AI | Whileone

Performance-Driven AI and Performance Engineering

Elevating Performance Through Specialized Co-Design

At Whileone, we operate at the critical intersection of Data, AI/ML, and Infrastructure. Our mission is to break performance barriers inherent in general-purpose hardware by delivering specialized, optimized solutions for complex, computation-bound tasks. We provide the expertise to ensure your data pipelines are robust, your AI models are performant, and your systems are operating with maximum efficiency.

Key Services

AI Model
Optimization

Edge
AI

AI driven Data
Analytics

Generative
AI

Our Foundational Experience

We have established a track record of successful execution across diverse,

high-performance environments:

100k+ Man Hours on project execution
300+ Applications Covered (including mission-critical AI/ML workloads)
25+ Major Projects in Cloud, HPC, and AI ecosystems

Workflow

AI Model Optimization

Platform specific acceleration.
Quantization and precision optimization of models
Memory and throughput improvements (flash attention)
Benchmarking and reproducibility (Pytorch and LLAMA)

Understanding DLRM with PyTorch

DLRM stands for Deep Learning Recommendation Model. It is a neural network architecture developed by Facebook AI (Meta) for large-scale personalized recommendation systems. DLRM is widely used in real-world applications where personalized recommendations or ranking predictions are needed. DLRM designed for click-through rate (CTR) prediction and ranking task. Examples: Online Advertising, E-commerce Recommendations, Social Media Feed Ranking, Streaming Services, Online Mark

To get maximum tokens generated for target CPU

LLMs are Getting Better and Smaller Let’s look at Llama as an example. The rapid evolution of these models highlights a key trend in AI: prioritizing efficiency and performance. When Llama 2 70B launched in August 2023, it was considered a top-tier foundational model. However, its massive size demanded powerful hardware like the NVIDIA H100 accelerator. Less than nine months later, Meta introduced Llama 3 8B, shrinking the model by almost 9x. This enabled it to run on smaller

Benchmarking Meta Llama 4 Scout on CPU-Only Systems: Performance, Quantization, and Architecture Tuning

Meta’s Llama 4 Scout, released in April 2025, is a 17-billion parameter general-purpose language model that brings powerful reasoning to a broader range of applications—including those running without GPUs. This blog focuses on benchmarking Llama 4 Scout on CPU-only systems, covering: Tokens per second Latency per token Prompt handling efficiency Quantization techniques Architecture-specific optimization for x86, ARM, and RISC-V (RV64) Converting to GGUF format for efficient

Edge AI

Designed an integrated pipeline connecting sensor data ingestion, real-time preprocessing, and shared-memory analytics for temperature, pressure, and anomaly-based inputs.

Implemented data sampling and synchronization logic between edge sensors and cloud ingestion nodes for deterministic performance.

Enabled lightweight inference on-device using optimized TensorFlow Lite models for anomaly detection and offset correction.

Top CPU Performance Benchmarking Toolkits You Should Know

Modern compute platforms - from cloud hyperscale CPUs to edge processors - deliver unprecedented parallelism and instruction-set capabilities. But to truly understand performance, you need the right benchmarking tools. Whether you're comparing cloud instances, evaluating Arm-based servers like Ampere , or validating x86, RISC-V, or AI-accelerated hardware, the ecosystem offers several battle-tested frameworks. In this blog, we explore the most widely-used CPU benchmarking too

Predicting Differential Loss at the Edge: Lightweight ML for Real-Time Test Intelligence

Inspiration In high-throughput production environments, every sensor reading tells a story. Test systems continuously record Pressure , Temperature , and Differential Loss (DL) across thousands of cycles, but much of this data remains passive, observed but not interpreted. We set out to change that by deploying machine learning directly at the edge on a BeagleBone Black board. The goal was not anomaly detection, but live inference : to compute what the ideal DL should be (

YOLOX on RISC-V QEMU

Goal of this project: This project aims to determine RISC-V's readiness for running YOLOX for the latest edge requirements. Target Application: Running YOLOX on RISC-V QEMU involves setting up a RISC-V virtual machine and then configuring the necessary environment to compile and run YOLOX. Please note that this is a complex process, and it's essential to have prior experience with virtualization and RISC-V development. From the RISCV website, this is a blog ( https://riscv.or

AI Driven Data Analytics

Analytical Reporting Intelligence : AI-powered report generation and summarization for deeper visibility into system performance, trends, and operational efficiency.
Data-Driven Optimization : Intelligent recommendations derived from analytics models to fine-tune configurations and achieve better benchmarking outcomes.

