Turiyam AI 2026: India's Open GenAI Inference Platform Built on RISC-V and CUDA-Free Middleware
Turiyam AI is an Indian AI infrastructure company building a full-stack, open-source compute platform for GenAI inference at scale — spanning chips, systems, and software, on RISC-V hardware with CUDA-free middleware. Founded by five co-founders with deep roots in AI systems and semiconductor engineering, Turiyam AI's mission is to accelerate intelligence from India for the world — and to make AI compute as open, accessible, and cost-efficient as India's own Digital Public Infrastructure.
In 2026, as enterprises globally grapple with runaway AI inference costs and dependency on NVIDIA's CUDA ecosystem, Turiyam AI is emerging as a credible alternative: sovereign, open, and built for disruptive total cost of ownership (TCO). This guide covers what Turiyam AI is, why the problem they're solving matters, how their technology stack works, and who stands to benefit from their platform.
What Is Turiyam AI in 2026?
Turiyam AI is a Bengaluru-based AI compute startup building what it calls an "open platform for accelerating intelligence." In 2026, the company's core offering is a full-stack GenAI inference platform that covers every layer of the compute stack — from custom silicon (chips) and hardware systems to the middleware and software needed to deploy and run large language models in production.
The name "Turiyam" is derived from Sanskrit, referring to the fourth state of consciousness — a state of pure awareness beyond the three ordinary states of waking, dreaming, and deep sleep. The name signals the founding team's vision: a platform that goes beyond the limitations of existing AI compute paradigms.
Turiyam AI at a Glance 2026
| Detail | Information |
|---|---|
| Company Name | Turiyam AI |
| Website | turiyam.ai |
| Headquarters | India |
| Co-founders | Sanchayan Sinha, Parag Jain, Praveen Jain, Mukul Ojha, Shomy Sanyal |
| Core Product | Full-stack GenAI inference platform |
| Hardware | RISC-V based AI accelerators |
| Software | CUDA-free middleware, open-source models |
| Primary Focus | Enterprise GenAI inference at scale, disruptive TCO |
| Philosophy | Open platform modelled on India's DPI (UPI/Aadhaar) |
| Contact | pkjain@turiyam.ai |
Core Mission Statement 2026
Turiyam AI's stated mission is to "build an open platform for Accelerating Intelligence." The company frames its work around a single premise: AI Factories are the new Industrial Infrastructure, and tokens are the currency of Intelligence. Just as electricity grids and internet infrastructure became utilities, Turiyam believes AI compute will become foundational infrastructure — and they intend to build the open version of it from India.
Why AI Inference Infrastructure Matters in 2026
To understand why Turiyam AI exists, it's important to understand the problem they're solving. In 2026, the AI industry is experiencing a structural shift: training AI models gets the headlines, but inference is where the real business cost lives.
The Enterprise AI Cost Problem 2026
- Inference dominates AI spend: Industry estimates in 2026 suggest 80–90% of total AI compute costs for enterprises come from inference (serving models to users), not training
- NVIDIA dependency: The dominant AI compute stack — built on NVIDIA GPUs and CUDA — creates significant vendor lock-in and pricing power for NVIDIA
- Hyperscaler premiums: AWS, Azure, and Google Cloud charge significant premiums for managed AI inference, eroding margins for AI-first businesses
- Data sovereignty gaps: For Indian enterprises and government workloads, routing AI inference through foreign hyperscaler infrastructure raises compliance and sovereignty concerns
- Accessibility barriers: High inference costs keep many enterprises and startups from deploying AI in production at scale
The Inference Stack Problem in 2026
| Problem | Current Reality 2026 | Turiyam's Approach |
|---|---|---|
| Hardware dependency | NVIDIA GPU lock-in; CUDA ecosystem | RISC-V open hardware; CUDA-free stack |
| Cost of inference | Hyperscaler margins on top of GPU costs | Disruptive TCO via full-stack ownership |
| Data sovereignty | Foreign infrastructure for Indian data | India-based, sovereign AI compute |
| Platform openness | Proprietary, closed stacks | Open models, open middleware, open hardware |
| Customisation | Generic cloud instances | Tuned for specific models and enterprise workflows |
Turiyam AI's Technology Stack in 2026
Turiyam AI's platform spans three layers of the AI compute stack. This full-stack approach is what distinguishes it from pure-software AI platforms and from cloud providers who resell NVIDIA hardware. Here is how each layer works in 2026:
Layer 1: Hardware — RISC-V AI Accelerators
RISC-V (Reduced Instruction Set Computer - Five) is an open-standard instruction set architecture (ISA). Unlike ARM or x86, RISC-V is royalty-free and open, meaning companies can design custom chips without licensing fees. In 2026, Turiyam AI uses RISC-V as the foundation for its AI accelerators.
