Best GPUs for Running Local LLMs in 2026: A VRAM-First Buying Guide

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Buying a graphics card for gaming and buying one for running language models locally are two different exercises. For games, raw shader performance decides most comparisons. For local LLMs in Ollama, LM Studio or llama.cpp, the first question is simpler: how much video memory (VRAM) does the card have? If a model does not fit in VRAM, it spills into system memory and generation slows dramatically.

This guide ranks the cards currently sold on Amazon US by how useful they are for local inference, using manufacturer specifications and the general consensus of the local-LLM community. It is a research guide, not a benchmark report. If you are still deciding whether local AI is right for you at all, start with our overview of on-device and private AI inference tools.

Quick picks

• Best overall (no compromises): GeForce RTX 5090 32GB – the most VRAM and bandwidth on a consumer card.
• Best 32GB value: Intel Arc Pro B65 32GB – 32GB of memory at a fraction of RTX 5090 pricing.
• Best 24GB value: Intel Arc Pro B60 24GB – the cheapest route to 24GB in this roundup.
• Best 24GB CUDA card: GeForce RTX 3090 24GB – previous generation, but 24GB and very high bandwidth.
• Best mid-range: GeForce RTX 5070 Ti 16GB – fast 16GB card with full CUDA support.
• Best budget NVIDIA: GeForce RTX 5060 Ti 16GB – 16GB at an entry-level price point.
• Best budget overall: Radeon RX 9060 XT 16GB – the lowest-cost 16GB card here.
• Best for very large models: GMKtec EVO-X2 (Ryzen AI Max+ 395, 128GB) – a mini PC that can assign up to 96GB of its memory to graphics.

How much VRAM do you actually need?

A useful rule of thumb: a model quantized to 4 bits needs a little over half a gigabyte of memory per billion parameters for its weights, plus extra room for the context window (the KV cache). Longer conversations and bigger documents need more of that extra room.

These are approximate planning figures, not guarantees. Actual memory use depends on the model architecture, quantization format, context length and software.

Memory bandwidth decides speed

Once a model fits, the speed at which it generates text is limited mostly by memory bandwidth, because every new token requires reading the model’s weights. That is why a previous-generation RTX 3090 with 936 GB/s can still feel quick, and why a large-memory mini PC trades speed for capacity.

Software support still matters

NVIDIA’s CUDA has the widest support across local AI tools, and nearly every guide and tutorial assumes it. AMD and Intel cards work with popular runtimes such as llama.cpp (through Vulkan and other back ends), and both companies maintain their own AI software stacks, but expect a bit more setup and to check compatibility for specific tools such as image-generation front ends.

The best GPUs for local LLMs in 2026

1. GeForce RTX 5090 32GB: best overall

The RTX 5090 is the consumer card with the most memory and the highest bandwidth: 32GB of GDDR7 and roughly 1.8 TB/s according to NVIDIA’s specifications. It runs 32B-class models with lots of context and leaves headroom for image and video generation workloads. The ASUS TUF version listed here is a 2.5-slot design that ASUS labels “SFF-Ready.”

The catch is price and supply. Listings were running above $6,000 at the time of writing (October 2026), far above the launch price, so it only makes sense if you need the best single-card performance available.

2. Intel Arc Pro B65 32GB: best 32GB value

The Arc Pro B65 is the most interesting card in this guide for local AI on a budget. ASRock lists 32GB of GDDR6 on a 192-bit bus with 608 GB/s of bandwidth, a 200W power draw, ECC-capable memory and a two-slot blower cooler that exhausts heat out of the case, which matters if you plan to run more than one card. It costs a fraction of an RTX 5090 while matching its memory capacity.

The trade-offs are lower bandwidth than NVIDIA’s flagship and a software stack that is less universally supported than CUDA. For running quantized models in llama.cpp-based tools, that is often an acceptable deal.

3. Intel Arc Pro B60 24GB: best 24GB value

The B60 offers 24GB of GDDR6 with 456 GB/s of bandwidth, a 200W TDP and a single 8-pin power connector, so it drops into most existing desktops without a power supply upgrade. ASRock specifically notes optimization for Linux multi-GPU LLM deployments; two of these cards give you 48GB of total memory for well under the price of a single high-end NVIDIA card.

