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AMD is closing parts of the AI accelerator gap, but the available evidence does not show that it has closed the gap overall. AMD reports competitive results in selected benchmark tests and has advanced its Instinct hardware and ROCm software. Those results apply to particular workloads, systems, and software configurations—not every AI model or deployment. NVIDIA also continues to publish strong results for its Blackwell systems. The useful comparison is therefore workload by workload, not a single all-purpose winner.
What does “closing the AI gap” mean?
There is no single measure of an AI accelerator’s performance. Training a model, serving it for inference, and running high-performance computing workloads put different demands on hardware and software. Results also depend on the model, numerical precision, system size, software stack, and benchmark round.
A claim of parity needs to specify what is being compared. A result on one model, at one scale, with one software release is evidence about that configuration. It does not establish that two vendors perform equally across other workloads or that their systems cost the same to deploy.
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- Workload: training and inference results are not interchangeable.
- Model and precision: a result for one model or precision setting does not automatically transfer to another.
- System and scale: the number of GPUs and the way they work together affect results.
- Software: frameworks, runtimes, and software versions are part of the tested configuration.
- Benchmark context: compare entries from the same round and relevant division, rather than headlines from different rounds.
What AMD’s results show—and what they do not
MLPerf Training v5.1
AMD says its MI355X achieved “near-parity” results against NVIDIA’s latest FP8-based submissions in MLPerf Training v5.1. AMD also reports that MI355X delivered “up to 2.8x” faster training than the previous generation. Both are AMD’s descriptions of results in that specific benchmark round, published in 2025. The “up to” figure is not a promise of the same improvement for every workload, and AMD’s characterization of near-parity is not a universal finding about AI performance.
#1 Best Overall
- NVIDIA Volta GV100 Architecture — 4,608 CUDA Cores, 640 1st-Gen Tensor Cores delivering 14 TFLOPS FP32 and 112 TFLOPS deep learning performance for AI training, inference, HPC, and scientific computing workloads
- 32GB HBM2 ECC Memory — 900 GB/s Bandwidth — High-bandwidth memory on a 4096-bit bus with ECC error correction provides the memory capacity and throughput required for the largest AI models, simulations, and datasets
- PCIe 3.0 x16 Interface — 250W TDP — Standard PCIe Gen3 connectivity with passive cooling designed for enterprise rack server deployment in HPE ProLiant, Dell PowerEdge, and Supermicro platforms with adequate chassis airflow
- NVLink — Scale to 96GB Unified Memory — Connect two V100 GPUs via NVLink at 300 GB/s bi-directional bandwidth to scale GPU memory from 32GB to 96GB for larger AI training and HPC workloads
- Multi-Precision Computing — Supports FP64 (7 TFLOPS), FP32 (14 TFLOPS), FP16 (112 TFLOPS) and INT8 precision modes for flexible deployment across training, inference, and scientific simulation workloads
MLPerf is useful because it tests more than a chip in isolation. MLCommons describes its Training suite as full-system testing of models, software, and hardware across machine-learning applications. Its v5.1 release listed 20 submitting organizations, including AMD and NVIDIA. To evaluate a specific claim, the relevant comparison is the actual result entry: its workload, division, system, and round.
A specific inference comparison
AMD’s ROCm 7.0 post describes an AMD Performance Labs test of DeepSeek R1 inference using SGLang. The configuration compared eight MI355X GPUs running a pre-release ROCm 7.0 build with eight NVIDIA Blackwell B200 GPUs running CUDA 12.8. This gives the test meaningful context, but the configuration alone does not establish a performance result, and the test should not be generalized to other models, software releases, or deployments.
Rank #2
- High-Performance AI Processing: The MX3 is designed to handle the most demanding AI computer vision workloads, delivering exceptional performance and efficiency.
- Flexible Integration: The MX3 can be easily integrated into your existing systems via its M.2 M-key form factor and support for Linux operating systems.
- Energy Efficient: The MX3 is designed to provide high performance while minimizing power consumption.
- Comprehensive Software Development Kit (SDK): The MX3 is supported by a comprehensive SDK that simplifies development and deployment.
