Sovereign AI, delivered.
Bud AI OS on AMD — from silicon to safe AI outcomes. For enterprises, governments, cloud service providers and global system integrators. Infrastructure by AMD, intelligence by Bud: one operating system to train, deploy, evaluate, secure, consume and automate AI, rather than another tool to integrate.
The entire AI stack is fragmented.
Seven layers, hundreds of parts — and all of it only produces raw intelligence. To turn it into agentic outcomes, every part has to talk to every other part, and none of them were built to. Then the whole assembly is rebuilt four times: for research, pilot, production and scale.
Enterprises need one system, not forty tools. Combined with AMD infrastructure, Bud AI OS becomes a complete end-to-end stack rather than a parts list.
Slower delivery
Longer development cycles as teams glue tools together by hand.
Accuracy loss
Quality leaks at every handoff between disconnected systems.
Performance drag
Latency and degradation pile up across the stack.
No ROI
Spend climbs; measurable business return never arrives.
Mounting tech debt
Every integration is one more thing to maintain forever.
Can’t comply
Impossible to regulate or stay compliant across jurisdictions.
Agents can’t talk
Every system a stranger, with no shared memory or protocol.
Locked to frontier
No clean way to use cheaper SLMs where they would do the job.
Infrastructure by AMD. Intelligence by Bud.
Bud AI OS, combined with AMD’s CPU, GPU, networking and adaptive computing portfolio, delivers one fully integrated AI operating system — from silicon to safe AI outcomes. It unifies infrastructure, models, data, agents, governance and consumption, so enterprises, governments and cloud providers can run production AI on infrastructure they own and control.
Infrastructure by AMD
AMD drives AI innovation with a broad portfolio spanning the data center, edge and client. Only AMD powers the full spectrum of AI, bringing together leadership CPUs, GPUs and other accelerators, networking and open software to deliver flexibility and performance.
AMD Instinct™ GPUs
AMD EPYC™ server processors
AMD Adaptive SoCs
AMD Ryzen™ AI processors
AMD Radeon™ graphics
AMD Pensando™ DPU accelerators
Intelligence by Bud
Bud AI OS is one operating system to train, deploy, evaluate, secure, consume and automate AI. On AMD infrastructure it provides everything from GPU virtualisation and model training to multi-modal inference, guardrails, data, agents and the workspace where people use them — under one identity, one policy and one audit trail.
Six reasons the stack belongs on AMD.
Proven on AMD Instinct™
Bud’s team trained HEX-1, India’s first commercially usable open-source Indic LLM, on AMD Instinct MI300X GPUs.
No rewrite to move to AMD
Bud LayerZero abstracts the hardware layer, ROCm™ included, so models and agents run on AMD without re-engineering.
CPU-first where it counts
Guardrails decide in under 10 ms on standard CPUs, so governance and smaller models can ride on AMD EPYC™ nodes.
Every GPU fully utilised
Fractional and virtualised GPUs, serverless scale-to-zero and multi-tenancy keep AMD Instinct capacity busy, not idle.
Open at every layer
Open standards, MCP and A2A, standard model formats and AMD’s open software stack — nothing proprietary to escape from.
Sovereign by design
On-premises, sovereign cloud, edge or air-gapped on AMD infrastructure you own — the same platform everywhere.
One fully integrated OS, from silicon to AI outcomes.
One operating system, two tiers of hardware. Bud Novaria and Bud Gaia are each a complete, standalone Bud AI OS — every component, from the datacenter rack to the desk and the edge.
Bud Novaria
The shared estate. Novaria runs the full Bud AI OS stack — GPU Foundry through AI Outcomes — on racks and clusters built on AMD Instinct™ GPUs and AMD EPYC™ CPUs, on-premises or in public, sovereign or hybrid cloud. Training, inference at scale and governed agents for the whole enterprise, inside the boundary.
Bud Gaia
The personal estate. Gaia brings the same control plane to AI PCs, workstations and edge systems. Local agents and on-device inference where the data lives, with nothing leaving the device — and every policy, identity and audit trail shared with Novaria.
From silicon to safe AI outcomes.
