Meta revives open AI push with Muse Glimmer

Meta has released Muse Glimmer, a 30-billion-parameter open-weight artificial intelligence model designed to run agentic workloads on consumer hardware, while preparing an open-weight version of its flagship Muse Spark 1.2 model for release within weeks.

The launch marks a renewed push by Meta to make downloadable AI models a central part of its strategy after its Muse family initially moved towards controlled access. Muse Glimmer, released on August 10, can be downloaded and modified by developers and is designed to operate locally without requiring continuous access to cloud infrastructure.

Glimmer was distilled from Meta’s larger Muse Spark model and combines a language model with a dedicated perception encoder. It can process text and images, conduct multi-step reasoning, call software tools and attempt to recover when tasks fail. Meta has positioned those capabilities around autonomous agents capable of handling extended workflows rather than simply responding to individual prompts.

The model is being distributed under the Apache 2.0 licence, giving developers broad scope to use and adapt it. Its 30-billion parameters put it below the scale of the largest frontier systems, but Meta has focused on making the model practical for local deployment. Quantised versions can run on a single high-end consumer graphics processor or compatible Mac hardware.

Local execution could give companies greater control over sensitive information because prompts and documents need not automatically be sent to an external AI provider. It may also reduce inference costs for organisations running large numbers of repetitive AI tasks, although users must provide computing infrastructure and manage deployment, security and updates themselves.

Meta is also preparing to open the weights of Muse Spark 1.2, its most capable model. Spark 1.2 already powers Muse Code, the company’s coding agent introduced this month. The model is designed for software development, reasoning and complex agentic operations, including the use of multiple sub-agents to work on different parts of a task simultaneously.

Opening Spark 1.2 would represent a significant change in Meta’s approach to its most powerful models. The original Muse Spark arrived in April as the first major model from Meta Superintelligence Labs and was offered through Meta AI and controlled developer access. Muse Spark 1.1, released in July, expanded coding, computer-use and multimodal capabilities while remaining closed-weight.

Meta’s renewed emphasis on downloadable models also revives an approach associated with its Llama generation of AI systems. The company helped accelerate the open-weight ecosystem by allowing developers to download Llama models and customise them rather than relying entirely on hosted application programming interfaces.

Competition has since intensified. Chinese developers including DeepSeek, Alibaba and other laboratories have expanded the performance and availability of open-weight systems, increasing pressure on US technology groups to provide alternatives that developers can operate independently. OpenAI, Anthropic and Google continue to rely heavily on controlled access for their leading commercial models.

Mark Zuckerberg has argued that broader access to advanced AI would prevent computing intelligence from becoming concentrated among a small number of companies and governments. Alongside the Glimmer launch, the Meta chief set out a wider case for giving individuals and developers more control over increasingly capable systems and called for US policies that support open AI development.

That position remains contested as AI systems acquire stronger capabilities in coding, cybersecurity, scientific reasoning and autonomous tool use. Open-weight releases allow researchers and developers to inspect, modify and fine-tune models, but they can also make provider-level restrictions harder to enforce once the software has been downloaded.

Meta itself has acknowledged that increasingly capable systems require safeguards. Its evaluations of earlier Muse Spark models covered cybersecurity, chemical and biological risks and potential loss-of-control scenarios, with mitigations applied before deployment. Making model weights available introduces a different risk profile because downstream developers can alter safeguards or use the underlying system in configurations that Meta does not directly control.