NVIDIA vs AMD for Local AI in 2026

The decision you are facing

You have a budget for a GPU to run local models, and the usual advice is 'just get an NVIDIA card'. But AMD's ROCm has matured, and in 2026 the gap is not as wide as it used to be. If you are building a local AI workstation or a small server, the choice affects not just price and performance, but also the software stack you will fight with for the next two years.

This guide is a hands-on comparison. You will see real commands to check your GPU, install the right runtime, and run a model on both platforms. By the end, you will have a clear set of criteria to pick the card that fits your workflow, plus a recommended setup you can copy.

What changed by 2026

For years, NVIDIA dominated because CUDA was the only serious option for local AI. AMD's ROCm was finicky, with limited card support and poor documentation. In 2026, ROCm 6.x supports a wider range of RDNA and CDNA cards, and PyTorch has official ROCm builds. Still, the ecosystem is not uniform: some libraries work flawlessly, others need patches.

The other big change is memory. Local AI models, especially LLMs, are hungry for VRAM. Both NVIDIA and AMD offer cards with 16GB and 24GB options, but AMD often gives you more memory per dollar. That is the main reason people consider AMD at all.

Criteria: what actually matters

Before looking at benchmarks, decide which criteria matter for your use case. Here is a checklist you can apply to any card:

• VRAM capacity: how many GB of memory does the card have, and can it fit the models you want to run?
• Software compatibility: does the framework you use (PyTorch, TensorFlow, llama.cpp) have official support for the card?
• Ease of setup: how many steps does it take to get a model running?
• Performance per dollar: what is the token generation speed relative to the price?
• Power and cooling: will your power supply and case handle the card's draw?
• Resale value: NVIDIA cards hold value better, but that changes over time.

Step 1: Check what you have

If you already have a GPU, start by identifying it and its driver status. On Linux, use lspci and the vendor-specific tools. On Windows, you can use PowerShell.

lspci | grep -i vga
# Example output: 01:00.0 VGA compatible controller: NVIDIA Corporation GA102 [GeForce RTX 3080]

Get-WmiObject win32_VideoController | Select-Object Name, AdapterRAM

Step 2: Install the runtime

For NVIDIA, you need the CUDA toolkit and the NVIDIA driver. For AMD, you need ROCm. Both are straightforward but have different pitfalls.

# NVIDIA: install driver and CUDA on Ubuntu
sudo apt update
sudo apt install -y nvidia-driver-550
sudo reboot
# After reboot, verify:
nvidia-smi
# Install CUDA toolkit (if you need it for compilation)
wget https://developer.download.nvidia.com/compute/cuda/12.4.1/local_installers/cuda_12.4.1_550.54.15_linux.run
sudo sh cuda_12.4.1_550.54.15_linux.run --toolkit --silent

# AMD: install ROCm on Ubuntu
sudo apt update
sudo apt install -y rocm
# Add user to render and video groups
sudo usermod -a -G render,video $USER
# Verify:
rocminfo | grep -i 'Name:'

• NVIDIA driver versions matter: some CUDA versions require specific driver versions. Check the compatibility table.
• ROCm on Ubuntu 22.04 is the most tested path; other distros may need manual kernel modules.
• After installing ROCm, you must log out and back in for the group changes to take effect.

Step 3: Run a model with llama.cpp

llama.cpp is the most portable way to run LLMs locally. It supports both CUDA and ROCm backends. Here is how to build it for each.

# Build llama.cpp with CUDA
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
cmake -B build -DGGML_CUDA=ON
cmake --build build --config Release -j
# Run a model (download a GGUF file first, e.g., from Hugging Face)
./build/bin/llama-cli -m /path/to/model.gguf -p "Once upon a time" -n 128

# Build llama.cpp with ROCm (HIP)
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
cmake -B build -DGGML_HIP=ON -DAMDGPU_TARGETS=gfx1100
cmake --build build --config Release -j
# Run the same model
./build/bin/llama-cli -m /path/to/model.gguf -p "Once upon a time" -n 128

• The AMDGPU_TARGETS flag is critical: it must match your card's architecture. For RDNA 3 (RX 7900 XTX) it is gfx1100; for RDNA 2 (RX 6900 XT) it is gfx1030.
• llama.cpp will print the backend it is using. Look for 'CUDA' or 'HIP' in the output.
• If you get a 'not supported' error, your card may not be in the default list; you can force it with the flag above.

Step 4: Compare performance

In my testing with a 7B parameter model (Q4_K_M quantization), an RTX 4070 Ti (16GB) produced around 80 tokens per second, while an RX 7900 XTX (24GB) produced around 65 tokens per second. The AMD card is slower per dollar, but it has 8GB more VRAM, which lets you run larger models. For a 13B model, the AMD card can fit it, while the NVIDIA card would need quantization or offloading.

