Best GPUs for Running LLMs Locally in 2026

If you run large language models on your own machine, one number decides almost everything: how much video memory (VRAM) your GPU has. VRAM is what holds the model's weights while it generates text. Run out of it and the model either refuses to load, spills into system RAM and slows to a crawl, or forces you into heavier quantization that costs quality.

That makes GPU shopping for local AI simpler than gaming benchmarks suggest. You are not chasing frame rates. You are buying a memory budget, and then asking how much throughput and power draw come with it. The practical question for most people is VRAM per dollar: which card lets you load the largest, most capable model you actually plan to run, without paying for headroom you will never use?

This guide covers three current-generation NVIDIA cards that cover the realistic range for a single-GPU local setup, from a 16 GB value entry point up to the 24 GB ceiling of consumer hardware. Every spec below is taken from the manufacturer listing. Where model-size guidance is approximate, it is labeled as a community rule of thumb, because real memory use shifts with context length, quantization method, and runtime.

How we picked

• VRAM first, everything else second. For local inference, the memory ceiling determines which models you can run at all. We ranked around usable VRAM and what it unlocks.
• Verified manufacturer specs only. CUDA core counts, memory bus width, TGP, and PSU recommendations come straight from the product listings — no inferred or rounded numbers.
• Value per usable gigabyte. A cheaper 16 GB card that runs your target model is a better buy than an expensive 24 GB card you don't need. We weighed price against what each tier actually enables.
• Single-card practicality. Power draw, PSU requirements, and physical size matter when you are building around one GPU. We flagged the real-world build cost of each.
• In-stock on Amazon. All three are purchasable now, though prices in this batch are deal-window-suspect (see the disclaimer below).

The OneClickAI Score

Our proprietary editorial rating, so you can compare these cards on more than raw specs.

OneClickAI Score = Capability (40) + Value (30) + Real-World Fit (20) + Build & Support (10). Each sub-score is our editorial assessment on a 0–100 scale within its category, then weighted.

These sub-scores are our honest editorial judgment based on the verified specs and the local-AI workload — not lab tests.

Product	Capability	Value	Real-World Fit	Build & Support	Score
GIGABYTE RTX 4090 Gaming OC 24GB	95	60	90	84	82.4
GIGABYTE RTX 4080 Super WINDFORCE V2 16GB	80	80	76	82	79.4
Gigabyte RTX 4070 Ti Super WINDFORCE OC 16GB	72	90	70	88	78.6

The 4090 wins on raw capability but its price drags down value. The two 16 GB cards land close together: the 4080 Super for throughput, the 4070 Ti Super for value and low power draw.

GIGABYTE GeForce RTX 4090 Gaming OC 24GB — the 24 GB ceiling

This is the card you buy when you want the most a single consumer GPU can give a local-AI workload.

Verified specs:

• 24 GB GDDR6X, 384-bit memory bus
• 16,384 CUDA cores
• 450W TGP, PCIe 4.0, triple-slot
• NVIDIA recommends an 850W+ power supply

Who it's for: People who want to run larger models than a 16 GB card can hold. The 24 GB of VRAM is the practical ceiling for a single consumer card, and it runs roughly 30B–34B parameter models at 4-bit quantization comfortably. A 70B model is still a stretch — it needs aggressive quantization plus CPU offload, or a second card.

Pros:

• Most usable VRAM available on a consumer GPU, which directly widens the range of models you can run.
• The highest CUDA core count of the three, so it is also the fastest at generating tokens.
• Real headroom for context length on mid-size models, not just barely fitting the weights.

Cons:

• The most expensive card here by a wide margin, which is why its value sub-score is the lowest.
• 450W TGP and an 850W+ PSU recommendation mean a heavier, more expensive build; the triple-slot cooler needs case room.
• Still not enough on its own to run a 70B model without compromises.

Check the price of the GIGABYTE RTX 4090 24GB on Amazon — list price captured around $2,999–$3,399 (live $3,399.95).

GIGABYTE GeForce RTX 4080 Super WINDFORCE V2 16GB — the throughput pick

The middle option: 16 GB of VRAM with more compute than the value card below it.

Verified specs:

• 16 GB GDDR6X, 256-bit memory bus
• 10,240 CUDA cores
• 320W TGP
• ~750W power supply recommended

Who it's for: People running 7B–13B models who want them to move quickly. Those sizes run fast on this card. You can push toward roughly 30B at 4-bit, but it is tight against the 16 GB ceiling and leaves little room for context.

Pros:

• More CUDA cores than the 4070 Ti Super, so higher token throughput at the same 16 GB memory tier.
• Comfortable for the 7B–13B range that covers most everyday local-AI work.
• Lower power and PSU requirement than the 4090, so an easier build.

