Majestic Labs Unveils Prometheus Server to Overcome AI Memory Limitations
New Server Hopes to Break Through AI’s “Memory Wall”

Image: Ieee Spectrum - Robotics
Majestic Labs is developing the Prometheus server, featuring up to 128 terabytes of memory, aimed at addressing the memory constraints of large language models (LLMs). This innovative architecture utilizes DRAM to enhance performance and reduce costs, with expected shipping in 2027.
- 01Prometheus will have over 60 times the memory of Nvidia's DGX B300 server, with a focus on DRAM architecture.
- 02The server design includes a proprietary memory interface using miniature copper cables for improved bandwidth.
- 03Prometheus will support major AI frameworks like PyTorch and OpenAI's Triton without requiring code changes.
- 04Customers can expect a 10 to 50 times reduction in capital expenditure and power consumption.
- 05Prometheus is set to ship in 2027, with pricing yet to be announced.
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Majestic Labs is tackling the significant memory limitations of large language models (LLMs) with its upcoming Prometheus server, which boasts a remarkable 128 terabytes of memory—more than 60 times that of Nvidia's DGX B300 server. Co-founder Sha Rabii emphasizes that traditional AI servers often over-provision compute resources while underutilizing memory. To address this, Prometheus employs a DRAM-centric architecture, utilizing LPDDR6 and a unique memory interface with miniature copper cables to achieve bandwidth of up to 25.6 terabytes per second. The server features 12 Ignite chips, which combine ARM and RISC-V cores for efficient LLM processing. Importantly, Prometheus will support existing AI frameworks like PyTorch and Triton without code modifications, easing customer adoption. Majestic Labs claims that this innovative design will significantly lower capital and power costs, with expectations of a 10 to 50 times reduction. Although pricing details remain undisclosed, Prometheus is anticipated to ship in 2027.
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The introduction of Prometheus could significantly lower operational costs for businesses utilizing AI technologies.
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