Eight tiers, one control plane. Every tier is built on the tiers beneath it — and the foundation is AMD silicon.
One stack. Nine products.
Nine natively integrated products replace the 40+ tool stack — to train, deploy, evaluate, secure, consume and automate, on a single stack rather than nine integrations.
Bud Agent
AI that builds the AI. Give it an outcome: it picks the model, claims the compute, trains what’s missing, grounds it in your data and hands back a working agent.
Bud Studio
Where the work happens. 60+ pre-built agents, a plain-language builder and a marketplace — on desktop, chat, browser and IDE, plus APIs, SDKs, CLI and MCP.
Bud SENTRY
Zero-trust security and governance. 160+ policies, guardrails on CPUs in under 10 ms, human approvals, red-teaming and a hash-chained audit trail.
Bud Data Foundry
Enterprise data becomes context. Indexing, retrieval, ontology and data agents over 200+ federated connectors, with governed NL-to-SQL.
Bud MCP Foundry
Enterprise software becomes tools. Turns software, APIs and workflows into secure, federated MCPs with no coding — 1,000+ ready-made tools.
Bud AI Foundry
Models become intelligence. Multi-modal inference, self-healing scaling, voice, OCR and RAG middleware, evals, memory and hybrid routing by cost, quality and risk.
Bud Model Foundry
Where enterprise models get made. 120+ architectures, 260+ data-prep operators, distillation to SLMs, agentic RL training and 140+ benchmark gates.
Bud GPU Foundry
Infrastructure as a utility. Bare metal, VMs, Kubernetes and SLURM; fractional GPUs, scale-to-zero, tenant isolation, metering and invoices.
Bud LayerZero
Any silicon, any site. 600+ hardware platforms, AMD Instinct™ included — the same agent runs on a CPU worker, a GPU or a cloud, with no rewrite.
Eight reasons teams consolidate.
No more fragmentation
Every component for an end-to-end AI outcome, fully integrated — from the GPU to the outcome and its consumption.
40%+ lower cost, day one
Routing, safe caching and context compression cut spend before any retraining — and costs keep falling as workloads move to owned models.
Hybrid AI
One control plane for frontier and self-hosted models, agents, tools and data. Each request goes to the model that earns it.
6× faster go-to-market
A pre-integrated platform, reusable skills and tools, and plain-language agent building. No stitching.
Truly portable
Across model providers, clouds and infrastructure — on-prem, sovereign, hybrid, edge or any hyperscaler, without re-architecture.
Self-healing, self-improving
Enterprise Recursive Intelligence: a flywheel that compounds with every interaction, and AI that builds, scales and trains itself.
Compliant by configuration
47+ compliance packs mapped to the EU AI Act, GDPR, DPDP, FINRA, HIPAA and SOC 2, enforced at runtime, with one hash-chained audit trail.
No scarce AI talent needed
Guided install, automated sizing, zero-config operation and plain-language agent building — a one-person AI team.
What enterprises get on day one, and every day after.
Truly hybrid and portable AI
Model-as-a-service
Jurisdictions and frameworks, fully compliant
Pre-built agents
Enterprise AI search
Sovereign, portable and responsible AI
Faster from idea to agents
Saved on platform engineering
From platform deployment to an agent working live tickets (TCS ITSM agent, 2026). Create, manage, deploy and maintain agents autonomously, in hours or days, not months.
Plain-language intent in; agent, skills and governed tools out.
One control plane for versions, policies, budgets and access.
Any silicon, cloud or site, behind Bud Guard and approval gates.
Bud Eval gates every change; agentic RL training retrains on live traces.
Every interaction makes the whole system better.
The self-improving enterprise AI flywheel compounds on every turn, with governance and observability across the loop.
HEX-1: a sovereign Indic LLM, built on AMD.
Bud Ecosystem trained India’s first commercially usable open-source Indic LLM using 40% fewer GPUs on Azure, with instances powered by AMD Instinct™ MI300X GPUs.
HEX-1 is a 4-billion-parameter language model optimised for Indian languages — Hindi, Tamil, Kannada, Telugu and Malayalam. It bridges the linguistic AI gap in India by enabling developers to build systems that understand and respond in native Indian languages. Read about HEX-1.
