MI455X vs. Rubin: Who plans AI training, inference or HPC infrastructure with HBM4 accelerators must weigh different GPU and rack-scale architectures. Both are designed for liquid-cooled rack systems. AMD positions MI455X for AI inference, training and fine-tuning; NVIDIA positions Rubin for training, post-training and inference and for scientific computing. At first glance the published specifications appear similar – comparable memory bandwidth, identical scale-up bandwidth and HBM4 on both – but several technical details and a fundamental comparability problem decide which platform fits a given workload and data center.
This article compares the two accelerators on documented manufacturer specifications, explains why the peak AI performance figures are not directly comparable, names the clearest differences in memory capacity and floating-point precision, and sets out what decides the platform choice before a large-scale deployment.
AMD Instinct MI455X and NVIDIA Rubin Overview
AMD Instinct MI455X and NVIDIA Rubin are compute accelerators of the HBM4 generation. AMD presented Instinct MI455X as part of the MI400 series announcement in July 2026; NVIDIA presented the Rubin platform in January 2026. Both are designed for rack systems with direct liquid cooling. AMD Helios integrates AMD Instinct GPUs, AMD EPYC CPUs and AMD Pensando networking in one rack; NVIDIA Vera Rubin NVL72 holds 72 Rubin GPUs and 36 Vera CPUs.
Because both are specified with direct liquid cooling, the data center must have liquid cooling at rack level before such systems can be installed. The comparison draws exclusively on manufacturer data published by AMD and NVIDIA; no independent benchmark results are cited.
MI455X vs. Rubin: Technical Specifications Compared
The table below lists the published specifications from AMD’s product page for MI455X and NVIDIA’s Vera Rubin platform blog and HGX platform page. All values are manufacturer claims.
| Specification | AMD Instinct MI455X | NVIDIA Rubin GPU |
|---|---|---|
| Architecture | CDNA 5; TSMC 2 nm and 3 nm; 320 billion transistors; 256 Work Group Processors | 2 compute dies; 336 billion transistors; 224 Streaming Multiprocessors |
| 4-bit AI | 40.3 PFLOPS OCP MXFP4 | 50 PFLOPS NVFP4 inference (sparse); 35 PFLOPS NVFP4 training (dense) |
| 8-bit / 6-bit | 20.1 PFLOPS OCP MXFP8, MXFP6 and FP8 | 17.5 PFLOPS FP8/FP6 training (dense) |
| FP16 / BF16 matrix | 5 PFLOPS (10.1 with structured sparsity) | 4 PFLOPS FP16/BF16 (dense) |
| FP32 | 315 TFLOPS vector and matrix | 130 TFLOPS vector; 400 TFLOPS matrix (Tensor Core emulation) |
| FP64 | 5 TFLOPS vector and matrix | 33 TFLOPS vector; 200 TFLOPS matrix (Tensor Core emulation) |
| Memory | 432 GB HBM4, 12 stacks, 23.3 TB/s peak; 192 MB L2 | up to 288 GB HBM4, up to 22 TB/s |
| Scale-up | 3.6 TB/s bidirectional (UALoE) | 3.6 TB/s bidirectional per GPU (NVLink 6) |
| Cooling | Enhanced Accelerator Module; direct liquid | warm-water direct liquid, 45 °C supply |
| Rack system | AMD Helios (AMD GPUs, AMD EPYC, Pensando) | Vera Rubin NVL72 (72 Rubin GPUs, 36 Vera CPUs) |
| Host CPU | AMD EPYC | Vera: 88 Olympus Arm cores, up to 1.5 TB LPDDR5X |
| Software | ROCm; HIP, OpenMP, OpenCL; PyTorch, JAX, TensorFlow, Triton, SGLang | CUDA; CUDA-X; PyTorch, JAX; TensorRT-LLM, vLLM, SGLang, Dynamo |
Note on comparability: actual performance depends on workload, batch size, model architecture, software stack and system configuration.
