One Chip Can’t Do It All - The New AI Tech Stack

Key Takeaways

• As AI moves into production, infrastructure leaders are prioritizing total cost of ownership, power efficiency, and utilization.

• No single processor can efficiently handle training, inference, orchestration, retrieval, analytics, and data preparation at scale.

• GPUs are highly effective for parallel AI workloads like training and inference, but using them for ETL, SQL analytics, or orchestration often increases cost, power use, and idle time.

• The right chip should match the right workload. CPUs for orchestration and control logic, GPUs for model training and inference, TPUs/cloud ASICs for hyperscale AI, and APUs for analytics-heavy data pipelines.

• APUs (Analytics Processing Units) optimize operations such as joins, aggregations, filters, Spark transformations, and SQL-style processing directly in silicon.

  
  

This article is based on a webinar with Dani Voitsechov, VP R&D and Co-founder, Dan Eaton, CSO, and Bam Gobets, VP EMEA, from Speedata. Watch the full webinar below.

https://www.youtube.com/watch?v=tVuuIXpCDgI

The Compute Stack Is Becoming Disaggregated

With power consumption sharply rising, the costs of AI infrastructure are compounding, and energy is becoming the core constraint shaping infrastructure decisions. Yet, enterprises have a tendency to ignore chip diversity and run everything on GPUs. This is breaking economics at scale.

AI workloads do not all behave the same way. Training, inference, data preparation, analytics, retrieval, orchestration, and simulation each have fundamentally different computational patterns. No single processor can execute every workload efficiently.

The GPU-first model made sense when AI was still experimental and speed-to-innovation mattered more than cost discipline. But in production environments, efficiency becomes the priority. Running the wrong workload on the wrong chip means overpaying in power, memory, and infrastructure costs.

We have entered the era of the Specialized Compute Stack. This requires shifting workloads away from general-purpose architectures and onto engines optimized for the task at hand,  therefore reducing energy consumption, lowering memory overhead, and improving total cost efficiency.

Jensen Huang of NVIDIA, has also highlighted this broader industry shift: accelerated computing is not about one chip doing everything, but about matching the right compute engine to the right workload.

The AIOps Pipeline Architecture

Power efficiency optimization starts at the architectural level. As AIOps workflows grow in complexity, "one-size-fits-all" hardware creates massive economic leaks. Each stage requires an engine optimized for its specific computational "shape," ensuring that performance gains don't come at the cost of unsustainable energy consumption.

1. Ingestion & Collection

The front end of the process involves pulling fragmented data from databases, applications, and external streams. CPUs remain the standard here, handling the essential "logic" of storage coordination and general-purpose traffic control. Because these tasks are often characterized by unpredictable, low-concurrency logic rather than heavy mathematical throughput, the versatility of the CPU makes it the most cost-effective choice for managing initial data movement.

2. Data Preparation

This stage involves cleaning records, joining datasets, and feature engineering to transform raw information into "AI-ready" formats. While CPUs have traditionally handled this, they often struggle with the sheer scale of modern datasets, leading to processing bottlenecks. This is where the APU (Analytics Processing Unit) emerges as an important accelerator. By offloading data-intensive transformations from the CPU to the APU, enterprises can process massive workloads at a fraction of the time and energy cost required by general-purpose silicon.

3. Storage & Retrieval

Efficiency is often determined not by a processor’s raw speed, but by the bandwidth of the pipes leading to it. High-performance GPUs require a massive, constant stream of data to remain efficient. Yet if the retrieval process is  too slow, these expensive chips sit idle in a "starving" state. The APU acts as the intelligent bridge in this stage, accelerating SQL-like queries and in-memory filtering at the storage layer to ensure the GPU is fed a continuous stream of optimized data, maximizing utilization and ROI.

4. Model Training

Once model training begins, highly parallel processors become valuable. GPUs remain extremely well suited for large-scale training, while purpose-built cloud training ASICs, such as Google TPU and Amazon Trainium are also designed to perform efficiently in this phase.

5. Production Inference

In production inference, the landscape broadens further. GPUs, TPUs, Trainium, LPUs, and other emerging accelerators are all competing to deliver lower latency and better economics for serving models at scale. Meanwhile, CPUs are increasingly capable for smaller-model inference, especially as advances in pruning, quantization, and compact architectures make efficient deployment possible. Simultaneously, APUs play a vital role in analytics workflows.

The Agentic Era Is Reshaping the Compute Stack

AI is also transitioning from single-model applications to autonomous agents. Therefore, the role of compute infrastructure is expanding again. Unlike simple chatbots, AI agents rely heavily on orchestration, decision logic, tool calling, API coordination, memory management, scheduling, and workflow execution. These are all areas where CPUs excel. Whether based on Arm architecture or x86 architectures, CPUs remain the general-purpose backbone of modern systems and are seeing renewed demand as enterprises prepare for large-scale agent deployment.