Fewer GPUs to train
Parameters, Indic-optimised
Indian languages supported
“AMD Instinct™ MI300X GPUs provide us a sweet spot where we save cost, train much faster, and have the right platform to meet our requirements.”Linson Joseph, Chief Strategy Officer, Bud Ecosystem
TCS built an ITSM agent on Bud AI OS in two weeks.
One governed agent for CPU, memory and disk over-utilisation tickets, inside the TCS estate on one control node and one inference worker. TCS built the solution layer; Bud AI OS is the platform beneath it.
Three ticket types, one manual loop
About 1,150 tickets a month, each worked by an engineer in shift hours: find the top consumers, find the owner, notify. Diagnosis varied by who picked it up.
One governed agent on Bud AI OS
TCS built only what is specific to its tickets: three diagnose skills, owner attribution, a notification flow, MCP tool sets, a policy set and an evaluation set.
Every ticket, one governed loop
Worked 24×7 inside the estate, within policy, with any system change behind human approval and a tamper-evident record per ticket.
Platform deployment to the agent on real tickets
Tickets a month in scope: CPU, memory and disk
Steps in one governed loop, each recorded
Ticket or host data leaving the TCS estate
The loop — eight steps per ticket, each recorded
Ticket raised; webhook to Bud Gateway
Authenticated, rate-limited, routed to the agent
Policies and similar past tickets retrieved
Host evidence via tools; a skill ranks consumers
Owner from CMDB, ticket history or directory
Bud Guard validates; changes go to approval
Owner notified; ticket updated, routed or closed
Evidence, decision and action on the audit trail
The next ticket type is a new skill, not a new project. The next customer is a policy pack; the next site is a configuration change. Source: Bud × TCS customer case study, 2026. Customer-validated.
Frontier-level accuracy at small-model cost.
A retail AI feature delivered by a global system integrator moved from a frontier API to a distilled model served on Bud AI OS.
Monthly cost
Response time
Accuracy
76–91% lower TCO than a frontier API, all SLOs met.
A GSI’s TCO report compared four enterprise workloads on a frontier API with the same workloads deployed on the Bud stack.
Customer-validated: retail AI feature delivered by a GSI; GSI TCO report, enterprise workloads.
Ten numbers, and where each one comes from.
Results from Bud AI OS deployments across sovereign cloud, government, healthcare, financial services and enterprise. Vcustomer-validated BBud engineering benchmark
GPU utilisation on a sovereign cloud, up from 35–50%
AI service lines on the same estate, live within 30–45 days
Lower monthly AI cost, accuracy unchanged (85.46% → 85.44%)
Lower TCO than a frontier API across four workloads, all SLOs met
From platform install to an agent working live IT tickets
Less physician documentation time, at 96% accuracy
Lower annual TCO (~$2.4M → ~$768K) with 60% less manual processing
Guardrail decision on a standard CPU — governance on every call
Safe cache hit rate at a 1.5% wrong-answer rate, 0.2 ms per hit
Silicon SKUs behind one runtime
Eight buyers. One AI operating system.
The same operating system, bought for eight different reasons — all of them running on AMD.
| Buyer | What they are up against | What Bud AI OS gives them |
|---|---|---|
| Global system integrators | Every AI engagement rebuilds the platform; the platform spend goes elsewhere. | One platform. Six AI revenue lines. Every outcome the GSI’s. |
| Sovereign AI | National GPUs have landed; the AI services haven’t. | Sovereign by design: open at every layer, locked in at none. |
| Regulated industries & governments | Capable AI exists; approved AI doesn’t. | One system, from GPUs to AI consumption. |
| Enterprise AI | Pilots prove the model; production needs a platform. | One foundational AI platform. No more fragmentation. |
| Cloud service providers | GPU-hours are a commodity; the margin sits above the chip. | Monetise every GPU, from bare metal to agent outcomes. |
| Banks | AI on bank data must satisfy the examiner. | Bud BankingCore: the AI operating system the bank owns. |
| Product companies | AI features erode margin and stall enterprise deals. | AI inside the product, on the vendor’s terms. |
| Platform companies | AI in every app, at small-business prices. | The AI engine beneath every app in the suite. |
Open at every layer, locked in at none.
