Author:
Kartik Manocha, Product Lead and Cloud Solution Architect, Intel
Why CPU choice determines your AI inference ROI, especially for agentic AI workflows
The AI landscape has fundamentally shifted beyond single-query responses. Agentic AI systems, in which AI agents perform multi-step reasoning, tool use, and complex decision chains, now represent the fastest-growing enterprise AI deployment pattern. In just the past two years, agentic AI has already reached 35% adoption, with another 44% of organizations planning to deploy it soon.(1) Unlike simple chatbots, these systems execute dozens of inference calls per user interaction, making CPU performance exponentially more critical.
Here's the key insight: Agentic workflows don't require specialized compute for the models themselves. They need powerful CPUs to orchestrate complex multi-step processes. While LLMs and other AI models handle reasoning tasks, the CPU manages the agentic orchestration layer, coordinating multiple agents, integrating tools, managing state across workflows, and executing parallel decision trees. Google Cloud C4's powered by Intel Xeon 6 processors, with more vCPUs and superior memory bandwidth, excel at this orchestration workload, enabling agentic systems to coordinate dozens of simultaneous tasks efficiently.
Agentic AI: The CPU Performance Multiplier Effect
While traditional AI performance matters…Agentic AI performance compounds:
Traditional AI: User asks → Model responds → Done
Agentic AI: User asks → Agent reasons → Calls tools → Processes results → Refines approach → Delivers solution
- Multi-step reasoning: Each reasoning step benefits from faster inference.
- Tool integration: Rapid model calls enable seamless external system integration.
- Context management: Efficient processing maintains conversation state across complex interactions.
Each step requires inference operations. A single agentic interaction might trigger 10-50 individual inference calls. As a result, performance gaps compound exponentially.
Examples:
Multi-agent/Crew-Based Systems:(2) Typical Range: 15-50 LLM inference calls per interaction. Multi-agent systems multiply agent calls because:
- Each agent performs independent reasoning
- Agents exchange messages (each message = inference)
- Coordinator/Planner agent orchestrates execution
- Arbitration or validation layers add more calls
Enterprise Orchestrator Agents (High Autonomy):(3) Typical Range: 25-100+ LLM inference calls per interaction. These workflows include:
- Multi-agent contextual retrieval
- Policy and permission validation
- HITL (human-in-th-loop) approval steps
- Audit trail generation
- Error recovery and retries
- Long-running, stateful execution
These systems explicitly persist state across steps, requiring repeated inference.
The performance advantages we see in individual AI workloads, LLMs, vision processing, and recommendation systems translate directly to agentic orchestration capabilities. Faster individual components mean more responsive multi-agent coordination and higher throughput across complex workflows. When an agentic system needs to rapidly query multiple models, process vision data, or run recommendation algorithms simultaneously, CPU performance at each task determines overall system responsiveness
The Intel Xeon 6 Processor: Built for AI Inference
Only Intel Xeon processors (4th gen and newer) feature Intel® Advanced Matrix Extensions (Intel® AMX), an architectural game changer for Agentic AI. Intel AMX is a built-in AI accelerator that turns the CPU into a high-throughput, low-latency inference engine. Intel AMX brings built-in AI acceleration to every CPU core, enabling scalable, cost-efficient inference for the many types of iterative, tool-driven model calls that agentic AI relies on.
The Performance Difference: Intel Xeon 6 Processors vs. AMD Turin
Large Language Models: The Foundation of Agentic Reasoning
In LLM inference workloads, including Llama 3.1 (8B), the backbone of most agentic systems. Intel Xeon 6 processors with Intel® Advanced Matrix Extensions (Intel® AMX) demonstrate clear advantages:
- Time to First Token (TTFT): 2.05x faster than AMD Turin
- Time per Output Token (TPOT): 1.44x faster than AMD Turin
Chart 1: C4-std-32-lssd vs. C4D-std-32-lssd
For agentic workflows, this means reasoning steps complete twice as fast, and multi-step agent interactions remain responsive throughout complex task chains.
Recommendation Systems: Powering Agent Decision-Making
DLRM workloads mirror the decision-making patterns agentic systems use for tool selection and context prioritization:
- Up to 3x higher throughput vs AMD Turin
Chart 2: C4-std-32-lssd vs. C4D-std-32-lssd
Vision Processing: Multi-Modal Agent Capabilities
Vision Transformer (ViT) Performance:
- Latency Mode: 1.54x (BF16) and 2.17x (INT8) performance advantage
- Throughput Mode: 1.96x (BF16) and 2.38x (INT8) performance advantage
- Performance per Dollar: Up to 2.00x better cost efficiency
Chart 3: C4-std-32-lssd vs. C4D-std-32-lssd
Stable Diffusion Results:
- 1.67x (BF16) and 1.77x (INT8) raw performance gains
- 1.40x and 1.49x better performance per dollar
Chart 4: C4-std-32-lssd vs. C4D-std-32-lssd
What Our Customers and Alliances are Saying
As enterprises move AI from experimentation into production, infrastructure choices increasingly determine whether those systems can scale with control and predictable economics. Through our benchmarking work, we saw that Intel® Xeon® 6 processor–based Google Cloud C4 instances delivered a meaningful step forward for enterprise AI workloads—supporting higher concurrency, more consistent response times, and materially better throughput compared to prior‑generation C3 platforms. For analytics‑led and agentic AI use cases, these results point toward faster time to value and a more consistent infrastructure foundation as organizations scale AI adoption.