Why the AI Performance Figures Are Not Directly Comparable
The most frequently compared row, 4-bit AI performance, contains a fundamental problem: the two vendors publish figures for different number formats. AMD states 40.3 PFLOPS for OCP MXFP4; NVIDIA states 50 PFLOPS NVFP4 for inference and 35 PFLOPS NVFP4 for training. OCP MXFP4 and NVFP4 are two different 4-bit formats, so their PFLOPS values cannot be compared directly without knowing how a given model behaves in each format.
A second comparability issue arises from the compute mode. NVIDIA states 50 PFLOPS for NVFP4 inference with sparsity, while the 35 PFLOPS for NVFP4 training represent a dense value.
AMD’s own comparison chart shows Rubin at 35 PFLOPS and states in footnote MI400-003 that NVFP4 dense values were used for Rubin. AMD therefore compares its OCP MXFP4 figure against Rubin’s dense NVFP4 value, not against the Transformer Engine value.
The practical consequence: a headline comparison of “40.3 vs. 50” compares values that were determined in different formats and on different bases. Vendor peak values do not settle the comparison; the reliable approach is to benchmark the actual model in the actual precision on both platforms.
HBM4 Memory and Bandwidth Compared
HBM capacity per GPU is the clearest documented difference. According to AMD, MI455X carries 432 GB HBM4 across 12 stacks; NVIDIA specifies up to 288 GB HBM4 for Rubin. That 144 GB gap matters for models whose working set does not fit in 288 GB or for inference deployments that want to keep multiple model variants resident per GPU.
Memory bandwidth is close but not identical. AMD states 23.3 TB/s peak bandwidth for MI455X; NVIDIA states up to 22 TB/s for Rubin. Both are peak values, and the difference is small; whether bandwidth limits a workload depends on the workload itself.
Scale-up bandwidth is identical on paper: both platforms specify 3.6 TB/s bidirectional per GPU, AMD via UALoE and NVIDIA via NVLink 6. The per-GPU figure says nothing about the network beyond the rack, which differs between Helios and Vera Rubin NVL72 and has to be planned separately for each platform.
FP64 and FP32: Differences for HPC and Simulation
For workloads beyond AI, such as scientific simulation, computational fluid dynamics, molecular dynamics or finite element analysis, double-precision floating-point throughput is decisive. Here the documented difference is large. NVIDIA states 33 TFLOPS FP64 vector and 200 TFLOPS FP64 matrix via Tensor Core emulation for Rubin. AMD states 5 TFLOPS FP64 vector and matrix for MI455X. That is a ratio of roughly 6:1 in vector mode and 40:1 in matrix mode, with Rubin’s matrix value based on emulation.
That difference matches the respective positioning: NVIDIA addresses converging AI and scientific computing with Rubin, while AMD positions MI455X for AI inference, training and fine-tuning. On paper, Rubin offers significantly more throughput particularly for FP64 matrix operations. What advantages result for a concrete HPC workload depends on its compute operations and software support. A training workload that runs in FP8, FP6 or OCP MXFP4 does not depend on FP64.
FP32 shows a different picture. AMD states 315 TFLOPS for both vector and matrix; NVIDIA states 130 TFLOPS vector and 400 TFLOPS matrix via Tensor Core emulation. Vector FP32 is higher on MI455X; the higher matrix value on Rubin only helps workloads whose FP32 operations run as matrix operations.
Software, Host Platform and Cooling
Both accelerators are designed for high-performance GPU servers and rack-scale systems, but differ in their platform architecture and deployed components. MI455X in a Helios rack works with AMD EPYC CPUs on a Linux x86-64 host; the software stack is ROCm with support for HIP, OpenMP and OpenCL, and the framework list includes PyTorch, JAX, TensorFlow, Triton and SGLang. Rubin in Vera Rubin NVL72 connects to Vera CPUs via NVLink-C2C at 1.8 TB/s (88 Olympus Arm cores per CPU, up to 1.5 TB LPDDR5X). The stack is CUDA with backward compatibility to earlier generations and CUDA-X libraries, with support for PyTorch, JAX, TensorRT-LLM, vLLM, SGLang and Dynamo.