Why RISC-V for AI inference in 2026?
- No licensing cost: RISC-V silicon carries no ARM/Intel royalties, reducing hardware cost
- Full customisation: Turiyam can design accelerators optimised specifically for LLM inference workloads
- India's semiconductor mission: Aligns with India Semiconductor Mission (ISM) and domestic chip manufacturing goals
- CUDA independence: No dependency on NVIDIA's proprietary compute ecosystem
Layer 2: Middleware — CUDA-Free Software Stack
The middle layer is Turiyam's CUDA-free middleware — the software that sits between the hardware and the AI models, managing compute scheduling, memory, tensor operations, and model serving. Most AI frameworks (PyTorch, TensorFlow) are heavily optimised for NVIDIA CUDA. Turiyam builds a middleware layer that runs AI workloads on non-CUDA hardware without performance compromise.
This is technically non-trivial: CUDA's deep ecosystem integration means most AI models and libraries assume GPU+CUDA. Building a performant CUDA-free stack requires re-implementing or wrapping a significant portion of the compute graph at the kernel level.
Layer 3: Software — Open Models and AI Platform
The top layer is the AI software platform — the models, serving infrastructure, APIs, and enterprise tooling that customers interact with. Turiyam builds on open-source AI models (Meta's LLaMA family, Mistral, and similar), customises them for enterprise workflows, and serves them through its managed inference platform. This avoids proprietary model lock-in while delivering enterprise-grade reliability.
Full-Stack Architecture 2026
| Stack Layer | Turiyam's Component | Alternative in Market |
|---|---|---|
| Hardware | RISC-V AI accelerators | NVIDIA H100/H200 GPUs |
| Middleware | CUDA-free compute framework | NVIDIA CUDA / cuDNN |
| Model layer | Open-source LLMs (LLaMA, Mistral etc.) | OpenAI GPT / Anthropic Claude |
| Serving | Optimised inference engine for enterprise | AWS Bedrock / Azure OpenAI |
| Interface | Enterprise API + customisation layer | OpenAI API / HuggingFace endpoints |
Key Features and Capabilities of Turiyam AI in 2026
Turiyam AI positions three headline capabilities for enterprise customers in 2026:
1. GenAI Inference at Scale
Turiyam's core product is infrastructure for serving GenAI models at production scale. This means handling high-concurrency LLM inference requests — for applications like enterprise chatbots, internal knowledge bases, document processing, and AI-assisted workflows — with low latency and high throughput. The platform is designed to scale horizontally as request volumes grow.
2. Enterprise-Ready Customisation
Unlike generic cloud AI APIs, Turiyam AI tuners its platform for specific enterprise models and workflows. This includes:
- Model fine-tuning and adaptation for domain-specific tasks
- Custom inference optimisation for particular LLM architectures
- Workflow-specific latency and throughput tuning
- Dedicated deployment options for regulated industries
3. Disruptive Total Cost of Ownership (TCO)
TCO for AI inference includes hardware costs, software licensing, operational overhead, and vendor margins. Turiyam's full-stack ownership — from silicon to software — allows the company to eliminate third-party markups at each layer. By owning the RISC-V hardware design, building CUDA-free middleware, and using open-source models, Turiyam aims to offer enterprise AI inference at significantly lower cost than hyperscaler alternatives.
Who Should Use Turiyam AI in 2026?
Turiyam AI is not a consumer product — it targets organisations with significant GenAI inference workloads. The platform is particularly well-suited for:
Primary Use Cases 2026
| Segment | Use Case | Turiyam Advantage |
|---|---|---|
| Large enterprises | Internal AI assistants, document processing, code copilots | Dedicated, lower-cost inference; data stays on-premise or in India |
| AI-first startups | Customer-facing LLM products at scale | Disruptive TCO reduces unit economics burn |
| Government & PSUs | Sovereign AI for citizen services | India-based infrastructure; data localisation compliance |
| BPO/outsourcing firms | AI-augmented workflows, document intelligence | High-volume inference at lower cost per token |
| BFSI (banking, finance, insurance) | Regulatory-compliant AI workflows | On-premise or dedicated deployment options |
| Healthcare organisations | Clinical documentation, patient data AI | Data sovereignty + customised models |
Who Should Consider Alternatives in 2026?
