Purpose-Built Silicon for AI

The Future of Transformer Inference

10x Faster. Dramatically Cheaper.

CRETA has developed a revolutionary ASIC chip designed exclusively to run transformer models. Unlike general-purpose GPUs, our purpose-built silicon delivers an order of magnitude faster inference at a fraction of the cost, with a single eight-chip server serving over 500,000 tokens per second.

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500K+
Tokens/Second
10x
Faster Than GPUs
90%
Lower TCO
8
Chip Server Config
Our Vision

Betting on the Transformer Architecture

At CRETA, we've made a bold bet: transformers will remain the dominant paradigm in artificial intelligence for the foreseeable future. Since the introduction of "Attention Is All You Need" in 2017, transformer architectures have revolutionized not just natural language processing, but computer vision, audio processing, protein folding, and countless other domains. This isn't a passing trend—it's a fundamental shift in how we build intelligent systems.

General-purpose GPUs were never designed for transformers. They're versatile, yes, but that versatility comes at a cost: inefficient memory access patterns, wasted compute cycles, and excessive power consumption. The attention mechanism at the heart of every transformer model has unique computational characteristics that cry out for specialized silicon. That's exactly what we've built.

Our ASIC architecture is optimized from the ground up for the specific operations that transformers require: massive parallel matrix multiplications, efficient softmax computations, optimized memory bandwidth for key-value caching, and hardware-accelerated attention mechanisms. The result is a chip that doesn't just run transformers—it was born to run them.

Attention-Optimized Purpose-Built Silicon Advanced Process Node Enterprise-Ready
Explore Our Architecture
Core Technology

Silicon Engineered for Intelligence

Every component of the CRETA chip has been meticulously designed to accelerate transformer inference. From our custom memory architecture to our specialized compute units, we've eliminated the bottlenecks that limit GPU performance for LLM workloads.

Transformer-Native Architecture

The CRETA ASIC represents a fundamental rethinking of how silicon should process transformer workloads. Rather than adapting a general-purpose architecture, we started with the mathematical operations at the heart of transformer inference and designed custom hardware to execute them with maximum efficiency. Our architecture natively supports multi-head attention, feed-forward networks, and layer normalization with dedicated hardware units that eliminate the overhead of software emulation.

  • Hardware-accelerated multi-head attention with configurable head counts
  • Native support for rotary position embeddings (RoPE) and ALiBi
  • Optimized softmax units with numerical stability guarantees

High-Bandwidth Memory System

Transformer inference is fundamentally memory-bound. The key-value cache that stores attention context grows linearly with sequence length and batch size, creating massive memory bandwidth requirements. Our custom HBM3E memory subsystem delivers unprecedented bandwidth with intelligent prefetching that anticipates access patterns unique to autoregressive generation.

  • 6.4 TB/s memory bandwidth per chip
  • 256 GB HBM3E per chip capacity
  • Intelligent KV-cache compression

Tensor Compute Units

Our custom tensor compute units (TCUs) are designed specifically for the matrix operations that dominate transformer workloads. Unlike GPU tensor cores that must support a wide variety of operations, our TCUs are laser-focused on the specific computations needed for inference, enabling higher utilization rates and more efficient power consumption.

  • 2,048 tensor compute units per chip
  • Native FP8/FP16/BF16 support
  • 95%+ compute utilization

Chip-to-Chip Interconnect

Large language models often exceed the memory capacity of a single chip, requiring model parallelism across multiple devices. Our proprietary interconnect fabric enables near-linear scaling across up to 8 chips per server, with custom protocols optimized for the communication patterns of tensor and pipeline parallelism in transformer inference.

  • 1.6 TB/s chip-to-chip bandwidth
  • Sub-microsecond latency
  • Full-mesh topology support

Comprehensive Software Stack

Hardware is only half the equation. Our software stack provides seamless integration with popular frameworks and serving systems. Drop-in compatibility with vLLM, TensorRT-LLM, and other serving frameworks means you can migrate your existing infrastructure with minimal code changes while immediately benefiting from our hardware acceleration.

