Best 24 GB GPU for Local LLMs: The 3 Cards That Actually Matter

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 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.

The RTX 3090 is the cheapest way to get 24 GB of VRAM with CUDA support. On the used market it costs a fraction of the 4090 while offering the same VRAM capacity. For builders who want to run larger models and cannot justify the cost of a new GPU, the 3090 is the entry ticket to 24 GB inference.

At 936 GB/s bandwidth it is slightly slower than the 4090 and 7900 XTX, but the difference in token generation speed is modest - typically 10-15% slower for the same model. You still get CUDA, you still get 24 GB, and the Ampere architecture supports Flash Attention and most quantization formats through llama.cpp and ExLlamaV2.

The main compromises are generational. Ampere lacks FP8 support (that is an Ada Lovelace and Blackwell feature), so you lose one potential speedup for quantized inference. The 3090 also draws 350 W and runs warm, especially on reference coolers. An aftermarket model with a good cooler is worth the small price premium on the used market.

If you are experimenting with local LLMs and want to see what 24 GB VRAM unlocks without spending GPU-launch money, the used 3090 is the lowest-risk option. It handles everything from 7B to 35B models on GPU, and even 70B models with partial offloading. Just make sure the card you buy has been tested and has clean VRAM.