Turiyam AI is a platform-level product. Organisations that need:
- Instant API access with no setup — standard cloud providers (AWS Bedrock, Azure OpenAI) offer faster on-boarding
- Access to GPT-4, Claude, or Gemini specifically — these proprietary models are not available on Turiyam
- Small-scale experimentation — the platform is optimised for scale, not hobbyist usage
Turiyam AI vs. Hyperscalers for GenAI Inference in 2026
How does Turiyam AI compare to established players for enterprise AI inference in 2026? Here's an honest assessment:
| Dimension | Turiyam AI | AWS Bedrock | Azure OpenAI | NVIDIA DGX Cloud |
|---|---|---|---|---|
| Hardware | RISC-V (open) | AWS Inferentia / NVIDIA | NVIDIA GPUs | NVIDIA A100/H100 |
| Software lock-in | None (open-source stack) | AWS SDK | Azure SDKs | CUDA / NVIDIA stack |
| Model access | Open-source LLMs | Multi-model (incl. Anthropic) | OpenAI + open models | Custom + NVIDIA NIM |
| Data sovereignty | India-based | Multi-region, US company | Multi-region, US company | US company |
| TCO potential | Disruptive (claimed) | Market rate | Market rate | Premium |
| Enterprise customisation | Deep (full-stack) | Moderate | Moderate | High (but expensive) |
| Maturity (2026) | Early-stage / Growth | Mature | Mature | Mature |
Important caveat for 2026: Turiyam AI is an early-stage company. Enterprises evaluating the platform should run rigorous proof-of-concept tests on performance, reliability, and actual TCO before committing at scale. The technology direction is compelling, but due diligence on production readiness is essential.
How Turiyam AI Reduces GenAI Inference Costs in 2026
The total cost of GenAI inference has three main components: compute (hardware), software (licensing, frameworks), and operations (management, support). Turiyam AI addresses all three:
Compute Cost Reduction 2026
NVIDIA GPU pricing — particularly for H100 and H200 accelerators — remains extremely high in 2026. By designing its own RISC-V AI accelerators, Turiyam avoids NVIDIA's pricing entirely. The cost advantage compounds when deployed at scale: enterprise clusters running millions of inference requests per day see the per-token cost reduction most dramatically.
Software Cost Reduction 2026
CUDA-based AI development carries hidden software costs: NVIDIA's proprietary tools, framework optimisation, and engineering time spent on GPU-specific code paths. Turiyam's open-source middleware eliminates these licensing dependencies and reduces the engineering overhead of maintaining a CUDA-dependent inference stack.
Operational Cost Reduction 2026
Full-stack ownership allows Turiyam to optimise the entire system — hardware, firmware, middleware, and model serving — as a cohesive unit. This vertical integration is how Apple achieves industry-leading performance-per-watt with Apple Silicon; Turiyam applies a similar philosophy to AI inference compute.
TCO Framework for Enterprise Evaluation 2026
| Cost Component | Hyperscaler | NVIDIA DGX (On-Prem) | Turiyam AI |
|---|---|---|---|
| Hardware capex | Zero (opex model) | Very high | Lower (RISC-V vs GPU) |
| Per-token compute cost | Hyperscaler markup | Hardware-amortised | Designed for disruption |
| Software licensing | Included in API cost | CUDA stack costs | Open-source (minimal) |
| Data egress & transfer | Significant at scale | None (on-prem) | India-based (low) |
| Data sovereignty compliance | Additional controls needed | Full control | India-native |
The "UPI Moment" for AI Compute in 2026
Turiyam AI's most provocative positioning is its analogy to India's Digital Public Infrastructure (DPI). India's Unified Payments Interface (UPI) created a frictionless, open payment layer that became the foundation for hundreds of fintech applications — from PhonePe to Paytm to BHIM. Before UPI, payment infrastructure was fragmented, expensive, and controlled by a few incumbents.
Turiyam AI argues that AI compute is at the same inflection point in 2026. The dominant AI compute infrastructure — NVIDIA GPUs, CUDA, AWS/Azure/GCP managed services — is expensive, proprietary, and concentrated in US companies. An open, accessible AI compute layer built from India could enable a similar explosion of AI applications across emerging markets that can't afford hyperscaler pricing.
The DPI Parallel: AI Compute as Infrastructure 2026
- Aadhaar: Open identity layer → any application can verify users → democratised financial inclusion
- UPI: Open payment layer → any app can process payments → $2 trillion+ in annual transactions
- Turiyam's vision: Open AI compute layer → any enterprise can deploy LLMs → democratised AI at scale
Whether this vision plays out depends on Turiyam AI's ability to match hyperscaler performance and reliability while delivering on the promised TCO advantage. But the framing is coherent and the market need is real: India and the broader Global South will not be able to build AI-first economies if every inference request is billed at US hyperscaler rates.