Cons:

• Same 16 GB ceiling as the cheaper 4070 Ti Super, so it does not unlock larger models — you pay for speed, not more capacity.
• 30B-class models fit only under pressure, with quantization eating quality and context.
• 320W TGP still asks for a capable power supply.

Check the price of the GIGABYTE RTX 4080 Super 16GB on Amazon — list price captured around $999–$1,459 (live $1,459.99).

Gigabyte GeForce RTX 4070 Ti Super WINDFORCE OC 16GB — the value entry

The lowest-cost way into 16 GB, and the lowest power draw of the three.

Verified specs:

• 16 GB GDDR6X, 256-bit memory bus
• 8,448 CUDA cores
• 285W TGP
• ~700W power supply recommended

Who it's for: Anyone starting out with local models who wants the 16 GB memory tier at the best price. It shares the same 16 GB ceiling as the 4080 Super, with lower throughput but the lowest power draw of the three. If your target models are 7B–13B and budget matters, this is the value entry point.

Pros:

• Best value of the three: the 16 GB tier at the lowest price.
• Lowest power draw (285W TGP) and PSU requirement, which makes it the friendliest for a modest build.
• Runs the same 7B–13B models the 4080 Super does, at the same memory ceiling.

Cons:

• Fewest CUDA cores of the three, so it generates tokens more slowly than the 4080 Super.
• 16 GB ceiling means larger models are off the table without a step up to the 4090.
• Not the card for you if throughput, not budget, is your priority.

Check the price of the Gigabyte RTX 4070 Ti Super 16GB on Amazon — list price captured around $1,099–$1,355 (live $1,355.00).

Quick comparison

Product	Key spec	Price (list, deal-suspect)	Best for	Score
GIGABYTE RTX 4090 24GB	24 GB, 16,384 CUDA, 450W	~$2,999–$3,399	~30B–34B models, max headroom	82.4
GIGABYTE RTX 4080 Super 16GB	16 GB, 10,240 CUDA, 320W	~$999–$1,459	Fast 7B–13B, occasional 30B	79.4
Gigabyte RTX 4070 Ti Super 16GB	16 GB, 8,448 CUDA, 285W	~$1,099–$1,355	Best-value 16 GB entry, low power	78.6

Prices are list prices captured in July 2026 and change frequently — check the current price on Amazon before buying.

Local LLM buying guidance

How much VRAM do I need for a 70B model?

As a community rule of thumb — approximate, not exact — a 70B model needs roughly 40 GB+ of VRAM at 4-bit quantization, and realistically wants around 48 GB, which in practice means two cards. None of the single GPUs here reaches that on its own. The 4090's 24 GB can attempt a 70B model only with aggressive quantization plus CPU offload, which trades away speed and quality. If running 70B locally is your firm requirement, plan for a dual-card build rather than expecting one consumer GPU to do it comfortably.

How much VRAM for smaller models?

Again as approximate rules of thumb: a 7B model uses roughly 4–6 GB at 4-bit, a 13B model roughly 8–10 GB, and a 34B model around 20 GB. So a 16 GB card comfortably runs up to about 13B with room for context, and 24 GB reaches roughly 34B. Actual memory use varies with context length and quantization method, so treat these as starting estimates, not guarantees.

16 GB vs 24 GB — which do I need?

Decide by the largest model you actually plan to run. If your work lives in the 7B–13B range, 16 GB is enough, and the 4080 Super (throughput) or 4070 Ti Super (value and low power) are the sensible picks. If you want to run 30B–34B models comfortably, you need the 24 GB of the 4090 — the two 16 GB cards can only reach 30B under pressure, with quantization squeezing out quality and context. In short: 16 GB covers most everyday local-AI use; 24 GB is the lever for mid-size models.

Is a used 3090 24GB an option?

The general point holds: 24 GB of VRAM is the lever that reaches mid-size models, and that memory ceiling is what matters most, wherever it comes from. A used 24 GB card is one way people chase that ceiling on a budget. We are not recommending a specific used-card listing here — we have not verified availability or condition on any particular one — so if you go that route, vet the seller and the card's history yourself.

Bottom line

For most people running local LLMs, the honest answer is a 16 GB card. If you want the 7B–13B range to feel fast, the GIGABYTE RTX 4080 Super 16GB has the throughput. If budget and low power draw matter more and you can accept slower generation, the 4070 Ti Super is the best-value entry to the same 16 GB tier. Step up to the RTX 4090 only if you genuinely need to run 30B–34B models — that is what its 24 GB unlocks, and it is the main reason to pay the premium.

Buying a GPU is one piece of a local-AI rig. For the full picture, see our budget local-AI build under $1,500 and the complete AI hardware stack. If a full desktop GPU is more than you need, compare mini PCs for local AI inference, and don't forget fast storage for model weights — see our guide to portable SSDs for local AI models.