ultraData Center

Max 1550 GPU

The Intel Data Center GPU Max 1550 is designed for HPC and AI workloads with high memory bandwidth and FP64 performance.

VRAM 128GB
TDP 600W
Contact providers for pricing
Max 1550 GPU

Cloud Pricing

No pricing data available for this GPU at the moment.

Prices updated daily. Last check: 4/8/2026

Performance

FP16
839 TFLOPS
Bandwidth
3276 GB/s

Strengths & Limitations

  • 128 GB HBM2e memory provides substantial capacity for large models and datasets
  • 3,276 GB/s memory bandwidth enables high-throughput data processing
  • 1,024 Intel XMX engines deliver dedicated AI acceleration hardware
  • oneAPI support offers unified programming across Intel hardware ecosystem
  • 128 ray tracing units provide hardware-accelerated rendering capabilities
  • 839 TFLOPS FP16 performance supports mixed-precision AI training
  • PCIe Gen 5 x16 interface provides modern connectivity standards
  • 600W TDP requires substantial cooling and power infrastructure
  • Limited software ecosystem compared to CUDA-native applications
  • Released in 2022, predating current-generation GPU architectures
  • Intel XMX engines may require code optimization for peak performance
  • Ray tracing capabilities are secondary to primary HPC/AI focus

Key Features

Intel Xe Matrix Extensions (XMX) engines
oneAPI programming model support
Hardware ray tracing units
Xe Vector Engines
HBM2e memory subsystem
PCIe Gen 5 connectivity
Intel Xe HPC architecture
Hardware-based AI acceleration

About Max 1550

The Intel Max 1550 is a datacenter GPU built on Intel's Xe HPC architecture, representing Intel's entry into the high-performance computing and AI accelerator market. As part of Intel's Data Center GPU Max series launched in 2022, the Max 1550 positions itself as an alternative to established players in the server GPU space, though it predates Intel's newer Arc and subsequent generations. The Max 1550 features 128 GB of HBM2e memory with 3,276 GB/s of memory bandwidth, paired with 128 Xe-cores and 1,024 Intel XMX engines for AI workloads. The GPU includes 128 ray tracing units and operates with a base clock of 900 MHz and maximum dynamic clock of 1,600 MHz. With 839 TFLOPS of FP16 performance and a 600W TDP, it targets compute-intensive applications requiring substantial memory capacity. In cloud deployments, the Max 1550 serves workloads that benefit from its large memory pool and oneAPI software ecosystem, particularly in environments where Intel's unified programming model aligns with existing development workflows.

Common Use Cases

The Max 1550 targets high-performance computing and AI workloads that require large memory capacity and benefit from Intel's oneAPI ecosystem. Its 128 GB HBM2e makes it suitable for training large language models, scientific simulations with extensive datasets, and memory-intensive inference tasks. The substantial memory bandwidth supports data-parallel workloads, while the Intel XMX engines accelerate mixed-precision AI training and inference. Organizations already invested in Intel's software tools and seeking alternatives to NVIDIA's ecosystem may find the Max 1550 appropriate for compute clusters and AI development platforms.

Full Specifications

Hardware

Manufacturer
Intel
Architecture
Xe HPC
TDP
600W

Memory & Performance

VRAM
128GB
Memory Bandwidth
3276 GB/s
FP16
839 TFLOPS
FP64
52 TFLOPS
Release
2022

Frequently Asked Questions

How much does a Max 1550 cost per hour in the cloud?

Max 1550 pricing varies by provider, region, and commitment level. Check the pricing table above for current rates across all providers.

What is the Max 1550 best used for?

The Max 1550 excels at AI training and inference workloads requiring large memory capacity, scientific computing with substantial datasets, and applications that can leverage Intel's oneAPI programming model. Its 128 GB HBM2e memory and Intel XMX engines make it particularly suitable for large language model training and memory-intensive HPC simulations.

How does the Max 1550 compare to NVIDIA's datacenter GPUs?

The Max 1550 offers competitive memory capacity at 128 GB HBM2e and provides an alternative to CUDA through Intel's oneAPI ecosystem. However, it lacks the mature software stack and widespread application support of NVIDIA's platform. Performance characteristics differ due to architectural differences between Intel's XMX engines and NVIDIA's tensor cores, requiring workload-specific benchmarking for accurate comparisons.