- Hardware compatability: The MX3 is compatible with the PCI-SIG M.2 M-key 2280 Specification. It can be used with the Raspberry Pi 5 with a M-key 2280 HAT.
What the hardware and software comparison tells you
The Instinct MI350 series is data-center accelerator hardware intended for AI, machine learning, and high-performance computing—not a standard desktop graphics card. AMD’s product and architecture materials describe the series and its CDNA 4 architecture, but specifications by themselves do not tell you how quickly a complete system will train or serve a particular model.
For practical performance, the accelerator has to work with the rest of the system and the software stack. ROCm is AMD’s software platform; CUDA is used in the cited NVIDIA configuration. A benchmark comparing both vendors therefore compares complete tested setups, not just two pieces of silicon. A published result is most useful when it names the model, runtime, software versions, GPU count, and precision used.
Rank #3
- ✅Powered by 26 Tera-Operations Per Second (TOPS) Hailo-8 AI Processor. 2.5W typical power consumption
- ✅Scalable, enabling simultaneous processing of multi-streams & multi-models
- ✅Enabling real-time, low latency and high-efficiency AI inferencing on the edge devices
- ✅Supports TensorFlow, TensorFlow Lite, ONNX, Keras, Pytorch frameworks
- ✅Supports Linux and Windows. Supports the temperature range of -40°C to 85°C
How to read NVIDIA’s Blackwell results
NVIDIA’s summary of its MLPerf v6.0 Training and Inference results reports Blackwell submissions across tasks and scales. NVIDIA says the summary draws on MLCommons results retrieved in June 2026. Treat the summary as NVIDIA’s presentation of benchmark data; for an exact head-to-head claim, check the corresponding MLCommons entries and match the workload, division, system size, and round.
Results from v6.0 should not be compared casually with AMD’s v5.1 claims as though they were one simultaneous contest. Different rounds may have different submissions and test contexts. NVIDIA’s published results show continued Blackwell participation and performance claims; they do not, by themselves, negate AMD’s v5.1 results or establish a universal ranking.
Rank #4
- 48GB AI graphics accelerator
What this means if you are choosing a system
For an organization evaluating AI compute, first define the workload and deployment rather than choosing by vendor-level benchmark headlines. Ask vendors or cloud providers for evidence that matches the models, software, and scale you plan to use.
- Specify the job. Identify whether you need training, inference, or both, and name the models and expected workload.
- Set the quality and precision target. Confirm that a performance comparison uses a precision and quality level suitable for your use case.
- Match the configuration. Check GPU count, system design, framework or runtime, and software versions. A result on a different setup may not predict your own.
- Compare like-for-like benchmark entries. Use the same benchmark round, workload, and relevant division where possible; distinguish full-system results from component specifications.
- Validate deployment fit. Confirm that the software and model workflow you require are supported in the environment you intend to operate.
- Evaluate economics separately. The cited evidence does not establish an apples-to-apples current cost comparison. Compare actual system or cloud pricing, availability, utilization, and operating requirements for your deployment.
Availability is a separate question
AMD’s Advancing AI 2025 announcement said broad MI350 availability was planned for the second half of 2025 and described deployments with Oracle Cloud Infrastructure. That dated announcement is not a current inventory of cloud regions, instance names, pricing, or stock. Confirm availability and configuration directly with the provider before making a deployment decision.
Quick Recap
Best Value
- Professional AI & Creator Workstation: AMD Radeon AI PRO R9700 GPU with 32GB GDDR6 is engineered for AI development, professional content creation, and compute-intensive workloads.
- Massive 32GB Memory Capacity: 32GB of GDDR6 memory on a 256-bit bus provides ample bandwidth for large AI models, 8K video editing, and complex 3D rendering.
- Advanced RDNA 4 with AI Accelerators: 64 Compute Units with 3rd Gen Ray Tracing and dedicated 2nd Gen AI Accelerators for groundbreaking AI performance and visual computing.
- Professional Blower Cooling: Efficient single blower design exhausts heat directly out of the chassis, ideal for multi-GPU workstation and server configurations.
- Enterprise-Grade Thermal Solution: Vapor chamber heatsink with industrial Honeywell PTM7950 thermal interface material ensures reliable cooling under sustained professional loads.
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