Building the Full-Stack AI Future: Chip, Runtime, and Models

Building the Full-Stack AI Future: Chip, Runtime, and Models

For too long, AI hardware and AI research have lived in silos. Hardware vendors chased TOPS and throughput. Model builders focused on accuracy and benchmarks. The burden of integration fell to device makers and enterprises – driving up cost, slowing adoption, and creating dependencies.

At SandLogic, we believe this is broken. That’s why we took a bold step: to become a full-stack AI company – building the chip, runtime, and models under one roof.

Article content — Chip to Model Zoo, one true native, sovereign full-stack for device makers

This is not three disconnected efforts. It is one tightly integrated stack designed for the future of AI.

1. Krsna AI Chip – India’s Own AI Processor

Patented architecture inspired by SIMD, designed for efficiency.
22 TOPS performance, under 2W power.
Supports all workloads – CNNs, LLMs, MoEs, multi-modal inference.
Comes with an open-source aligned compiler and runtime stack for easy adoption.

Krsna is not just about performance metrics. It’s about sovereignty, adaptability, and usability. By building indigenous silicon, India can reduce dependence on foreign processors and create a “Make in India, Make for the World” success story.

2. EdgeMatrix Runtime — Orchestrating AI Everywhere

Hardware is only as useful as the software that drives it. That’s why we built EdgeMatrix, our runtime layer:

Reduces compute needs drastically with kernel-level optimizations.
Accelerates token generation for LLMs.
Cuts 30% cost and power directly at deployment.
Runs on GPUs, CPUs, ARM devices, and Krsna AI Chip.
Tested on models up to 70B parameters – including CPU-only scenarios.

3. Shakti LLM Models – Sovereign and Enterprise-Ready

No AI chip is complete without models tuned for it. Our Shakti LLMs are India’s native foundational models:

Novel architectures for enterprise + on-device use.
Released six models (100M → 4B); 8B and 30B are in progress.
Built for efficiency – supports quantization down to INT4.
Papers published and accepted by Springer, validating our research rigor.

Shakti is not just a model family. It is the validation playground where every kernel, quantization method, and optimization in Krsna and EdgeMatrix is tested end-to-end.

Why Full-Stack Matters

Today, device makers juggle multiple vendors: one for chips, another for runtimes, another for models. This creates:

Higher TCO from integration costs.
Longer time-to-market due to complexity.
Dependence on foreign IPs with uncertain licensing.

But the deeper truth is this: raw TOPS per watt is no longer the metric that matters.

The real benchmark is Energy Per Inference (EPI) — how much energy it takes to generate each token, each prediction, each decision. And EPI is dictated not just by compute units, but by DDR traffic, memory movement, and software orchestration. A chip-only company cannot solve this. A model-only company cannot solve this. Only a full-stack company can.

SandLogic’s approach – krsna+edgematrix+Shakthi LLM and any other model is also supported for reduced ddr fetches and hardware compression, optimize kernels, and align models with silicon. That’s how we cut energy per inference, not just quote peak TOPS.

The Market Is Signaling the Next Frontier

This isn’t just strategy; it’s backed by hard numbers.

The Edge AI Chips market, worth just a few billion today, is forecast to reach between USD 15-36 B by 2032 – 2034, with CAGRs near 18–22%.
The broader Edge AI market (hardware + software + services) is set to grow from ~USD 20.7 B in 2024 to USD 66.5 B by 2030, at ~21.7% CAGR.
Meanwhile, Agentic AI – autonomous agents that perceive, plan, and act – is projected to surge from ~USD 5-7 B today to USD 100-200 B+ by the early 2030s, with growth rates often above 40% CAGR.

The message is clear:

AI is moving to the edge.
Agentic AI will live on devices.

And this shift will demand silicon, runtimes, and models that are co-designed for efficiency, accuracy, and independence.

Make in India, Make for the World

SandLogic’s mission is deeply aligned with Make in India. By owning the chip, runtime, and model stack, we empower:

Device makers – who no longer need to integrate three different vendors.
Enterprises – who get sovereign, cost-effective AI solutions.
India’s AI ecosystem – which can stand on its own instead of importing critical technology layers.

This is about more than technology. It’s about reducing dependency, accelerating innovation, and giving India a voice in the global AI stack.

AI chipmaking is not about chasing higher TOPS. It’s about delivering usable, efficient AI experiences on real devices. That requires silicon, runtimes, and models designed together.

With Krsna AI Chip, EdgeMatrix Runtime, and Shakti LLMs, SandLogic is building that future – a full-stack AI ecosystem that reduces cost, complexity, and dependency, while unlocking the possibilities of on-device Agentic AI.

This is not three products. This is one integrated vision. This is the soul of SandLogic.

0 Comments