Pricing4d ago
Cohere closes first tranche of Series E targeting up to C$7B at a C$35.7B valuation, with Canada taking an equity stake of up to C$1.6B
On October 29, Cohere announced the initial close of a Series E targeting up to C$7 billion at a C$35.7 billion valuation, led by Schwarz Group with NVIDIA, Bell Ventures and others, which Cohere calls the largest private round in Canadian startup history. The same day Prime Minister Mark Carney said the government will make a staged equity investment of up to about C$1.6 billion; Cohere commits to keep its Canadian incorporation and headquarters and to invest more than C$4 billion and add 1,500+ jobs in Canada through 2031.
Release5d 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.
Release10d ago
NVIDIA open-sources Nemotron 3.5 Bolt E4B 4GB, a Gemma 4 E4B-based small model for local assistants, characters and robots with tool use
On 2026-10-23 (15:41 UTC) the official @NVIDIAAI account announced Nemotron 3.5 Bolt E4B 4GB, a small open model for local assistants, interactive characters and robots that can hold a conversation and use tools. The official Hugging Face model card says it is post-trained from Gemma-4-E4B-it, has 8B total parameters (4B effective), ships in Q4_K_M quantization, supports up to 64K input and 64K output tokens, and has a switchable thinking mode, plus a DFlash speculative-decoding drafter and a BF16 version. Weights, data and recipes are open under the OpenMDW-1.1 license, with commercial use allowed. On the model card the 4GB build scores 55.60 on MultiChallenge (Gemma4 E4B-it at the same quantization 39.36, Qwen3.5 4B 52.23), but trails Qwen3.5 4B on Tau2-Bench (52.32) and BFCL-v3 (67.31).
Discussion & Comments
0Sign in to join the discussion
Connect with AI developers to exchange benchmark insights.