-Julie Shen, principal, Deloitte Consulting LLP and Intel Lead Alliance Partner
For critical enterprise functions like inference, retrieval, and anomaly detection, Intel Xeon 6 with Intel AMX on Google Kubernetes Engine provides strong performance and excellent cost efficiency for always-on workloads. The lower latency and better economics enabled by Intel’s AMX built into Xeon processors are valuable additions to production AI workloads.
-Renato Nascimento, Head of Technology for Articul8 AI
Anyscale gives AI builders a single compute platform to run and scale workloads from a laptop to production-scale clusters. Now with native support for Google Cloud C4 instances powered by Intel Xeon 6 processors, customers can immediately tap into built-in AMX acceleration to boost throughput and improve cost-performance across data preparation and batch inference workloads without any application code changes.
-Elizabeth Hu, Product Lead, Anyscale
The Verdict: Intel Performance Leadership for the Agentic Future
Google Cloud C4 instances powered by Intel Xeon 6 processors deliver measurable advantages that become exponentially more valuable in agentic deployments:
- 2.0x faster reasoning initiation across agent workflows
- 47% higher decision-making throughput
- Up to 2.38x vision processing for multi-modal agents
- Consistent cost efficiency leadership
In the agentic AI economy, these aren't just benchmark victories; they're the performance foundation that enables sophisticated AI agents to deliver real business value at enterprise scale.
Ready to power your agentic AI systems? Learn more and experience the Intel Xeon 6 CPU advantage in your production agent workflows.
Come See Us at Google Cloud Next 2026
Intel is excited to participate in Google Cloud Next 2026 at the Mandalay Bay Convention Center in Las Vegas on April 22-24, 2026.
Make sure you don't miss our Lightning Talk, where I will be discussing “How Intel is Raising the Performance Bar: AI Compute and Security on Google Cloud” #EXPOLT014. I will be presenting alongside Olivia Melendez, Product Manager at Google Cloud. We will also be giving away premium, limited-edition Intel swag you’ll definitely want, as well as raffling off fun prizes for attendees.
Please join us on Wednesday, April 22, 2026, 2:00 pm - 2:20 pm PDT. Location: Theater 2 (behind Intel Booth)
Platform Configurations
Hardware and OS Configuration
VM Configuration:
Instance Type Family: Config 1: c4-standard-32-lssd; Config 2: c4d-standard-32-lssd
# of vCPUs (Threads) Config 1: 32; Config 2: 32
Cloud Provider: Config 1: GCP; Config 2: GCP
Microarchitecture: Config 1: GNR; Config 2: Turin
Numa Nodes: Config 1: 1; Config 2: N/A
CPU Model: Config 1: Intel Xeon 6985P-C; Config 2: AMD EPYC 9B45
Base Frequency: Config 1: 2; Config 2: 2
All-core turbo frequency (GHz): Config 1: 4.1; Config 2: 4.10
Single-Core max turbo frequency (GHz): Config 1: 4.1; Config 2: 4.1
Microcode: Config 1: 0xffffffff; Config 2: 0xffffffff
Memory:
Memory Capacity (GB/vCPU): Config 1: 112GB (7x16GB RAM []); 8GB (1x8GB RAM []); Config 2: 112GB (7x16GB RAM []); 12GB (1x12GB RAM [])
Storage:
Direct-Attached SSD Size (GB): Config 1: 1x 10G nvme_card-pd, 1x 375G nvme_card2, 1x 375G nvme_card0, 1x 375G nvme_card1, 1x 375G nvme_card3, 1x 375G nvme_card4, 1x 1.5T nvme_card-pd; Config 2: 1x 10G nvme_card-pd, 1x 1.5T nvme_card-pd, 1x 375G nvme_card1, 1x 375G nvme_card0
Direct-Attached SSD Type: Config 1: nvme; Config 2: nvme
OS:
Version: Config 1: Ubuntu 24.04.3 LTS; Config 2: Ubuntu 24.04.4 LTS
Kernel: Config 1: 6.17.0-1008-gcp; Config 2: 6.17.0-1008-gcp
Price: Config 1: $1.992318904 / 1 hour; Config 2: $1.676310751 / 1 hour
Software Configuration
Workload: Config 1 and Config 2: dl_boost benchmark suite for ViT, Stable Diffusion, DLRMv2
Application: Config 1 and Config 2: dl_boost benchmarking suite
Middleware, Framework, Runtimes: Config 1: pytorch 2.10.0a0+gitb66e7cf inductor backend; Config 2: pytorch 2.10.0a0+gitb66e7cf +zendnn zentorch backend
Containers and Virtualization: Config 1 and Config 2: amr-registry.caas.intel.com/psec/dlboost/pytorch:2026_ww03
Pricing
Prices on demand per 1 month as of 4/13/2026:
C4-standard-32-lssd: $1434.47
c4d-standard-32-lssd: $1207.94
Footnotes:
(2) https://myengineeringpath.dev/tools/agentic-frameworks/
(3) https://docs.cloud.google.com/architecture/agenticai-orchestrate-access-disparate-systems ; https://www.intel.com/content/dam/www/central-libraries/us/en/documents/2025-10/intel-it-agentic-ai-in-enterprise-paper.pdf
Notices and Disclaimers
Performance varies by use, configuration, and other factors. Learn more on the Performance Index site.
Performance results are based on testing as of dates shown in configurations and may not reflect all publicly available updates. See backup for configuration details. No product or component can be absolutely secure.
Your costs and results may vary.
Intel technologies may require enabled hardware, software, or service activation.
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