Majestic Labs
Majestic Labs helps enterprises run massive AI models efficiently with disaggregated memory architecture.
Majestic Labs builds Prometheus, a custom AI server that solves the memory wall problem by disaggregating memory from compute, enabling up to 128 TB of ultra-fast, power-efficient memory per server—nearly 100x more than leading GPU servers. The system allows organizations to run multi-trillion-parameter LLMs, mixture-of-experts models, and agentic AI systems in a single unit while dramatically reducing power consumption and infrastructure costs. Built by veteran silicon architects from Meta and Google, the company targets hyperscalers and enterprises that need to deploy massive AI models efficiently.
Problem solved
AI models are memory-bound; processors spend most of their time waiting for data rather than processing it, forcing organizations to overprovision expensive compute just to access required memory.
Target customer
Hyperscalers and large enterprises (financial services, pharmaceuticals, cloud providers) deploying trillion-parameter language models and complex AI workloads.
Founders
O
Ofer Shacham
CEO & Co-Founder
Former VP and Head of Silicon at Meta and Google; sold Chip Genesis to Google in 2013; holds Ph.D. and M.S. from Stanford, B.S. from Tel Aviv University.
S
Sha Rabii
President & Co-Founder
Sold Arda Technologies to Google in 2011; former senior director of engineering at Google; founding team member at Atheros Communications; Ph.D. in Electrical Engineering from Stanford.
M
Masumi Reynders
COO & Co-Founder
15-year Google veteran with background in legal and business development; rose to director of product management and silicon at Google.
Funding history
Seed
$10M
Unknown
Led by Lux Capital
· Unknown
Series A
$71M
September 2024
Led by Bow Wave Capital
· SBI, Upfront, Grove Ventures, Hetz Ventures, QP Ventures, Aidenlair Global, TAL Ventures
Total raised:
$100M
Industries
Pricing
Not publicly available. Prototypes available for select customers in 2027; company has begun discussions on pre-orders.
Tech stack
Open Graph
HSTS (Security)
Google Font API (Font scripts)
Varnish (Caching)
Fastly (CDN)
GitHub Pages (PaaS)
Website
Competitors
SambaNova
Develops dedicated AI chips but focuses on different memory architecture approaches; Majestic's disaggregated memory design enables significantly more memory bandwidth per server.
Cerebras
Builds large-scale AI processors but does not address the memory wall problem with Majestic's custom accelerator and memory interface approach.
Groq
Specializes in inference acceleration; Majestic focuses on enabling massive model deployment across training and inference with extreme memory capacity.
Tenstorrent
Develops AI accelerators but uses traditional GPU-like memory hierarchies; Majestic's disaggregated architecture provides 100x more memory per server.
NextSilicon
Israeli competitor in custom AI silicon but lacks Majestic's specific focus on solving the memory wall for trillion-parameter model deployment.
NVIDIA
Dominates GPU market but traditional GPU servers are memory-constrained; Majestic's server enables equivalent performance to multiple GPU racks in a single unit.
Why this matters: Majestic Labs is backed by three veteran silicon architects who shipped hundreds of millions of custom chips at Google and Meta, tackling one of AI's most fundamental bottlenecks: the memory wall. With $100M in funding and a 2027 prototype timeline, the company represents a credible challenge to NVIDIA's infrastructure dominance for memory-bound AI workloads, potentially reshaping how enterprises deploy trillion-parameter models.
Best for: Hyperscalers and enterprises deploying trillion-parameter language models, mixture-of-experts systems, and complex AI workloads that are memory-bound on traditional GPU infrastructure.
Use cases
Deploying Multi-Trillion-Parameter LLMs
Organizations can run the most advanced language models with massive context windows (hundreds of millions of tokens) in a single Prometheus server. This eliminates the need to shard models across multiple GPU servers, reducing latency and operational complexity for applications requiring very large models.
Running Mixture-of-Experts Models Efficiently
Complex mixture-of-experts architectures that require substantial memory for parameter access can run on Prometheus without the memory bottleneck. This enables faster inference and training compared to GPU-based systems that must manage memory spilling and data movement.
Agentic AI Systems with Large Context Windows
AI agents that maintain long conversation histories and access large knowledge bases can execute entirely on a single server. This reduces the infrastructure footprint and latency for real-time AI applications in financial services, pharmaceutical research, and customer service.
Graph Neural Networks and Tabular Model Training
Large-scale graph and tabular models that cannot fit efficiently on conventional GPU infrastructure can be trained and deployed on Prometheus, enabling data-intensive machine learning workloads at hyperscaler scale.
Alternatives
GPU Clusters (NVIDIA H100/H200)
Traditional approach requiring multiple servers and complex sharding; Prometheus achieves equivalent performance in a single ultra-efficient unit with lower power consumption and data center footprint.
SambaNova Systems
Offers purpose-built AI chips but with different memory architecture; choose SambaNova if seeking broader software ecosystem, Majestic if prioritizing extreme memory bandwidth and density.
Cerebras
Focuses on large-scale processor design; choose Cerebras for specific wafer-scale compute paradigm, Majestic for disaggregated memory approach with standard server form factors.
FAQ
What does Majestic Labs do? +
Majestic Labs builds Prometheus, an AI server with custom accelerator and memory interface chips that disaggregates memory from compute, enabling up to 128 TB of ultra-fast memory per server. This solves the memory wall problem where processors spend most of their time waiting for data rather than processing it, enabling enterprises to deploy massive AI models efficiently in a single unit instead of across multiple racks.
How much does Majestic Labs cost? +
Pricing is not publicly available. Prototypes will be available for select customers starting in 2027, with the company already discussing pre-orders with interested organizations.
What are alternatives to Majestic Labs? +
Competitors include NVIDIA GPU clusters (traditional approach), SambaNova (dedicated AI chips), Cerebras (large-scale processors), Groq (inference acceleration), Tenstorrent (AI accelerators), and NextSilicon. Each offers different tradeoffs in memory architecture, inference vs. training focus, and form factor.
Who uses Majestic Labs? +
Target customers are hyperscalers and large enterprises from financial services, pharmaceuticals, and cloud infrastructure sectors that deploy trillion-parameter language models and complex AI workloads. Specific customer names have not been publicly disclosed.
How does Majestic Labs compare to NVIDIA? +
NVIDIA dominates with mature GPU clusters but traditional GPUs face memory constraints requiring sharding models across multiple servers. Majestic's disaggregated memory architecture achieves equivalent performance to multiple GPU racks in a single ultra-efficient unit with dramatically lower power consumption and data center footprint, making it ideal for memory-bound AI workloads.
When will Majestic Labs be available? +
Prototypes are expected in 2027. The company has begun pre-order discussions with select customers but full commercial availability has not been announced.
Who founded Majestic Labs? +
The company was co-founded by Ofer Shacham (CEO, former VP of Silicon at Meta), Sha Rabii (President, former senior director at Google), and Masumi Reynders (COO, 15-year Google veteran). All three spent years leading silicon products together at Meta and Google.
Tags
AI infrastructure
memory bandwidth
GPU alternative
custom silicon
AI servers
trillion-parameter models
hyperscaler infrastructure