The software choice decides ecosystem compatibility. A codebase already written in CUDA can in principle continue on Rubin, provided the deployed CUDA versions, libraries and applications support the GPU architecture. A codebase targeting ROCm, or one written in a framework that supports both backends, can run on MI455X. Library support, profiling and debugging tools and the team’s experience with one of the two stacks also belong in the decision.
Both platforms use direct liquid cooling. NVIDIA specifies warm water with a 45 °C supply temperature for Vera Rubin NVL72; AMD specifies direct liquid cooling for MI455X but does not publish a supply temperature in the source used here.
Typical Mistakes When Comparing the Accelerators
The most common mistake is treating vendor comparison charts as neutral benchmarks. AMD’s comparison chart on the MI455X product page lists Rubin’s NVFP4 dense values alongside MI455X OCP MXFP4 values. That chart is AMD’s selection, not an independent test. NVIDIA does not publish a direct MI455X comparison in the Rubin platform blog. Reading a vendor chart as a verdict ignores the format and compute-mode differences explained above.
A second mistake is comparing the 50 PFLOPS NVFP4 inference value with the 40.3 PFLOPS OCP MXFP4 value and concluding that Rubin is faster. That comparison mixes a Transformer Engine value with a dense value and two different 4-bit formats.
Ignoring FP64 is a mistake for any workload that includes traditional HPC. A pure AI deployment may not care; a facility running both training and simulation workloads must account for the 6:1 or 40:1 FP64 gap. Treating the platforms as interchangeable because their HBM4 and scale-up bandwidth figures are similar overlooks the precision and software differences that determine which workload class runs efficiently.
Finally, treating the accelerator choice as a component decision overlooks that both accelerators are specified for liquid-cooled rack systems. Cooling and the network beyond the rack have to be planned together with the accelerator.
MI455X or Rubin: Which Platform Fits Which Workload?
The workload profile decides the platform, not the peak figure. If a workload requires more than 288 GB local GPU memory, MI455X’s HBM4 capacity of 432 GB can be a decisive advantage, particularly if model parallelism or memory offloading is to be avoided. If a model is to run in NVFP4 with Transformer Engine support, NVIDIA’s inference value is the relevant reference; for other formats, only a benchmark of the actual model gives a reliable answer.
Where FP64 throughput matters, for example in computational fluid dynamics, molecular dynamics, seismic processing or weather models, Rubin’s documented performance of 33 TFLOPS vector and 200 TFLOPS matrix is the decisive figure; MI455X is specified with 5 TFLOPS FP64. A mixed environment that combines low-precision training and FP64 simulation has to weigh both requirements or split the deployment.
The software stack is a binding constraint. An existing CUDA codebase can in principle continue on Rubin, provided the deployed CUDA versions and libraries support the GPU architecture. A codebase targeting ROCm, or one that already runs on AMD Instinct accelerators, fits MI455X. Porting between stacks is possible but is a project of its own.
The host platform also matters. Helios uses AMD EPYC x86-64 CPUs, which fit existing x86 Linux toolchains. Vera uses Arm CPUs; NVIDIA states that major Linux distributions, AI frameworks and orchestration platforms run unmodified on Vera. For environments built around x86 operations, staying on x86 may reduce integration effort.
Finally, the facility must be ready for direct liquid cooling and the network design of the chosen platform. A data center operator evaluating both platforms should obtain detailed rack, cooling and power specifications from AMD and NVIDIA before making a platform commitment.
For businesses, system integrators and research facilities planning a large-scale AI or HPC deployment around HBM4 accelerators, the MI455X vs. Rubin decision depends on memory per GPU, precision requirements, software stack, host platform and facility readiness. Peak AI values from vendor comparison charts are not directly comparable, because formats and compute modes differ. Where memory capacity or FP64 throughput is decisive, the table above shows the direction. Where both platforms appear suitable on paper, benchmark the actual workload in the actual precision on both stacks. We advise you on selecting the right server GPUs and AI servers and support you with questions on compatibility, system architecture and integration. Together with our server specialists we find the right solution for your AI and HPC workloads – contact us.
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