Open-weight releases increasingly adopt mixture-of-experts architectures with tens or hundreds of billions of total parameters but only a few billion active per token, often paired with 1M-token context windows. This pattern pushes capability up while keeping inference cost manageable for local or cloud deployment.
Reflection has released Beam, an open LLM with a 501-billion total and 23-billion active parameter MoE architecture. It supports a 1 million token context and BF16, FP8, and NVFP4 precision. The focus is token-efficient reasoning: about one third of ...
The model card for Tencent Hy-Memory-30B-A3B sparks debate over a slippery claim: an architecture with 3 billion active parameters out of 30 billion total would beat Opus 5 on an unspecified memory test. The issue is not the benchmark but the structu...
Meta has opened the weights of Muse Spark Open, but the point is not just scale: Helion, a PyTorch-native DSL for portable kernels, and TorchTitan support for fine-tuning shift the discussion to hardware control, TCO, and self-hosting. A second- and ...
Meta releases the weights for Muse Spark Open, a model with 816B total and 42B active parameters and a 1M-token context window. The announcement combines Helion and TorchTitan: portable kernels across NVIDIA, AMD, and Intel GPUs, plus PyTorch-native ...
NVIDIA positions Nemotron 3.5 Bolt as a vision-language model for everyday agentic tasks running locally: 74B total parameters, 9B active, native 2-bit quantization, and a 24GB footprint. It supports text, images, video, and up to 256K tokens of cont...
Microsoft AI has developed a local version of MAI Code 1.1 Flash, a coding-optimized mixture-of-experts model with 137 billion total and 6.8 billion active parameters. The news points to a push toward self-hosted development tools on Windows, with im...
inclusionAI's Ling-3.1-flash appears on Hugging Face with 560 billion total parameters, 25 billion active parameters per token, and a 1-million-token context window. The mixture-of-experts configuration reduces per-token compute, but shifts the focus...
NVIDIA's Nemotron Bolt 3.5 74B A9B arrives Oct 15 as a 74B-parameter, 9B-active MoE compressed to 24GB using 2-bit quantization with quantization-aware training. It targets speed and intelligence balance; a 4GB E4B model follows Oct 23.
LiquidAI has introduced LFM2.5-24B-A2B, a reasoning model with 24 billion total parameters and 2.45 billion active. Built for private on-device agents, it claims to compete with Qwen3.6-35B-A3B and run up to 1.7 times faster than comparable MoEs, wit...
