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Intel® GPU D-series

Scale inference, not infrastructure

Intel GPU.

Agentic AI is changing data center workloads

Agents read far more than they write: long contexts, multistep workflows, compute dominated by prefill. As demands grow, challenges expand beyond bandwidth. Intel D-series GPUs are built for this new era of agentic AI, efficiently handling larger contexts, more concurrent sessions, and more tokens within practical power and cost limits.

Intel data center GPU D270 product image.

Memory up to

480

GB

LPDDR5X per card

Power

350

W

Air-cooled

Compute

32

Xᵉ

Xᵉ3P cores

Compute

256

XMX

Xᵉ3P matrix engines

Scaling agentic AI

Agentic fleets

Over thousands of steps, 100K token contexts, and dozens of concurrent sessions, keep agents resident to help sustain throughput across the agentic loop.

Reasoning over more context

Whole-context reasoning

Keep more of the model and long context resident, for richer reasoning with fewer GPUs and no high bandwidth memory (HBM).

Economics of inference

Efficient token serving

Dense, air-cooled, standard PCIe infrastructure helps maximize tokens per rack and watt, scaling serving capacity and keeping costs in check.

More inference within the power and racks you already run

As agentic AI drives longer prompts and more concurrent sessions, customers need scalable infrastructure without trade-offs in power, cost, or deployment complexity. Intel GPU D270 offers up to 480 GB LPDDR5X memory for power-efficient, memory-bound inference in data center environments.

Intel data center GPU processor package with an exposed chip die.
Intel GPU.

Built for AI

Built for agentic workloads

Die area reallocated from graphics and ray‑tracing pipelines to Xᵉ3P cores and matrix compute, improving inference efficiency.

Built for compute

Compute where it matters

More prompt processing per watt: 32 Xᵉ3P cores and 256 XMX engines accelerate prefill, curbing long‑context and agentic costs.

Built for the workload

Precision for every workload

From native low‑precision inference to FP64 scientific computing, the workload determines the precision—not the hardware.

Built for efficiency

Standard PCIe 5.0

Standard form factor, standard interconnect, using existing servers. No proprietary networking or bespoke systems required.

Built for reliable power

350W air-cooled

Passive cooling in the racks you already run, with no liquid‑cooling retrofit needed between evaluation and deployment.

Built for your data center

Open by design

Zero-day support for leading open models, served on the frameworks you already run: vLLM and the open PyTorch ecosystem.

Workload-first compute for modern AI

Deliver an intelligent evolution in data center infrastructure. Built for the new era of AI inference.



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Frequently Asked Questions

Intel GPU D270 is an inference-optimized data center GPU designed for the agentic AI era. It is built to support enterprise AI, SaaS, GPUaaS, and government or sovereign-cloud deployments that need memory-rich, power-efficient AI acceleration.

Intel GPU D270 is designed for production AI inference workloads such as agentic AI, long-context retrieval-augmented generation (RAG), enterprise AI assistants, coding assistants, document understanding, multimodal AI, diffusion models, and high-concurrency serving.

Agentic AI often turns one task into multiple rounds of model calls, tool use, retrieval, reasoning, and response generation. Intel GPU D270 is designed for the inference requirements that become important in those environments, including prefill processing, larger context, memory capacity, concurrency, and power efficiency.

Inference-optimized means the GPU is designed for running AI models in production rather than training them at massive scale. Intel GPU D270 focuses on the needs of deployed AI applications, including prompt processing, high-concurrency serving, memory capacity, efficient power use, and system-level performance.

Long-context AI workloads can require more memory to support larger prompts, retrieval-heavy workflows, and KV cache growth during inference. Intel GPU D270 is designed with high available memory capacity to help support larger models, longer context, and more concurrent sessions.

Intel GPU D270 messaging should reference familiar AI software and serving frameworks such as PyTorch, vLLM, SGLang, and OpenAI Triton, where approved. This helps developers and platform teams understand how the product fits into existing AI workflows.

Intel GPU D270 is designed around a standard PCIe 5.0 form factor and air-cooled deployment profile, helping enterprises evaluate AI inference acceleration within existing data center environments without requiring a specialized liquid-cooling retrofit.

The primary audience includes AI system architects, AI platform architects, infrastructure and cloud engineering leaders, enterprise AI decision-makers, SaaS providers, GPUaaS providers, government or sovereign-cloud teams, and developers deploying production inference workloads.

Customers should connect with Intel to learn more about Intel GPU D270, evaluate how it may fit their AI inference roadmap, and receive updates as additional product information becomes available.

Disclaimers

Performance projections vs RTX Pro 6000 are based on publicly available information and Intel internal analysis as of Feb 2026. Performance varies by use, configuration, and other factors. Memory capacity comparison at comparable bandwidth.