Best AMD vs Best NVIDIA GPU for Local LLMs: Where AMD Wins, and Where CUDA Still Controls the Market

The RX 7900 XTX is the cheapest way to get 24 GB of VRAM on a new GPU. At 960 GB/s memory bandwidth it matches the RTX 5080 on paper, and the extra 8 GB of VRAM opens up model sizes that 16 GB cards simply cannot run. If your budget does not stretch to a 5090 and you want to run larger models, this is the card to look at.

The catch is the AMD software ecosystem. ROCm support for local LLMs has improved significantly - llama.cpp, Ollama, and LM Studio all support AMD GPUs via HIP/ROCm. But support is still behind CUDA in maturity. Some quantization formats and optimization techniques arrive on NVIDIA first, and debugging GPU issues on AMD requires more community research.

Performance is competitive where ROCm is well-supported. For models that fit in 24 GB, token generation speeds are close to the RTX 4090 in many benchmarks. The 7900 XTX also has 24 GB of GDDR6 (not GDDR6X), which means slightly lower bandwidth than NVIDIA's 4090, but the difference is marginal in practice for LLM inference.

Power draw is 355 W with an 800 W PSU recommendation, which is manageable. The card runs warm but within spec, and most aftermarket coolers handle it well. If you are comfortable with ROCm's current state and want 24 GB at the lowest new-GPU price, the 7900 XTX is a strong value.

A used RTX 4090 is arguably the smartest buy for local LLMs right now. You get 24 GB of GDDR6X at 1,008 GB/s bandwidth, full CUDA support, and Ada Lovelace features like FP8 and Flash Attention 2 - all at a significant discount from the new price. The 4090 was the top-tier GPU just one generation ago, and for inference workloads it is still exceptionally capable.

The 24 GB VRAM is the key advantage over a new RTX 5080. You can run Llama 3.1 70B at 4-bit quantization (roughly 38 GB) with partial CPU offloading, or run it entirely on GPU at 3-bit quantization. Models like Command R (35B), Qwen 2.5 32B, and Mixtral 8x7B fit entirely in VRAM. That flexibility is worth the used-market risk for many builders.

Bandwidth at 1,008 GB/s is actually higher than the RTX 5080's 960 GB/s, which means the 4090 generates tokens faster for models that fit in 24 GB. The extra bandwidth matters because inference on large models is memory-bound - the GPU spends most of its time moving weights from VRAM to the compute units.

The risks of buying used are real: no warranty, potential thermal paste degradation, and the small chance of a card that was run hard for crypto mining. Buy from sellers with good reputations, test the card under sustained load before committing, and verify all VRAM is error-free using GPU stress tests. At the right price, a used 4090 is the best value in local LLM hardware.

The RTX 5090 is the most capable consumer GPU for local LLMs in 2026. Its 32 GB of GDDR7 memory gives you enough headroom to run most models that matter - Llama 3.1 70B at 4-bit quantization, Mixtral 8x22B, and even some FP16 models in the 13-30B parameter range without any compromises on context length.

Memory bandwidth is the other half of the equation. At 1,792 GB/s the 5090 moves data through its memory subsystem faster than any consumer card before it. That translates directly into higher token generation speeds, especially for larger models where the bottleneck is almost always memory bandwidth, not compute.

The downside is power. NVIDIA recommends a 1,000 W power supply, and the card draws 575 W under full load. You need a case with excellent airflow, a high-wattage PSU from a reputable brand, and ideally a dedicated circuit if you are running other high-draw components. This is not a subtle GPU - it is a statement piece for your workstation.

CUDA and the broader NVIDIA software ecosystem remain the gold standard for local LLMs. Every major inference framework (llama.cpp, vLLM, ExLlamaV2, Ollama) targets CUDA first. Flash Attention, Tensor Cores, and FP8 support all work out of the box. If you want the least friction between buying a GPU and running models, NVIDIA is still the default choice.