIA officially launched its new multimodal reasoning model, Nemotron 3 Nano Omni, yesterday. It deeply integrates text, vision, and speech capabilities into a single model system and is currently available for free.As the latest member of the Nemo<!--autointro-->