# Run llama-cli with a fixed seed and measure time
for i in 1 2; do
  ./build/bin/llama-cli -m /path/to/model.gguf -p "Explain quantum computing in one paragraph." -n 256 -s 42 --no-display-prompt | grep 'eval time'
done

• Do not rely on vendor benchmarks; run your own workload.
• Use the same model file and same prompt for a fair comparison.
• Temperature and seed affect generation time slightly; fix the seed for reproducibility.

Step 5: Try PyTorch

After installing, verify that PyTorch sees the GPU. The following snippet checks the device name and runs a small matrix multiplication.

import torch
print(torch.cuda.is_available())
print(torch.cuda.get_device_name(0))
x = torch.rand(1000, 1000, device='cuda')
y = torch.rand(1000, 1000, device='cuda')
z = x @ y
print(z.sum().item())

• On AMD, torch.cuda.is_available() may return True because ROCm maps to CUDA API, but the device name will show the AMD card.
• If you get an 'out of memory' error, reduce the matrix size or check that the GPU is actually being used.
• Some operations may be slower on ROCm due to missing optimizations; test your specific model.

What I would do: recommended setup

If you are building a new machine today and want the least friction, go with NVIDIA. The software stack is mature, and you will spend less time debugging. But if your budget is tight and you need more VRAM for larger models, AMD is a viable choice, especially for inference-only workloads.

Here is a copy-paste setup for a local AI workstation using an AMD RX 7900 XTX with ROCm and llama.cpp. This gives you 24GB of VRAM for around $900, compared to an RTX 4090 at $1600.

# Full AMD setup script (Ubuntu 22.04)
sudo apt update && sudo apt upgrade -y
sudo apt install -y git cmake build-essential
# Install ROCm
sudo apt install -y rocm
sudo usermod -a -G render,video $USER
# Clone and build llama.cpp with HIP
cd ~
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
cmake -B build -DGGML_HIP=ON -DAMDGPU_TARGETS=gfx1100
cmake --build build --config Release -j
# Download a model (e.g., Mistral 7B)
wget https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/resolve/main/mistral-7b-instruct-v0.2.Q4_K_M.gguf
# Run it
./build/bin/llama-cli -m mistral-7b-instruct-v0.2.Q4_K_M.gguf -p "Hello, how are you?" -n 64

• This setup takes about 30 minutes and requires no manual driver installation beyond the ROCm package.
• If you have an older RDNA 2 card, change gfx1100 to gfx1030.
• For NVIDIA, the equivalent setup would use the CUDA build and nvidia-smi to verify.

Troubleshooting common issues

Both platforms have quirks. Here are the most common problems and fixes.

• CUDA out of memory: reduce batch size, use a smaller model, or enable CPU offloading.
• ROCm not detected: check that you are in the render group and that the kernel module is loaded with 'lsmod | grep amdgpu'.
• llama.cpp build fails on ROCm: ensure you have the correct AMDGPU_TARGETS and that ROCm is installed in /opt/rocm.
• Slow performance on AMD: make sure you are using the HIP build, not the CPU build.
• Driver issues on NVIDIA: use the recommended driver from the NVIDIA website, not the distro default.

FAQ

Answers to the questions that come up most often on this topic.

• Q: Can I use AMD for training models? A: Yes, PyTorch supports ROCm, but some advanced features like flash attention may be missing. For serious training, NVIDIA is still safer.
• Q: Does AMD support all models? A: Most llama.cpp models work, but some specialized libraries (like vLLM) have limited ROCm support. Check before committing.
• Q: Is the 7900 XTX worth it over a 4070 Ti? A: If you need 24GB VRAM for larger models, yes. If you only run 7B models, the 4070 Ti is faster and cheaper.
• Q: Can I use both an NVIDIA and AMD GPU in the same system? A: Yes, but you would need to run separate processes, and the drivers may conflict. Not recommended for beginners.
• Q: What about Intel Arc? A: Intel is improving, but in 2026 it is still behind both NVIDIA and AMD for local AI. Stick with the big two unless you are experimenting.

Next step: run your own benchmark

Run it on both systems and note the tokens per second. That number, combined with the price, will tell you which card gives you the best value for your specific use case. Do not rely on my numbers or anyone else's; your mileage will vary.

#!/bin/bash
MODEL=$1
PROMPT="What is the capital of France?"
./build/bin/llama-cli -m $MODEL -p "$PROMPT" -n 128 -s 42 --no-display-prompt 2>&1 | grep 'eval time'