The Team Behind Turiyam AI in 2026
Turiyam AI was founded by five co-founders, each bringing technical depth across the AI and semiconductor stack. A founding team of five — rather than the typical two-to-three — signals that the company views this as a multi-disciplinary problem requiring simultaneous expertise in hardware design, AI systems, enterprise software, and operations.
Co-Founders 2026
| Co-Founder | Profile |
|---|---|
| Sanchayan Sinha | Co-founder, Turiyam AI (LinkedIn profile active) |
| Parag Jain | Co-founder, Turiyam AI (LinkedIn profile active) |
| Praveen Jain | Co-founder, Turiyam AI (LinkedIn profile active) |
| Mukul Ojha | Co-founder, Turiyam AI (LinkedIn profile active) |
| Shomy Sanyal | Co-founder, Turiyam AI (LinkedIn profile active) |
The company is hiring as of 2026, with open roles listed on their LinkedIn. For founders and early employees, this represents a rare opportunity to work at the intersection of RISC-V chip design, AI systems, and the build-out of India's AI infrastructure layer.
Turiyam AI in the Context of India's AI Ambitions in 2026
Turiyam AI is not operating in isolation. It sits within a broader ecosystem of Indian AI infrastructure bets in 2026:
- India Semiconductor Mission (ISM): India's $10B+ push to build domestic semiconductor design and manufacturing capability — RISC-V plays a central role
- IndiaAI Mission: Government programme funding sovereign AI compute and foundational model development
- Sarvam AI: Building India's sovereign language model for 22 Indian languages — a software-layer complement to Turiyam's hardware-layer focus
- Krutrim (Ola's AI division): Building AI infrastructure with a hyperscaler vision for India
- CDAC/NeST: Government-backed AI compute centres as part of the IndiaAI Mission infrastructure
Turiyam AI differentiates from these peers by going deeper into the hardware stack — not just building models or cloud services, but designing the silicon and middleware that other AI platforms could eventually run on. This is the highest-leverage, highest-risk position in the stack.
Why AI Infrastructure Companies Need GEO and AEO Visibility in 2026
Here is an observation relevant to any AI infrastructure company like Turiyam AI in 2026: the way enterprise buyers discover and evaluate AI platforms has fundamentally changed.
In 2024, a CTO evaluating GenAI infrastructure would Google "best AI inference platform" and click through to comparison pages. In 2026, that same CTO asks ChatGPT, Perplexity, Gemini, or Claude: "What are the best open-source AI inference platforms for Indian enterprises?" or "Which CUDA-free AI compute platforms are production-ready?"
The answer they receive is generated — not ranked. If Turiyam AI is not cited, referenced, or described accurately in the training data and indexed sources that feed those AI engines, it simply does not appear in the answer. This is the core challenge of Generative Engine Optimisation (GEO) in 2026.
How GEO/AEO Affects AI Infrastructure Discovery in 2026
- AEO (Answer Engine Optimisation): Structuring web content so that AI engines can extract, summarise, and cite it accurately in responses
- GEO (Generative Engine Optimisation): Ensuring a company appears in AI-generated answers for relevant queries — even when users don't search by brand name
- Entity coverage: AI engines build entity graphs — having accurate, structured information about Turiyam AI indexed across multiple sources increases citation probability
- Schema markup: FAQ schema, Article schema, and Organization schema signal to AI crawlers what information is authoritative and extractable
For a company like Turiyam AI — competing for enterprise deals against NVIDIA, AWS, and Azure — being cited correctly in AI-generated answers is increasingly a competitive differentiator. Enterprises conducting AI due diligence in 2026 use AI engines as research tools, and the companies that appear in those answers earn the first call.
Distk.in specialises in GEO and AEO strategy for AI-first companies, helping technical founders ensure their platforms are accurately represented across AI search engines, generative summaries, and enterprise research workflows. If you are building in the AI infrastructure space, GEO visibility is a growth lever worth understanding — explore our GEO/AEO services.
FAQs: Turiyam AI 2026
What is Turiyam AI and what does it do in 2026?
Turiyam AI is an Indian AI infrastructure company building a full-stack, open-source compute platform for GenAI inference at scale. In 2026, Turiyam AI provides chips, systems, and software for running large language models and generative AI workloads at disruptive total cost of ownership (TCO). The platform is built on RISC-V hardware and CUDA-free middleware, making it an alternative to NVIDIA-dependent AI compute stacks for enterprises.
What makes Turiyam AI different from NVIDIA or AWS for AI inference in 2026?