4. GeForce RTX 3090 24GB: best 24GB CUDA card

Years after launch, the RTX 3090 remains a favorite for local AI because it pairs 24GB of GDDR6X with a 384-bit bus and up to 936 GB/s of bandwidth. That combination makes it fast at text generation and fully compatible with CUDA-only tools. It is a previous-generation card, so compare the price against the newer options above, and confirm whether a listing is new or renewed before buying.

5. GeForce RTX 5070 Ti 16GB: best mid-range

The RTX 5070 Ti is the strongest 16GB card for most people. NVIDIA rates its GDDR7 memory at 896 GB/s, so 7B to 14B models run quickly and image generation is well supported. ASUS lists a minimum 750W power supply with a 12V-2×6 connector, a 304mm length and a 2.5-slot design, and includes a GPU support bracket. (The RTX 5080 also has 16GB, so for pure LLM work the 5070 Ti is usually the better value.)

6. GeForce RTX 5060 Ti 16GB: best budget NVIDIA

The 16GB version of the RTX 5060 Ti is the cheapest current NVIDIA card with enough memory for comfortable local AI. Its bandwidth (448 GB/s per NVIDIA) is half that of the 5070 Ti, so it generates text more slowly, but it fits the same models. Make sure you buy the 16GB model; the 8GB version is far more limiting for LLMs.

7. Radeon RX 9060 XT 16GB: best budget overall

AMD’s RX 9060 XT is the lowest-cost 16GB card in this guide. ASRock lists 16GB of GDDR6 at 20 Gbps on a 128-bit bus (about 320 GB/s), a compact 249mm two-slot design, a single 8-pin connector and a recommended 550W power supply. It is a sensible choice for running 7B-14B models in llama.cpp-based apps such as LM Studio, as long as you are comfortable working outside the CUDA ecosystem.

8. GMKtec EVO-X2 mini PC: best for very large models

If you want to run 70B-class models and do not need top speed, a different approach makes sense. The EVO-X2 uses AMD’s Ryzen AI Max+ 395 with 128GB of LPDDR5X-8000 memory shared between CPU and graphics; GMKtec says up to 96GB can be assigned as VRAM through AMD software. That is far more memory than any consumer graphics card, in a small box drawing between 54W and 140W depending on mode. Bandwidth is much lower than a high-end GPU, so expect slower generation on big models. Our laptops and mini PCs for local LLMs guide covers more alternatives in this category.

Comparison table

Before you buy: build checklist

• Power supply: check the card’s recommended wattage and connector type (8-pin vs 12V-2×6).
• Case clearance: long triple-fan cards can exceed 300mm; measure first.
• System RAM: 32GB or more helps when models partially offload to the CPU.
• Supporting hardware: long inference runs stress cooling and power. Our list of essentials for running local AI models covers support brackets, fans, storage and UPS units.

Frequently asked questions

Is 16GB of VRAM enough for local LLMs?

For most everyday use, yes. 16GB comfortably runs quantized models up to roughly 14B parameters, which covers capable coding and chat models. Move to 24GB or 32GB if you want 27B-32B models or very long context windows.

Should I buy NVIDIA, AMD or Intel for local AI?

NVIDIA has the broadest software support through CUDA, so it is the least effort. AMD and Intel offer more memory for the money and work well with llama.cpp-based tools, but some specialized apps may need extra setup or may not support them yet.

Can I use two GPUs together?

Yes. Tools such as llama.cpp can split a model across multiple cards so their memory adds up. Make sure your motherboard, case and power supply can handle two cards; blower-style cards like the Arc Pro series are designed for this kind of stacking.

Is a used RTX 3090 still worth it?

For LLMs, the 3090’s 24GB and 936 GB/s bandwidth remain very competitive. Compare its current price against the Arc Pro B60 (24GB) and check the seller, warranty and whether the listing is new or renewed.

Do I need a GPU at all to run local AI?

No. Small models run on a modern CPU, and Apple Silicon Macs or large-memory mini PCs like the EVO-X2 handle bigger ones. A dedicated GPU mainly buys you speed.

The bottom line

Buy for memory first, bandwidth second and ecosystem third. If budget is no object, the RTX 5090 is the fastest single-card option. For the best balance of capacity and cost, the Arc Pro B65 32GB and Arc Pro B60 24GB stand out, while the RTX 5070 Ti is the easiest pick for people who want CUDA without flagship pricing.

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