  • PyTorch and JAX integration
  • OpenAI-compatible API server
  • Kubernetes operators for orchestration

Exceptional Power Efficiency

Data centers face increasing pressure to reduce power consumption. Our purpose-built architecture eliminates the wasted transistors and power consumption of general-purpose compute, delivering dramatically more tokens per watt than any GPU solution. This translates directly to lower operating costs and reduced environmental impact.

  • 450W typical power consumption
  • 5x better tokens per watt vs. GPUs
  • Advanced thermal management
Performance

Benchmarks That Speak for Themselves

Real-world performance metrics comparing a single CRETA 8-chip server against equivalent GPU configurations. All benchmarks performed on Llama 3.1 70B with standard inference workloads.

Metric
CRETA (8-chip)
8x H100 SXM
Peak Throughput Maximum tokens generated per second at batch size 256
CRETA 512,000 tok/s 10x
8x H100 48,000 tok/s
Time to First Token Latency from request to first generated token (batch size 1)
CRETA 12ms 4x
8x H100 48ms
Inter-Token Latency Average time between consecutive tokens during generation
CRETA 8ms 3x
8x H100 24ms
Power Consumption Total system power draw under full inference load
CRETA 3.6 kW 2x
8x H100 7.2 kW
Cost per Million Tokens Total cost of ownership amortized over 3 years
CRETA $0.008 12x
8x H100 $0.10
Maximum Context Length Supported sequence length without degradation
CRETA 256K tokens 2x
8x H100 128K tokens

Throughput by Model Size

Llama 3.1 8B 1.2M tok/s
Llama 3.1 70B 512K tok/s
Llama 3.1 405B 180K tok/s
Mixtral 8x22B 420K tok/s

Latency by Batch Size

Batch 1 12ms TTFT
Batch 8 15ms TTFT
Batch 32 22ms TTFT
Batch 256 45ms TTFT
System Architecture

Full-Stack Inference Platform

From silicon to software, every layer of the CRETA platform is engineered to work together seamlessly, providing a complete solution for transformer inference at scale.

Application Layer

OpenAI-Compatible API

Drop-in replacement for OpenAI API endpoints, supporting chat completions, embeddings, and function calling with identical request/response formats.

gRPC Streaming Interface

High-performance streaming interface for latency-sensitive applications with support for bidirectional streaming and custom metadata.

Prometheus Metrics

Comprehensive observability with detailed metrics on throughput, latency distributions, queue depths, and resource utilization.

Runtime Layer

Continuous Batching Engine

Dynamic batching system that continuously adds and removes requests from running batches, maximizing throughput while maintaining SLA compliance.

PagedAttention Manager

Memory-efficient KV-cache management using paged allocation, enabling larger batch sizes and longer context lengths without memory fragmentation.

Speculative Decoding

Hardware-accelerated speculative decoding with draft model support, achieving 2-3x speedup on autoregressive generation for compatible workloads.

Compiler & Optimization

Model Compiler

Advanced ahead-of-time compiler that optimizes model graphs for our hardware, performing operator fusion, memory layout optimization, and automatic parallelization.

Quantization Engine

Automatic quantization with calibration support for FP8, INT8, and INT4 inference with minimal accuracy loss, validated against reference implementations.

Kernel Generator

Auto-tuning kernel generator that produces optimized implementations for specific model architectures, attention patterns, and batch configurations.

Hardware Layer

Tensor Compute Units

2,048 custom tensor compute units per chip with native matrix multiply-accumulate operations optimized for attention and feed-forward computations.

HBM3E Memory

256GB high-bandwidth memory per chip with 6.4 TB/s bandwidth, intelligent prefetching, and hardware KV-cache management.

Interconnect Fabric

1.6 TB/s chip-to-chip interconnect with full-mesh topology support, enabling efficient tensor and pipeline parallelism across 8 chips.

Applications

Built for Enterprise AI Workloads

From conversational AI to real-time content generation, CRETA silicon powers the most demanding transformer inference workloads across industries.

Large-Scale LLM Serving

Cloud providers and AI companies running production LLM inference at scale see dramatic improvements in both cost efficiency and user experience. Our silicon enables serving millions of concurrent users with sub-50ms latencies while reducing infrastructure costs by up to 90%.

10M+
Daily Users
<50ms
P99 Latency
90%
Cost Reduction

Conversational AI

Power real-time chatbots and virtual assistants with human-like response times that keep users engaged.