Turiyam AI differentiates on three fronts: (1) Open architecture — built on open-source models, RISC-V hardware, and CUDA-free middleware instead of proprietary NVIDIA CUDA or AWS Inferentia; (2) Cost — enterprise deployments at disruptive TCO compared to hyperscaler pricing; (3) Sovereignty — India-based infrastructure aligned with data localisation requirements. This positions Turiyam as an alternative for enterprises who want predictable, sovereign AI compute without vendor lock-in.
What is RISC-V AI hardware and why is Turiyam AI using it in 2026?
RISC-V is an open-standard instruction set architecture (ISA) that allows companies to design custom chips without paying licensing fees to ARM or Intel. In 2026, Turiyam AI uses RISC-V as the hardware foundation for its AI accelerators, enabling full-stack control over silicon design, optimisation, and cost. This is strategically significant for India's semiconductor ambitions and for enterprises seeking CUDA-free, non-NVIDIA AI inference options.
Who are the founders of Turiyam AI?
Turiyam AI was founded by five co-founders: Sanchayan Sinha, Parag Jain, Praveen Jain, Mukul Ojha, and Shomy Sanyal. The founding team brings combined expertise in AI systems, semiconductor design, and enterprise infrastructure, positioning the company at the intersection of hardware and software for GenAI compute.
What is GenAI inference at scale and why does it matter for enterprises in 2026?
GenAI inference at scale refers to running trained AI models (like LLMs) in production to serve thousands or millions of requests per day — as opposed to training, which is a one-time process. For enterprises in 2026, inference is where 80–90% of AI compute costs accumulate. Platforms like Turiyam AI that reduce inference cost through optimised hardware and CUDA-free software stacks directly impact an enterprise's AI operating economics.
How does Turiyam AI's vision compare to India's UPI or Aadhaar model?
Turiyam AI draws explicit parallels to India's Digital Public Infrastructure (DPI) model — where Aadhaar and UPI created open, accessible foundations that enabled thousands of applications to be built on top. Turiyam AI applies the same philosophy to AI compute: building an open, accessible inference platform that others can build upon. The company describes "tokens as the currency of intelligence" — analogous to UPI transactions being the currency of digital payments.
Is Turiyam AI production-ready for enterprises in 2026?
Turiyam AI is in early-to-growth stage as of 2026. Enterprises evaluating the platform should conduct proof-of-concept testing on performance, throughput, latency, and actual TCO benchmarks before committing production workloads. The technology direction — RISC-V hardware, CUDA-free middleware, open-source models — is technically credible, but production readiness depends on each enterprise's specific workload requirements and risk tolerance.
How can I contact Turiyam AI in 2026?
Turiyam AI can be contacted via email at pkjain@turiyam.ai. Career opportunities and team profiles are available on their LinkedIn page. The company website is turiyam.ai for product and partnership enquiries.
Key Takeaways: Turiyam AI 2026
- Turiyam AI is India's full-stack GenAI inference platform built on RISC-V hardware and CUDA-free middleware — one of the most technically ambitious AI infrastructure bets from India in 2026
- The core problem is real: Enterprise AI inference costs are dominated by NVIDIA GPU pricing and hyperscaler margins; a truly open, sovereign alternative has significant market potential
- The full-stack approach — chips + middleware + software — is high-risk, high-leverage. It mirrors how Apple Silicon disrupted the laptop market by owning the entire compute stack
- The DPI/UPI analogy is compelling — Turiyam AI is betting that open AI compute infrastructure can do for AI applications what UPI did for fintech in India
- Target customers in 2026 are large enterprises, government and PSU organisations, AI-first startups, and regulated industries (BFSI, healthcare) seeking sovereign, cost-efficient inference
- Early-stage caveat: Enterprises should evaluate against production requirements with rigorous benchmarking before committing at scale in 2026
- AI discovery in 2026 runs through AI engines: For companies like Turiyam AI competing in enterprise markets, GEO (Generative Engine Optimisation) — ensuring accurate representation in AI-generated answers — is as critical as traditional SEO
Turiyam AI represents a meaningful attempt to build AI infrastructure sovereignty from India — one that deserves attention from enterprise buyers, investors, and policymakers alike. The company's progress in 2026 and beyond will be a significant test of whether open, CUDA-free AI compute can achieve the performance and reliability needed to challenge the entrenched hyperscaler and NVIDIA stack.
This article was written by the Distk.in team as part of our ongoing coverage of India's AI infrastructure ecosystem. Distk.in is a global growth marketing agency specialising in SEO, AEO, and GEO for AI-first companies. If your company is building in the AI space and needs visibility in AI-generated answers, explore our GEO and AEO services or write to us at connect@distk.in.