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Bud Ecosystem · Architecture

Federated Hybrid LLM Inferencing

A multi-tier inference framework enabling 1M-parameter edge models to achieve GPT-4 class accuracy through token-level reward routing and actively-learning N-Gram caching across NVIDIA hardware.

Read the Research Paper →

What is Federated Hybrid LLM Inferencing?

A revolutionary architecture that distributes AI intelligence across a hierarchy of devices — from powerful cloud servers to tiny IoT sensors — enabling GPT-4 level accuracy even on edge devices with just 1M parameters.

☁
Multi-Tier Architecture: Seamlessly routes inference across Cloud, Client, Edge, and IoT tiers based on complexity and requirements.
🚀
Token-Level Reward Routing: Smart reward models score each token, only escalating to cloud when local generation falls short.
📊
Active Learning N-Gram Cache: Every cloud correction is cached with O(1) lookup, continuously improving local accuracy.
💻
NVIDIA Hardware Optimized: Purpose-built for B200, H200, H100, DGX Spark, RTX, and Jetson platforms.
51-65%
Cloud cost reduction at launch
~3%
Tokens requiring cloud LLM
90%+
Local generation as cache matures
O(1)
Cache lookup via suffix automaton

Multi-Tier Architecture

Intelligence flows seamlessly across the hierarchy, with each tier optimized for specific workloads and hardware capabilities.

N-Gram Cache
Cloud
70B+ params
B200 · H200 · H100
☁

Cloud LLM / MM-LLM

NVIDIA B200 / H200 / H100

Source of reasoning & token verification. Invoked only when the reward model rejects an SLM token. Every correction feeds back into the N-Gram cache hierarchy.

100%
Source
↑ Rejected tokens ↓ Corrections
Client
Subject Matter Experts
600M – 4B
DGX Spark · RTX 5090
🏦

Enterprise Hub

Domain SLMs for banking, legal, healthcare on DGX Spark

💻

Workstation

Developer & analyst SLMs on RTX 5090/5080

📱

Personal AI

4B MoE (4×600M) on laptops & smartphones

🕶

AR Glasses

Real-time contextual reasoning at the edge

90%+
Hit Rate
Edge
Context Experts
1M – 500M
RTX A-Series · Jetson AGX
🚗

Automotive

In-cabin AI: navigation, driver assist, offline-capable

🏠

Smart Hub

Home orchestration: energy, security, scheduling

🏭

Industrial

Factory-floor intelligence & predictive maintenance

75%
Hit Rate
IoT
Task Experts
1M – 50M
Jetson Orin Nano
🌡

Sensor Agent

Single-task: interpret & alert from sensor data streams

🤖

Robot Controller

Appliance intelligence with cached cloud reasoning

⌚

Wearable

Health monitoring with cached diagnostic patterns

60%
Hit Rate

How It Works

1
Edge SLM Generation
Edge SLM generates candidate tokens autoregressively on local NVIDIA hardware (DGX Spark, RTX, or Jetson).
2
Reward Model Scoring
Reward model scores each token against the cloud LLM's distribution. Accepted tokens stay local; rejected tokens trigger a selective cloud call.
3
Suffix Automaton Cache
Stores every cloud correction at O(1) lookup. Recurring patterns bypass both reward evaluation and cloud calls entirely.
4
Cache Propagation
N-Gram cache propagates across the hierarchy — Cloud → Client → Edge → IoT. Intelligence learned once is available everywhere.

Key Benefits

Transform your AI infrastructure with dramatically reduced costs, improved latency, and enterprise-grade reliability.

💰

Massive Cost Reduction

Achieve 51-65% cloud cost reduction at launch, with costs continuing to decrease as the cache matures and learns from usage patterns.

⚡

Ultra-Low Latency

90%+ of tokens generated locally as the cache matures, eliminating network round-trips and delivering near-instantaneous responses.

🔒

Data Sovereignty by Design

Private data never leaves the device during inference. Sensitive tokens are processed on-device; GDPR, HIPAA, and financial-regulation compliant out of the box.

📍

Offline Capability

Edge devices continue functioning even without connectivity, using cached intelligence for reliable offline operation.

📈

Continuous Learning

Every cloud correction improves the entire hierarchy. The system gets smarter with usage, automatically adapting to your specific needs.

🌐

Hardware Flexibility

Optimized for the full NVIDIA stack from B200 cloud servers to Jetson Orin Nano edge devices, with seamless scaling.

⚙

Tunable Quality-Cost Dial

Adjust the reward threshold to balance accuracy vs. cloud cost per use case — without retraining or redeployment. Configure at runtime.

💼

Decreasing Marginal Cost

Cloud reasoning is cached after first invocation. The system gets cheaper every day as the cache saturates, approaching zero marginal cost over time.

Use Cases Across Every Tier

From enterprise data centers to wearable devices, Federated Hybrid LLM Inferencing adapts to your deployment environment while maintaining consistent quality.

Client
Edge
IoT

🏦 Enterprise Hub

Deploy domain-specific SLMs for banking, legal, and healthcare on DGX Spark with full compliance and data sovereignty.

600M-4B params DGX Spark 90%+ cache hit

💻 Developer Workstation

Code completion, documentation, and analysis running locally on RTX 5090/5080 with cloud-level accuracy.

RTX 5090 Code-optimized

📱 Personal AI Assistant

4B MoE models on laptops and smartphones providing always-available, privacy-first AI assistance.

4B MoE (4×600M) Mobile-ready

💻 On-Device Coding Assistant

Run a 3B code model locally on RTX 5090. Cloud LLM handles complex architecture decisions; project-specific patterns cached in days.

3B Code Model RTX 5090 Pattern caching

🌐 Real-Time Translation

2B multilingual SLM on phones with domain-specific phrases cached from cloud. Achieves sub-5% cloud activation within a week of deployment.

2B Multilingual Mobile <5% cloud calls

🚗 Automotive AI

In-cabin AI for navigation, driver assistance, and infotainment with full offline capability for safety-critical scenarios.

1M-500M params Jetson AGX Offline-ready

🏠 Smart Home Hub

Central home orchestration managing energy, security, and scheduling with local processing for privacy.

RTX A-Series 75% cache hit

🏭 Industrial Intelligence

Factory-floor AI for predictive maintenance, quality control, and process optimization with real-time inference.

Industrial-grade Real-time

🌡 Sensor Agents

Single-task AI interpreting sensor data streams and generating alerts with minimal power consumption.

1M-50M params Jetson Orin Nano

🤖 Robot Controllers

Embedded appliance intelligence with cached cloud reasoning for consistent behavior across scenarios.

Cached reasoning 60% hit rate

⌚ Wearable Devices

Health monitoring with cached diagnostic patterns for real-time analysis while preserving battery life.

Ultra-low power Health-optimized

See It In Action

Watch how tokens flow through the system in real-time. Most tokens are generated locally (purple) or served from cache (teal), with only occasional cloud corrections (red) when the local model needs guidance.

Simulated Token Stream
SLM Accepted Cache Served Cloud Correction
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