National GPUs have landed; the AI services haven’t. Bud AI OS on AMD gives a nation everything it needs to own its AI on its own ground: open standards, any model, any silicon, one platform — and no specialist team required.
Built on open standards
Industry-standard APIs, MCP and A2A for agents, standard model formats.
No silicon lock-in
One runtime across 600+ silicon SKUs; change vendors without a rewrite.
No infrastructure lock-in
On-prem, sovereign cloud, edge or fully air-gapped, without re-architecture.
Open, inspectable supply chain
Software and models scanned, signed and licence-checked before they run.
Any model
Open-weight, national or closed models behind one gateway.
Grounding & bias reduction
Answers grounded in the nation’s own data, with citations and automated bias checks.
The entire stack, one platform
Silicon to consumption under one identity, policy and audit fabric.
No specialist expertise required
Guided install, automated sizing and plain-language agent building.
Deploys where the nation decides
On-prem
National and state data centres.
Sovereign cloud
In-country, state-owned or empanelled.
Air-gapped
Classified and disconnected networks.
Edge & field
Bud Gaia on edge hardware.
GPU utilisation on a sovereign cloud, up from 35–50%
In production at the Income Tax Department, Government of India
Open-source edition: inspect it, run it on national hardware
What private infrastructure gives you that public cloud cannot
As enterprises and governments move from experimentation to production, the limits of public and shared infrastructure start to bind.
Data sovereignty by design
Sensitive data stays on-premises or private, meeting residency, compliance and governance requirements.
Full control across the lifecycle
Govern models, training, deployment, scaling and access on AMD infrastructure you operate.
Predictable cost and performance
Private AI gives predictable economics at scale, with efficient training and inference on AMD Instinct™ and AMD EPYC™ underneath.
The same platform, configured differently.
One foundational AI platform
Forty pilots, three in production, and every team rebuilt the same plumbing. Bud AI OS puts data, models, gateway, training, tools and consumption on one platform — on AMD infrastructure you own.
- Frontier and self-hosted models under one router and one policy
- At least 40% lower AI cost from day one; 6× faster to agents
- The same control plane in any cloud account, on-prem or air-gapped
- Built, bought and partner agents run as peers via A2A and MCP
- Bud Managed Agents-as-a-Service: Bud builds, runs and retrains the agents on your data, priced per outcome — not per token, seat or GPU
AI-native neo-clouds
GPU-hours are a commodity; the margin sits above the chip. Bud AI OS manages and monetises AMD Instinct™ capacity from bare metal to agent outcomes.
- Metering, quotas, budgets, billing and chargeback by tenant, project, model and key
- Tenants who build agents and tuned models stop shopping on price per hour
- The full lifecycle in-jurisdiction, air-gap capable, under the provider’s own brand
Truly sovereign AI
Capable AI exists; approved AI doesn’t. One vendor, one security review, compliant and auditable — and improving on its own.
- One system, from GPUs to the staff workspace
- An internal ChatGPT alternative: model-as-a-service, coding and desktop agents and AI search, inside the perimeter
- 47+ compliance packs mapped to DPDP, MANAV, FINRA, RBI FREE-AI and more
- On-prem and air-gapped, with one hash-chained audit trail
- Models refreshed every two quarters; production traces retrain private models
The CSP monetisation ladder
Every rung up lowers churn, lifts lifetime value and widens gross margin. Margins and churn are modelled on a 500-accelerator CSP B — rung widths are illustrative.
CSP utilisation, service-line and time-to-production figures: V one Indian sovereign cloud provider, 2025.
Built for enterprise choice.
Bud AI OS enables immediate adoption with long-term flexibility and scale.
Modalities
Models
AMD hardware
Deployment
Integrations
Compliance
Up for a live demo?
See how Bud AI OS runs on AMD silicon end to end — secure, compliant and scalable AI, from training and inference to governed agents and outcomes, inside private and regulated environments. Model-agnostic, hardware-agnostic: on-premises, cloud, hybrid or air-gapped.