However, a secondary shift is occurring at the data platform layer. Agents do not simply generate responses; they interrogate systems, retrieve context, run analytics, compare options, and trigger actions. That means agents can generate large volumes of database queries and analytical requests, creating new pressure on traditional CPU-based data platforms.

This is driving the rise of what many now call agentic analytics workloads: machine-generated query traffic that can exceed what legacy architectures were designed to handle. As a result, specialized acceleration for analytics, retrieval, and structured data processing is becoming increasingly relevant.

Now, each processor category is finding its lane:

• CPUs remain the versatile workhorse for orchestration, general compute, transactions, and mixed workloads.

• GPUs continue to dominate large-scale AI training and play a major role in inference thanks to their parallel architecture optimized for matrix operations. Companies like NVIDIA and AMD continue to push performance boundaries.

• TPUs and cloud accelerators such as Google TPU and Amazon Trainium are highly optimized for training and inference workloads in cloud environments.

• LPUs are emerging as specialized options for certain inference architectures, particularly low-latency decoding workloads.

• APUs and other domain-specific accelerators are being designed for SQL analytics, data preparation, and structured query processing.

Watch the full webinar above for a deeper dive into best-fit use cases, where to avoid each approach, and practical recommendations.

Deep Dive Into APUs

An APU is a processor purpose-built for analytics rather than model training or graphics. It is designed specifically to execute operations such as SQL queries, joins, aggregations, filters, and table processing directly in silicon.

Unlike CPUs, which process instructions sequentially, or GPUs, which excel at vectorized math, APUs are optimized for table-oriented data movement and analytics pipelines. They are built to efficiently read, decode, decompress, join, aggregate, and transform records while minimizing expensive memory movement.

This makes APUs particularly relevant for:

• Batch ETL pipelines

• AI data preparation

• SQL analytics at scale

• Agentic analytics workloads

• Large Spark-based transformation jobs

  
  

The value proposition is straightforward: dramatically faster analytics performance, lower infrastructure cost, and better energy efficiency for workloads that do not belong on GPUs.

The APU impact:

• 10x-100x faster than CPU/GPU on analytics workloads

• 90% TCO reduction at enterprise scale

• Zero code changes to existing Spark

  
  

Key Use Cases for APUs

As enterprises optimize AI infrastructure, one of the clearest opportunities lies in accelerating the data workloads. This is where APUs can deliver value:

1. Batch ETL and Enterprise Data Pipelines

Every organization generates operational data across finance, sales, product, logistics, and customer systems. That data is typically spread across multiple databases and business applications, then moved into platforms such as Apache Spark for transformation and downstream analytics.

The heavy lifting happens in batch ETL: cleaning records, changing formats, joining tables, aggregating metrics, and preparing datasets for BI systems. These workloads are compute-intensive and often mission-critical for data-driven enterprises.

APUs are highly suited to this layer, accelerating the transformation stage dramatically and reducing the time required to move raw business data into usable analytics outputs.

2. AI Data Preparation for Model Training

Many AI teams still spend more time preparing data than training models. Before a model can be fine-tuned or deployed, data must be normalized, deduplicated, cleaned, labeled, tokenized, and structured correctly.

Today, much of that work still runs on CPUs or shared infrastructure that was not purpose-built for large-scale data preparation. APUs offer a more efficient path by accelerating these structured data operations before the cleaned datasets are handed off to GPUs or TPUs for training.

The result is faster end-to-end model readiness, lower infrastructure cost, and less wasted high-value GPU time on non-training workloads.

3. Agentic Analytics

A rapidly emerging category is agentic analytics, when AI agents answer business questions by generating SQL or querying structured enterprise systems.

For example, a user may ask an AI assistant:

• Which customers are most likely to churn this quarter?

• Why did margins decline in Europe last month?

• Which suppliers are causing delivery delays?

  
  

To answer these questions, the model often needs complex joins, aggregations, filtering, and multi-table analysis across enterprise datasets. Traditional platforms can struggle with the speed and concurrency demands of agent-generated workloads.

APUs are designed for exactly this type of heavy relational processing. By accelerating Spark-based analytics queries, they can reduce response times from minutes to seconds, helping make real-time analytics through AI assistants practical at scale.

The Power Efficiency Impact of APUs

Real-world deployments are beginning to show what happens when workloads move to purpose-built analytics silicon.

In one ad-tech machine learning data preparation environment, a workload that previously required 37 CPU servers was reduced to just 3 APU-powered servers. End-to-end runtime dropped from 12 minutes to 2 minutes, dramatically improving time to insight while also compressing infrastructure footprint and lowering total cost of ownership.

See APUs in Action

Many enterprises are now evaluating their existing Apache Spark workloads to understand where acceleration can reduce runtime and cost. The opportunity is often larger than expected, especially in legacy ETL pipelines and agent-driven analytics use cases.

Try the Speedata Workload Analyzer for yourself and discover how APUs can accelerate your existing workloads.

Download the Workload Analyzer CLI to your own environment, upload your Spark execution logs, and automatically discover your expected Al acceleration and cost savings benefits.