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COMPANY · ENTITY #1730

BF16

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EVENT TIMELINE

3

MODELS · 1 SOURCE · Cohere

Cohere releases open-source "Transcribe Arabic" ASR model claiming best open-weight Arabic accuracy

Cohere published Transcribe Arabic, an open-source Arabic automatic speech recognition (ASR) model (Apache 2.0) based on its 2B ASR work. Cohere says the model achieves a 25.87 WER on the Hugging Face Arabic ASR leaderboard, outperforming Meta’s OmniASR-LLM-7B and OpenAI’s Whisper Large V3, and was preferred to Whisper in about 96% of human evaluation tests; weights are available on Hugging Face and via the Cohere API/Model Vault.

8.0

CODING · 1 SOURCE · Cohere

Cohere publishes megakernel serving engine for North Mini Code with faster H100 decoding

Cohere describes a megakernel-based serving engine for its North Mini Code 30B model that runs BF16 on a single NVIDIA H100 and claims 1.25×–1.41× end-to-end speedup over vLLM, with a reported 292 tok/s (62% of SoL) at batch size 1 — about 1.58× faster than vLLM. The system supports production features (continuous batching, paged attention, ragged sequences), an OpenAI-compatible endpoint with tool calling, is implemented as a single CUDA file, and the code is available on GitHub.

7.0

RESEARCH · 1 SOURCE · Google Research

Google Research outlines software optimizations for mixed-input matmul on NVIDIA Ampere (CUTLASS)

A Google Research blog post by Manish Gupta presents software techniques to implement mixed-input matrix multiplication (e.g., F16 inputs × U8 weights) on NVIDIA Ampere Tensor Cores by handling data-type conversion and layout conformance; the methods are released in the open-source NVIDIA/CUTLASS repository and are reported to add minimal software overhead while approaching hardware peak performance. The work targets memory- and compute-heavy LLM workloads by enabling weight-only quantization patterns that reduce model memory footprint.

7.0