Intel has enabled day-0 support for Meta’s Muse Spark Open across Intel Xeon processors and Intel data center GPU platforms, Intel GPU D-series, through targeted low-bit model quantisation.
Meta’s official open-weight release of Muse Spark Open, an 816-billion-parameter any-to-text model, represents a major milestone in multimodal AI and requires extreme memory efficiency and hardware-level low-precision acceleration.
Muse Spark Open is a coding-optimised open-weight model. Trained specifically for agentic coding workflows, it delivers higher first-attempt accuracy, reliable tool calling and deep codebase understanding across the repositories developers use.
The open-weight model is strong in code generation, debugging and codebase understanding. The 1-million-token context window handles entire projects, including complex dependencies, legacy code and thousands of files, without requiring tasks to be broken into smaller pieces.
Muse Spark Open also supports multimodal understanding, including audio, video, image and document comprehension.
For agentic use cases, Muse Spark Open was built to plan, act, observe and self-correct over extended workflows. It is trained to browse the web, including multimodal content, use an agentic harness to spawn and coordinate subagents for decomposable work, and leverage computer-use capabilities across desktop, mobile and browser environments.
Large language models at the scale of Meta’s Muse Spark Open highlight the growing infrastructure demands of advanced AI workloads. At hundreds of billions of parameters, they require substantial memory capacity, efficient low-precision compute, power efficiency and architectures that can bring these capabilities into existing data center footprints.
Muse Spark Open has been tested and is running on the upcoming Intel® GPU D-series, today. This early work reflects Intel’s commitment to day-0 support for emerging AI models and gives customers an early path to begin evaluating Muse Spark Open on Intel silicon.
Intel GPU D-series is designed for the demands of large and agentic AI models. Built on the Intel Xe3P architecture, it features:
- 32 Xe3P cores
- 256 Intel XMX engines
- Dedicated hardware acceleration across a broad range of data types, including MXFP4
- 160 GB LPDDR5x memory capacity, with options up to 480 GB
These capabilities are well aligned with large-model inference, combining low-precision compute with high memory capacity to support models such as Muse Spark Open efficiently. Its 350W air-cooled PCIe form factor also simplifies deployment. A compact four-card configuration can host an 816 billion-parameter-class model within conventional air-cooled data center racks, helping reduce infrastructure complexity for organizations evaluating frontier-scale AI.