AMD Instinct MI350X GPU: Specs, CDNA 4 & AI Performance

The AMD Instinct MI350X Accelerator is a data center graphics processing unit (GPU) built on the 4th-generation AMD CDNA 4 architecture, designed specifically to scale massive generative AI models and High-Performance Computing (HPC) workloads.

Equipped with a massive 288 GB of HBM3e memory, an 8 TB/s memory bandwidth, and native support for low-precision data types like MXFP4 and MXFP6, the MI350X delivers up to 9.2 PFLOPS of FP4 matrix compute.

Operating within a 1,000 W Thermal Design Power (TDP) air-cooled OAM form factor, it offers up to a 35x generational leap in inference performance over its predecessors, positioning it as a direct competitor to top-tier enterprise AI hardware available through leading hardware suppliers like Servchip.

At the heart of the AMD Instinct MI350X lies the refined CDNA 4 architecture. Unlike consumer graphics cards optimized for real-time rendering, CDNA 4 strips away standard display pipelines and focuses entirely on parallel compute density, matrix operations, and high-speed data interconnects.

Multi-Chiplet Design and 3nm Manufacturing

• Compute Dies (XCDs): 8 Accelerator Complex Dies manufactured on TSMC's advanced N3P (3 nm) process.
• I/O Dies (IODs): 2 I/O dies built on a 6 nm node, streamlined from the previous 4-die setup to optimize routing efficiency and power utilization.
• Transistor Count: A staggering 185 billion transistors packaged across a multi-chiplet substrate.

AMD continues its leadership in chiplet engineering by combining advanced TSMC manufacturing nodes to maximize compute density per watt. To explore similar enterprise chip architectures and accelerators, browse through our AMD Instinct lineup.

288 GB HBM3e Memory for Unmatched Context Windows

Memory capacity remains a massive bottleneck when serving Large Language Models (LLMs) with hundreds of billions of parameters. The MI350X solves this by mounting 288 GB of 12-high HBM3e memory.

This high memory density allows enterprise teams to fit 100B+ parameter models on fewer GPUs without needing extensive tensor parallelism, drastically cutting down interconnect latency during inference.

Native Microscaling Formats (MXFP4 and MXFP6)

With CDNA 4, AMD introduced redesigned matrix engine pipelines with hardware support for MXFP4, MXFP6, and FP8 precision formats. Lower precision representation allows data center operators to run larger batch sizes with smaller memory footprints while retaining near-FP16 model accuracy.

Open ROCm 7.0 Software Stack

Hardware power is meaningless without a flexible software ecosystem. The MI350X leverages AMD ROCm 7.0, an open-source software platform providing zero-day out-of-the-box support for leading frameworks like PyTorch, TensorFlow, JAX, and ONNX Runtime. This open ecosystem ensures that developer teams can easily migrate existing AI pipelines without vendor lock-in.

Enterprise teams seeking tailored deployments can evaluate custom Servchip solutions for seamlessly integrating high-density AI clusters.

The MI350X scales across a wide range of data center footprints, from single-node workstations to full rack-scale clusters. The reference architecture uses AMD Universal Baseboards (UBB) that hold up to 8x MI350X OAM accelerators each, totaling 2.3 TB of HBM3e per board.

Standalone workstation nodes give individual teams a single OAM module over PCIe 5.0 x16, ideal for local fine-tuning and domain-specific dataset generation.

For larger clusters, UBB 2.0 modules mount 8x MI350X accelerators on a single board, aggregating 2.3 TB of total HBM3e VRAM for heavy model training. Check out our catalog of enterprise hardware to compare servers and accelerators.

At full scale, 4th-generation Infinity Fabric interconnects allow network engineers to link up to 64 MI350X GPUs in air-cooled rack deployments — and up to 128 GPUs in liquid-cooled configurations — to run distributed exascale workloads.

Deploying cutting-edge hardware infrastructure like the AMD Instinct MI350X across international data centers often involves sending engineering teams abroad for site setup, maintenance, and compliance audits. Servchip coordinates global logistics, sourcing, and delivery so that enterprise teams can deploy MI350X clusters on schedule, wherever they are built.

Selecting the right hardware architecture for your enterprise AI initiatives requires deep expertise in procurement, server compatibility, and thermal management. Read more about Servchip to discover how we assist organizations worldwide in sourcing and deploying state-of-the-art compute hardware.

If you are planning an infrastructure upgrade or need technical guidance on hardware procurement, feel free to reach out directly via our Servchip contact page.

The AMD Instinct MI350X is the strongest direct alternative to NVIDIA in the data center AI segment. With 288 GB of HBM3e memory, 8 TB/s bandwidth, native MXFP4 support and an open ROCm 7.0 software stack, it gives enterprise teams NVIDIA-class performance without vendor lock-in — often at a lower total cost of ownership.

Compared against the NVIDIA B200 and B300, the MI350X wins on raw memory capacity and open-ecosystem flexibility, making it a strong pick for frontier inference and large-batch training. Request an MI350X quote or compare accelerators with our team to size the right configuration for your workload.