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Short answer
NVIDIA remains the default for AI because of CUDA, the deepest software ecosystem, and NVLink systems that join 72 GPUs. AMD is now a real alternative in data centers: the Instinct MI355X carries more memory than a B200 (288 GB vs 180 GB) and roughly matches it in MLPerf inference, and the MI455X (432 GB of HBM4) competes with Rubin in 72-GPU Helios racks. On ROCm, PyTorch, vLLM and SGLang support Instinct GPUs well. For local AI on a desktop, NVIDIA RTX cards are still easier and faster to set up, though Radeon support has improved.
Data-center GPUs compared
| GPU | Memory | Bandwidth | Dense FP8 | Dense FP4 | Scale-up domain |
|---|---|---|---|---|---|
| AMD MI355X (2025) | 288 GB HBM3e | 8 TB/s | 5.0 PFLOPS | 10.1 PFLOPS | 8 GPUs |
| NVIDIA B200 (2024) | 180 GB HBM3e | 8 TB/s | 4.5 PFLOPS | 9 PFLOPS | 8, or 72 in GB200 NVL72 |
| NVIDIA B300 (2025) | 288 GB HBM3e | 8 TB/s | 4.5 PFLOPS | 13.5 PFLOPS | 8, or 72 in GB300 NVL72 |
| AMD MI455X (2026) | 432 GB HBM4 | 23.3 TB/s | 20.1 PFLOPS | 40.3 PFLOPS | 72 (Helios) |
| NVIDIA Rubin (2026) | 288 GB HBM4 | 19.2 TB/s | 17.5 PFLOPS | 35 PFLOPS | 72 (Vera Rubin NVL72) |
On paper, AMD leads on memory in each generation and is close on compute. NVIDIA's advantages are its larger scale-up domains, which arrived a generation earlier, its networking, and software. For the full head-to-head, read MI355X vs B200 and NVIDIA Rubin explained, or compare 28 chips in the specs database.
Benchmarks
In MLPerf Inference 6.0 (April 2026), AMD reported single-node MI355X results from roughly equal to about 19% ahead of B200, and 82% to 104% of B300, depending on the test. These ratios are AMD's own calculations from MLCommons-verified results. Independent testing generally finds that AMD does best on large-memory inference with well-supported models and trails most on workloads that rely on NVIDIA-only software.
AMD's Tensor Core and CUDA equivalents
AMD's equivalent of Tensor Cores is the Matrix Core. On Instinct data-center GPUs, built on the CDNA architecture (MI300X, MI325X, MI355X), each compute unit has Matrix Cores that run MFMA (matrix fused multiply-add) instructions, doing the same job as NVIDIA Tensor Cores: small matrix multiplies at FP16, BF16, FP8 and, on CDNA 4 (MI350 series), FP6 and FP4. Radeon gaming cards use the RDNA architecture instead, where each compute unit has two "AI accelerators" that run WMMA instructions; RDNA 4 (Radeon RX 9000 series) added FP8 and sparsity support. Read what is a Tensor Core for how these units work.
AMD's equivalent of CUDA is ROCm with HIP. ROCm is AMD's open-source GPU computing platform, and HIP is its C++ programming language, deliberately close to CUDA so code can be ported with the HIPIFY tools. The main CUDA libraries have ROCm counterparts: hipBLASLt and rocBLAS for cuBLAS, MIOpen for cuDNN and RCCL for NCCL. PyTorch, JAX, vLLM and llama.cpp all ship ROCm builds.
| NVIDIA | AMD equivalent |
|---|---|
| Tensor Core | Matrix Core (CDNA, Instinct) / AI accelerator (RDNA, Radeon) |
| CUDA platform | ROCm |
| CUDA C++ | HIP |
| CUDA core | Stream processor |
| Streaming multiprocessor (SM) | Compute unit (CU) |
| cuBLAS / cuDNN / NCCL | hipBLASLt, rocBLAS / MIOpen / RCCL |
| NVLink | Infinity Fabric |
Software: ROCm vs CUDA in 2026
| NVIDIA CUDA | AMD ROCm | |
|---|---|---|
| PyTorch | Full support on Linux and Windows | Official on Linux; Windows support newer and still maturing |
| vLLM and SGLang | Full support, usually first | Official support on Instinct GPUs |
| llama.cpp | CUDA backend | HIP backend, plus Vulkan on any Radeon |
| Inference optimizers | TensorRT-LLM, Dynamo, NIM | No direct equivalent for some; AMD tunes vLLM and SGLang instead |
| Porting code | n/a | HIP mirrors the CUDA API; HIPIFY converts much code automatically |
| New models and research code | Usually work on day one | Popular models usually supported quickly; niche code may need work |
ROCm 7, released in 2025, and ROCm 7.2 in January 2026 improved performance, added support for more Radeon cards and brought better tooling. The remaining gap is breadth: custom CUDA kernels, CUDA-only libraries and a much larger community of developers and answers online.
For local AI on a desktop
| Card | Memory | Bandwidth | Notes |
|---|---|---|---|
| NVIDIA RTX 5090 | 32 GB GDDR7 | 1,792 GB/s | Fastest consumer card; widest software support |
| NVIDIA RTX 5080 | 16 GB GDDR7 | 960 GB/s | Good for 8B to 14B models |
| AMD Radeon AI PRO R9700 | 32 GB GDDR6 | 640 GB/s | 32 GB at a lower price; slower memory |
| AMD Radeon RX 9070 XT | 16 GB GDDR6 | 640 GB/s | Works with llama.cpp, Ollama and LM Studio |
Because generation speed follows bandwidth, the RTX 5090 is roughly 2.8 times faster than a 640 GB/s Radeon on models that fit both. Radeon cards can make sense when price per gigabyte of VRAM matters more than speed. See the best GPU for local LLMs and RTX 5090 for AI.
Which should you choose?
- Choose NVIDIA for the easiest setup, research and custom kernels, NVIDIA-only tools, the largest NVLink systems, or if your team already knows CUDA.
- Choose AMD for memory-heavy inference of popular open models, when AMD capacity is cheaper or easier to get, or to avoid depending on one vendor.
- For local AI, NVIDIA remains the simpler choice, while AMD can offer more VRAM per dollar for llama.cpp-style inference.
Frequently asked questions
Is AMD good for AI in 2026?
Yes, especially in data centers. MI355X roughly matches B200 in MLPerf inference with more memory, and major AI companies have announced large AMD deployments. Software is the main trade-off: it works well for mainstream frameworks and models and requires more effort for anything custom.
Can AMD GPUs run CUDA?
Not directly. Code must be ported to HIP, which is very similar to CUDA, and HIPIFY automates much of the work. Code written in PyTorch usually runs on AMD without changes.
Why does NVIDIA dominate AI?
NVIDIA invested in CUDA from 2007, long before deep learning took off, so nearly all AI software was built on it. It also sells complete systems, with GPUs, NVLink switches, networking and software designed together.
Is the Radeon RX 9070 XT good for running LLMs?
It runs models up to about 14B at 4-bit well within its 16 GB, using llama.cpp, Ollama or LM Studio. An NVIDIA card with the same memory is usually somewhat easier to set up.