Predictive Performance Projection : Data-analytics framework for forecasting system and architecture performance pre-launch, empowering proactive sales and technical planning.

AI Chat Bot

Flow Chart

AI Chat Bot

1/2

Understanding DLRM with PyTorch

DLRM stands for Deep Learning Recommendation Model. It is a neural network architecture developed by Facebook AI (Meta) for large-scale personalized recommendation systems. DLRM is widely used in real-world applications where personalized recommendations or ranking predictions are needed. DLRM designed for click-through rate (CTR) prediction and ranking task. Examples: Online Advertising, E-commerce Recommendations, Social Media Feed Ranking, Streaming Services, Online Mark

Top CPU Performance Benchmarking Toolkits You Should Know

Modern compute platforms - from cloud hyperscale CPUs to edge processors - deliver unprecedented parallelism and instruction-set capabilities. But to truly understand performance, you need the right benchmarking tools. Whether you're comparing cloud instances, evaluating Arm-based servers like Ampere , or validating x86, RISC-V, or AI-accelerated hardware, the ecosystem offers several battle-tested frameworks. In this blog, we explore the most widely-used CPU benchmarking too

Generative AI

Autonomous Decisions & Actions : Agentic AI not only analyzes information but also takes context-aware actions without manual intervention.
Multi-Step Reasoning & Tool Orchestration : Agents break tasks into steps, plan workflows, and autonomously call the right tools, APIs, or systems to complete the task.

Continuous Learning & Adaptive Behavior : Agents retain context, learn from interactions, and improve their decision-making and efficiency over time.

Success Story: How We Built a Trusted SRE Partnership with Our Client

In the world of Site Reliability Engineering (SRE), trust, knowledge, and execution matter more than anything else. When our team was presented with the opportunity to support one of the leading clients in the inference systems domain, we knew the competition would be fierce. Many well-established and much larger organizations were bidding for the same project. Yet, we saw this as an opportunity to prove that expertise, dedication, and the right approach can outweigh size and

Site Reliability Engineering (SRE) Support for System Infrastructure

Operational Excellence for Service-Driven Enterprises As businesses increasingly deploy services and in production environments, the reliability and uptime of servers have become a critical need. These workloads are often hosted in hybrid setups, including dedicated data centers and public clouds, where even brief outages can impact performance, user trust, and business outcomes. To meet these demands, a dedicated Site Reliability Engineering (SRE) team provides comprehensive

Performance-Driven AI and Performance Engineering

Elevating Performance Through Specialized Co-Design

Key Services

AI Model Optimization

Edge AI

AI driven Data Analytics

Generative AI

Our Foundational Experience

We have established a track record of successful execution across diverse,

high-performance environments:

​

100k+ Man Hours on project execution

300+ Applications Covered (including mission-critical AI/ML workloads)

25+ Major Projects in Cloud, HPC, and AI ecosystems

Workflow

AI Model Optimization

Understanding DLRM with PyTorch

To get maximum tokens generated for target CPU

Benchmarking Meta Llama 4 Scout on CPU-Only Systems: Performance, Quantization, and Architecture Tuning

Edge AI

Top CPU Performance Benchmarking Toolkits You Should Know

Predicting Differential Loss at the Edge: Lightweight ML for Real-Time Test Intelligence

YOLOX on RISC-V QEMU

AI Driven Data Analytics

Understanding DLRM with PyTorch

Top CPU Performance Benchmarking Toolkits You Should Know

Generative AI

Success Story: How We Built a Trusted SRE Partnership with Our Client

Site Reliability Engineering (SRE) Support for System Infrastructure

AI Model
Optimization

Edge
AI

AI driven Data
Analytics

Generative
AI