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Beirit Harvey

This blog looks at the AMD Instinct MI455X GPUs and how it shapes up against NVIDIA's Rubin, Google TPUs, and AWS Trainium.

AMD vs. NVIDIA vs. Google TPUs: Comparing AI Chips

Picking an AI chip really comes down to a few practical factors and considerations… How much of your model actually fits in memory, how fast the chip can chew through it, whether your software runs well on it, and what it'll cost you to train or serve the model.

The AMD Instinct™ MI455X brings together a strong mix of memory capacity and compute performance. It's built for AMD's Helios platform and goes head-to-head most directly with NVIDIA's Rubin GPUs. Google TPUs and AWS Trainium are also worth a look, though they come with different architectures and software environments.

Here's how they stack up on the factors that matter for training, fine-tuning, and inference.

MI455X stands out for its memory capacity and bandwidth, giving AMD a strong position for workloads that need room for larger models, longer contexts, or more simultaneous requests. The software environment adds another dimension: existing code, supported libraries, and optimization work can all influence which chip delivers the best results. 

NVIDIA provides the closest GPU comparison, while Google TPUs and AWS Trainium introduce different tradeoffs in how models are developed and deployed.


AMD MI455X vs. NVIDIA Rubin

AMD and NVIDIA are really the most direct GPU comparison here. Both handle AI training and inference just fine, but their memory specs, compute capabilities, and software stacks differ in ways that'll shape your decision. 

Where AMD stands out is its commitment to open-source ecosystems. Its software stack is built with open standards, which gives developers and teams more flexibility and a real escape route from vendor lock-in if you ever want to switch things up.


Memory: AMD Provides More Capacity Per GPU

MI455X has 432 GB of HBM4, compared with Rubin’s published 288 GB. That gives AMD 50% more memory per GPU. Its peak memory bandwidth is also approximately 6% higher than the 22 TB/s figure NVIDIA publishes for Rubin.

Capacity and bandwidth solve different problems. Capacity determines how much model data and runtime state can stay on the GPU. Bandwidth affects how quickly that data can move to the compute engines.

For inference, extra capacity can leave more room for the KV cache used by transformer models, larger batches, or longer contexts. For training and fine-tuning, it can provide additional space for model state and activations. Depending on the workload, it may also reduce the number of GPUs needed to fit a model.

Those benefits depend on the deployment. A workload that already fits comfortably in memory may gain more from efficient kernels or faster computation than from additional capacity.


The Real-Life Example

AMD's performance modeling for the Helios rack (72X MI455X) uses the Kimi K2 Thinking workload with a 32K input and 8K output sequence profile. This is a trillion-parameter-class MoE reasoning model with a large context window, exactly the kind of memory-hungry inference workload where KV cache capacity bites.

In that modeled comparison, AMD claims Helios delivers 30% more tokens per dollar than NVIDIA's Vera Rubin NVL72, alongside 50% more HBM capacity per rack (31 TB vs. 20.7 TB).


Compute: Compare the Same Precision

AMD publishes up to 5 PFLOPS of dense BF16/FP16 matrix performance for MI455X. NVIDIA lists 4 PFLOPS of dense FP16/BF16 performance for Rubin.

On those peak specifications, AMD offers 25% more compute at the same precision. Actual throughput depends on how effectively the software uses that hardware.

Low-precision figures require additional care. FP4, FP8, and BF16 numbers describe different kinds of computation, and vendors also distinguish dense performance from performance using structured sparsity. A headline FLOPS comparison is useful only when those conditions match.

For a practical benchmark, use the same model, precision, input and output lengths, and quality requirements. For inference, measure throughput alongside latency. For training, compare time to reach the same quality target.


Software: Account for the Code You Already Use

A team with CUDA-specific libraries, custom kernels, and tuned NVIDIA deployments has an existing investment to consider. Moving that workload involves more than comparing the GPUs’ specifications.

But don’t fret, this siloed approach is pretty easy to overcome. AMD uses ROCm, with HIP providing a path for adapting CUDA source code. PyTorch’s ROCm implementation preserves familiar GPU APIs, helping many existing workflows transfer with limited changes. Custom extensions and unsupported features can still require development and tuning.