Code Generation

Accelerate developer productivity with instant code completions, explanations, and refactoring suggestions.

Content Creation

Generate marketing copy, articles, and creative content at scale without waiting for slow generation.

Document Analysis

Process and analyze lengthy documents with 256K context windows for comprehensive understanding.

RAG Systems

Build retrieval-augmented generation pipelines with low-latency embedding and generation capabilities.

Real-time Translation

Enable instantaneous translation across languages for global communication and content localization.

Summarization

Condense lengthy reports, meetings, and research into actionable summaries in seconds.

Getting Started

Deploy in Three Steps

We've designed the CRETA deployment experience to be seamless. Whether you're migrating from GPUs or building a new inference infrastructure, our team will guide you every step of the way.

01

Assessment & Planning

Our solutions architects work with your team to understand your current inference workloads, performance requirements, and infrastructure constraints. We'll develop a migration plan tailored to your specific needs.

  • Workload analysis and benchmarking
  • Capacity planning and sizing
  • ROI and TCO analysis
  • Custom deployment timeline
02

Integration & Optimization

Our engineering team helps you integrate CRETA into your existing infrastructure. We'll optimize your models for our hardware, configure the software stack, and ensure seamless operation with your current systems.

  • Model conversion and optimization
  • API integration and testing
  • Performance tuning and validation
  • Observability and monitoring setup
03

Launch & Scale

Go live with confidence. Our team provides ongoing support to ensure smooth operations, help you scale as demand grows, and keep your deployment running at peak performance with regular updates and optimizations.

  • Phased rollout and validation
  • 24/7 technical support
  • Regular software updates
  • Scalability consulting
Technical Specifications

Detailed Hardware Specifications

Complete technical specifications for the CRETA inference accelerator and server configurations.

CRETA Inference Chip

  • Process Node 4nm FinFET
  • Transistor Count 92 billion
  • Tensor Compute Units 2,048
  • Peak INT8 Performance 4.8 PetaOPS
  • Peak FP16 Performance 2.4 PetaFLOPS
  • Memory Type HBM3E
  • Memory Capacity 256 GB
  • Memory Bandwidth 6.4 TB/s
  • Interconnect Bandwidth 1.6 TB/s
  • TDP 450W
  • Supported Precision FP32/FP16/BF16/FP8/INT8/INT4
  • Die Size 814 mm²

CRETA Server (8-chip)

  • Form Factor 4U Rackmount
  • Accelerator Count 8x CRETA Chips
  • Total Memory 2 TB HBM3E
  • Aggregate Bandwidth 51.2 TB/s
  • Peak Throughput 500K+ tok/s
  • Host CPU 2x AMD EPYC 9654
  • System Memory 1 TB DDR5
  • Networking 8x 400GbE
  • Storage 30 TB NVMe SSD
  • Power Supply 6x 3000W (N+1)
  • Typical Power Draw 5.5 kW
  • Cooling Liquid Cooled
About CRETA

Pioneering the Next Generation of AI Infrastructure

CRETA was founded with a singular vision: to build the world's most efficient silicon for transformer inference. Our team of semiconductor veterans and AI researchers came together with the conviction that the future of artificial intelligence demands purpose-built hardware, not repurposed graphics processors.

We've assembled a world-class team with deep experience in chip design, compiler development, and large-scale ML systems. Our engineers have led projects at leading semiconductor companies, built production inference systems serving billions of requests, and published seminal research in computer architecture and machine learning.

Our Mission

"To make AI inference faster, cheaper, and more accessible by building silicon that's purpose-built for the transformer architecture that powers modern artificial intelligence."

Founded 2023
Headquarters Los Angeles, CA
Focus AI Inference Silicon
Technology Transformer ASIC
Get in Touch

Ready to Transform Your AI Infrastructure?

Whether you're exploring options for your next-generation inference platform or ready to deploy CRETA silicon today, our team is here to help. Contact us to discuss your requirements, schedule a technical deep-dive, or request a demonstration.

Email contact@getcreta.com
Phone (334) 687-2581
Headquarters 810 E Pico Blvd, Unit 101
Los Angeles, CA 90021
Website getcreta.com