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Background

The AI landscape has witnessed a profound transformation in language models. What started as a field dominated by a handful of closed-source companies has evolved into a vibrant open-source ecosystem that’s not just catching up—it’s leading innovation.

This shift is most evident in recent benchmarks:

  1. Grok 2, when quantized to 4-bit precision, has shown to match or come very close to GPT-4 in coding tasks, demonstrating that even smaller models can achieve high performance through quantization techniques.
  2. DeepSeek-R1 has been shown to match or exceed OpenAI’s o1 in mathematical reasoning benchmarks, illustrating the competitive edge of open-source models in niche areas.
  3. The R1 model family by DeepSeek continues to demonstrate how specialized models can achieve superior performance in targeted tasks, maintaining transparency in their training processes.

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Current state

Today’s LLM ecosystem is characterized by three key developments:

Open Source Dominance

The gap between closed and open-source models has reversed:

Quantized open models like Grok 2 achieve GPT-4 level performance

DeepSeek’s R1 family outperforms proprietary models in specialized domains

Transparent training processes enable targeted optimizations

Democratized Deployment

Small Language Models (SLMs) have revolutionized edge deployment:

3B parameter models achieve production-grade performance

Eval degradation across the quantization spectrum is minimal

Edge-optimized architectures enable IoT device participation

Inference Diversity

Multiple inference patterns are now supported:

Text-Generation-Inference for high-throughput

llama.cpp for edge deployment

ONNX for standardized inference

Custom engines for specialized hardware

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Key limitations

Despite these advances, significant challenges remain:

  1. Most inference runs on centralized cloud providers
  2. Hardware dependencies create vendor lock-in
  3. Privacy concerns with centrally processing sensitive data

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Ritual’s Innovation

Ritual introduces sovereign compute for LLMs through three key innovations:

1

Universal Inference Layer

Our execution sidecars abstract away infrastructure complexity with support for:

  • Any model architecture
  • Any inference engine (TGI, llama.cpp, ONNX)
  • Any hardware profile (CPU, GPU, NPU, including Apple Silicon)
2

Verifiable Execution

Leveraging Symphony’s dual proof sharding, we offer:

  • Guaranteed model authenticity
  • Verifiable inference results
  • Privacy-preserving execution through TEEs
3

Sovereign Deployment

True ownership of your AI stack:

  • Run models anywhere, from edge to cloud
  • No central infrastructure dependencies
  • Full control over model and data privacy

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Beyond Inference

While we start with inference, our platform is designed for the full AI lifecycle. Our vTune framework enables:

  • Model fine-tuning
  • Architecture adaptation
  • Performance optimization
  • Specialized training

Through Ritual’s execution sidecars, we’re not just deploying models—we’re enabling a new paradigm of sovereign, verifiable AI compute that works with any model, engine, and hardware.