AMD reported at its July 2026 launch that PyTorch, Hugging Face, vLLM, and SGLang were enabled on MI455X. The next step is checking the exact model, software version, and features you need.

For AMD, the opportunity combines substantial GPU memory with an open software stack. For NVIDIA, an existing CUDA deployment may make continuity and previously completed optimization valuable factors in chip decision making.


Where Helios Fits Into the Comparison

MI455X is the GPU; Helios is the platform built around it. A full Helios configuration connects 72 MI455X GPUs, providing approximately 31 TB of aggregate HBM4 memory, alongside AMD EPYC™ CPUs and Pensando™ networking.

That matters when a workload needs multiple GPUs. The chip determines its local compute and memory capabilities; the connections between chips affect how efficiently they work together. Helios uses UALink over Ethernet for connectivity within the rack and Ethernet networking across racks.

So when you're evaluating the MI455X, you're really evaluating Helios too. The chip's spec sheet tells you what one GPU can do, but most real workloads run across many GPUs. How those GPUs talk to each other matters just as much as the raw numbers on the box. That's why a single-GPU benchmark only gets you partway there. To really know how the MI455X will perform in practice, you need to see it running in the configuration you'd actually deploy. That means benchmarking Helios, not just the chip.


AMD MI455X vs. Google TPUs

Google's TPUs are purpose-built AI accelerators, and the current Ironwood TPU7x generation packs 192 GiB of HBM per chip with roughly 7.38 TB/s of memory bandwidth.

The MI455X publishes substantially more memory capacity and bandwidth per chip. For workloads that are memory-constrained, that can make it the more attractive option — and you get to stay in a familiar GPU software environment while you're at it.

That's not to say TPUs don't have their own strengths, or their own software path. Google supports JAX and PyTorch on TPU hardware, along with tooling for deploying models. If your team already works in that environment, the fair comparison is model support, compilation behavior, and measured performance against a GPU implementation… not just the spec sheet.

And the roadmap keeps moving. Google has announced TPU 8t for training and TPU 8i for inference, but both are still listed as coming soon. For this article, Ironwood is the generation you can actually compare against today.


AMD MI455X vs. AWS Trainium

Trainium, AWS’s custom accelerator was built for training and inference. Trainium3 provides 144 GB of HBM3e and 4.9 TB/s of memory bandwidth per chip. MI455X offers considerably higher published figures in both categories.

Trainium is also part of the AWS environment. For teams already operating there, data location, related services, and commercial commitments may influence its value. Its software runs through AWS Neuron, and the native PyTorch backend TorchNeuron entered public beta in October 2026.

And adoption appears to be substantial. In April 2026, Anthropic reported using more than one million Trainium2 chips to train and serve Claude. That establishes real use at frontier-model scale, while remaining distinct from a benchmark of the newer Trainium3.

Anthropic is also expanding its hardware options. AMD announced in July that Anthropic plans up to 2 GW of Helios capacity featuring MI455X, with the first gigawatt beginning deployment in the first half of 2027.

These examples show why accelerator selection can vary across workloads. Memory specifications, software fit, and delivered cost all deserve a place in the comparison.


Which chip should you choose?

MI455X deserves a close look when GPU memory capacity and bandwidth are priorities. Rubin deserves consideration when NVIDIA-specific software and existing optimization shape the deployment. TPUs and Trainium offer additional paths for teams whose workloads fit their respective environments.

When comparing costs, don't just compare hardware. To make the right decision, compare what you're actually trying to get done. For training, that means the cost to reach a target quality level, not raw FLOPS. For serving, it means the cost to handle requests within your latency requirement, not peak throughput on a spec sheet. And either way, factor in the engineering effort to get there.

TensorWave focuses on AMD infrastructure and has announced its adoption of Helios. We help customers evaluate how AMD GPUs fit their training, fine-tuning, and inference requirements, starting with the model and software they intend to run.

Explore MI455X on TensorWave.

AMD vs. NVIDIA vs. Google TPUs: Comparing AI Chips

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