Release7d ago
NVIDIA open-sources Nemotron 3.5 Transcribe, a 30B MoE speech model with about 4B active parameters that handles transcription in 40 languages, speaker attribution and translation, and tops open models on the Open ASR English leaderboard
NVIDIA AI announced Nemotron 3.5 Transcribe on Oct 28, with weights on Hugging Face and ModelScope. It is an offline, instruction-driven speech model whose single checkpoint covers transcription in 40 languages, translation between 31 languages and English, speaker attribution in 13 languages, and audio question answering, on recordings up to an hour long with up to eight labeled speakers. NVIDIA says it ranks first among open-weight models on the Hugging Face Open ASR English leaderboard at 5.33% average WER, and Artificial Analysis measured a 2.8% AA-WER, tied for first among open models with Voxtral Small, which uses 24B active parameters. The weights, the Granary v2 training data and fine-tuning recipes are all released.
Open Source20d ago
NVIDIA open-sources Nemotron 3.5 Super VL (120B-A12B multimodal agent model, 1M context) and the 24GB 2-bit local Bolt 74B-A9B
On 2026-10-15 NVIDIA released Nemotron 3.5 Super VL, an open multimodal agent model with 120B total / 12B active parameters: a Mamba-2–Transformer hybrid MoE with a C-RADIO vision encoder, text/image/document/video input, up to 1M context, and built-in MTP plus LiLiCorr speculative decoding. Weights, datasets and recipes ship under OpenMDW-1.1 on Hugging Face, OpenRouter, ModelScope and NIM. Alongside it, NVIDIA published Bolt 74B-A9B 24GB, a natively 2-bit (QAT) local agent model for RTX/DGX Spark; the 4GB Bolt E4B follows on Oct 23.
Inference14d ago
vLLM and Ant Group Open-Source Humming: Up to 5x Kernel Acceleration and +43.3% MoE Throughput
The vLLM project team and Ant Group officially open-sourced Humming (humming-kernels), a specialized high-performance kernel library designed for serving quantized MoE and dense LLMs at massive scale. Humming natively supports WnA16, W8A8, W4A8, and W4A4 across INT1–INT8 and FP4–FP8 formats, spanning NVIDIA architectures from Turing through Blackwell as well as T-Head Zhenwu 810E silicon. In production benchmarks, Humming kernels outpace cuBLAS by 3–4x at small batch sizes and surpass Marlin by 2x on H200 clusters. Serving DeepSeek-V4.1-Flash across 8x H20 GPUs, Humming drives a +43.3% throughput gain under 64 concurrent requests while reducing time-to-first-token (TTFT) by 32%, pre-integrated directly into vLLM v0.29.0+.
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