Best GPU for Local LLMs: The One We Keep Recommending (And the 3 We Don't)

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.

The RTX 5080 hits the price-performance sweet spot for local LLMs. At 16 GB GDDR7 with 960 GB/s bandwidth, it runs 7B models at or near their full potential and handles 13B models at 4-bit quantization comfortably. If your workflow centers on Llama 3.1 8B, Mistral 7B, or Phi-3 medium, this card delivers without the premium tax of the 5090.

GDDR7 memory is the key upgrade over the previous generation. The bandwidth is competitive with the RTX 4090 despite having less total VRAM, which means token generation speeds for models that fit in 16 GB are very fast. You are not sacrificing speed - you are sacrificing capacity.

Power draw is reasonable at 360 W with an 850 W PSU recommendation. That is within the comfort zone of most modern PSUs and cases, unlike the 5090 which needs a significant power infrastructure upgrade for many builders.

The limitation is 16 GB of VRAM. Models like Llama 3.1 70B at 4-bit quantization need roughly 38 GB, which does not fit. You can still run it with offloading to system RAM, but inference speed drops significantly. If your goal is running the largest models locally, step up to the 5090 or consider a used 24 GB card.

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.

The RTX 4070 Ti Super is the cheapest new NVIDIA GPU that makes sense for local LLMs. At 16 GB GDDR6X with 672 GB/s bandwidth, it targets the same model range as the RTX 5080 (7B-13B models) but at a significantly lower price. If you are building a new system for local LLMs and your budget does not stretch to $999, this is where you land.

The 4070 Ti Super gets you into the Ada Lovelace generation with FP8 support, DLSS 3, and good power efficiency at 285 W. For inference specifically, FP8 is the feature that matters - it allows certain quantized models to run faster than they would on Ampere cards like the 3090, even though the 3090 has more VRAM.

Bandwidth is the limitation. At 672 GB/s it is noticeably slower than the 5080 (960 GB/s) or 4090 (1,008 GB/s). Token generation speeds for the same model will be lower. For smaller models (7B) this difference is less noticeable, but for 13B models the slower bandwidth becomes more apparent.

This card makes the most sense for someone building a new workstation who wants CUDA support, does not need to run 70B models, and wants to keep the total GPU cost reasonable. Pair it with 32 GB of system RAM and you can even offload larger models, albeit at reduced speed.