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BLOOM

Model ● Covered in 6 stories Compare ⇄ + Follow

BLOOM is a 176-billion-parameter open-source multilingual language model developed through a collaboration of over 1,000 researchers and released in 2022, capable of generating text in 46 natural languages and 13 programming languages. The model has been the subject of multiple optimization efforts, including inference acceleration techniques using DeepSpeed and quantization methods like 8-bit compression that enable it to run on more accessible hardware. BLOOM's release under the BigScience RAIL license established an influential template for responsible AI licensing frameworks that balance open access with use-based restrictions.

Updated 3 August 2026

Specifications

No specifications recorded yet.

Latest developments

Petals TLDR Dev · 1 week ago · 34

Incredibly Fast BLOOM Inference with DeepSpeed and Accelerate Hugging Face Blog · 3 years ago · 1

OpenRAIL: Towards open and responsible AI licensing frameworks Hugging Face Blog · 3 years ago · 26

A Gentle Introduction to 8-bit Matrix Multiplication for transformers at scale using transformers, accelerate and bitsandbytes Hugging Face Blog · 3 years ago · 47

The Technology Behind BLOOM Training Hugging Face Blog · 4 years ago · 12

Introducing The World's Largest Open Multilingual Language Model: BLOOM Hugging Face Blog · 4 years ago · 29

Timeline

Month Quarter Year

Q3 2026

23 Jul Petals

Q3 2022

BLOOM, a 176-billion parameter multilingual language model, released as open-source by international research collaboration Open source release

Relationships

Products & technology

Petals deploys this model · 1 source
DeepSpeed integrated with this model · 1 source
Hugging Face integrated with this model · 1 source
Jean Zay supplies this model · 1 source
NVIDIA supplies this model · 1 source
BigScience develops this model · 1 source

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TLDRocket 3 August 2026

The story of AI in August 2026 is one of consolidation meeting enforcement. Alibaba's Qwen3.8-Max, a 2.4-trillion-parameter mixture-of-experts model with a million-token context window, arrives as a direct challenge to OpenAI and Anthropic's frontier offerings—priced at $2 per million input tokens, it's positioning itself as the capable-and-cheaper alternative that China's AI push has long promised. But the more consequential development is structural: enterprises are finally getting the plumbing to actually deploy these models. June, backed by Salesforce's Marc Benioff, raised $20 million to automate AI integration into legacy systems, scanning corporate infrastructure and generating deployment workflows without expensive consultant fees. This solves what's become clear as the real bottleneck—not model capability, but implementation friction in messy, heterogeneous corporate environments.

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