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?

What GPU do I need to run LLMs locally in 2026?

For most people, a 16 GB card is the answer. Sixteen gigabytes covers the 7B–8B and 13B–14B model classes at 4-bit quantization with room left for context, which is where the overwhelming majority of everyday local-AI work lives. Step up to 24 GB — the ceiling on a single consumer GPU — only if you specifically need 32B–34B models, which is the tier 24 GB unlocks at 4-bit. Nothing in the consumer single-GPU range runs a 70B model at any quantization; that requires two cards, CPU offload, or a cloud endpoint.

OneClickAI VRAM Reference Table

Last verified 2026-08-05. This is our maintained reference for matching model size to VRAM. We publish the method so you can check it against your own setup rather than taking our word for it.

Method: VRAM (GB) ≈ parameters (billions) × bytes-per-weight × 1.2 Bytes-per-weight is 2 for FP16, 1 for Q8, and 0.5 for Q4. The 1.2 multiplier is an overhead factor covering the KV cache and activations, which live in VRAM alongside the weights. Figures below use a representative size from each class — 8B, 14B, 34B, and 70B — and are rounded.

Model size class	Q4	Q8	FP16	GPU tier that handles it
7B–8B	~5 GB	~10 GB	~19 GB	16 GB runs Q4 and Q8 with context headroom; FP16 needs a 24 GB card
13B–14B	~8–9 GB	~17 GB	~34 GB	16 GB runs Q4 comfortably; Q8 needs 24 GB; FP16 exceeds 24 GB
32B–34B	~20–21 GB	~41 GB	~82 GB	24 GB runs Q4 only, and tight on context; Q8 and up exceed a single consumer card
70B+	~42 GB	~84 GB	~168 GB	Exceeds every single consumer GPU at every quantization — dual-GPU or CPU offload

The 16 GB tier in that last column means the RTX 4060 Ti 16GB, RTX 4070 Ti Super, and RTX 4080 Super. The 24 GB tier means the RTX 4090, or a used RTX 3090. All are covered below.

Two caveats worth stating plainly. First, this is a planning estimate, not a guarantee: real memory use shifts with context length, quantization method, and runtime, and a long context window can add gigabytes on its own. Second, "fits" is not the same as "runs well" — a model that just barely fits leaves nothing for context, and you will feel that before you feel the token throughput.

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 from NVIDIA's published specifications. No inferred or rounded numbers.
• Priced picks are grounded; the rest are spec-only. We only quote a price and link a listing for cards we track directly. Other cards worth knowing about appear as specs only — no price, no buy link — because we will not publish a price we have not verified.
• 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.
• No lab benchmarks. You will not find tokens-per-second figures here. We have not run those tests, so we do not report them.

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. We score only the two cards we track a verified price for, because a Value sub-score is meaningless without one.

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

The 4090 wins on raw capability and scores highest overall (82.4 vs. 79.4), but its price drags down its Value sub-score. The 4080 Super trades a few overall points for meaningfully better cost-per-GB — roughly $100 per GB of VRAM against roughly $125–$142 per GB on the 4090 — covering the model sizes most people actually run at a fraction of the total cost.

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 per the reference table above it runs 32B–34B models at 4-bit — the tier no 16 GB card reaches. A 70B model is out of range regardless; that needs a second card or CPU offload.

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 any card here, so it is also the fastest at generating tokens.
• Real headroom for context length on 13B–14B 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.

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

Sixteen gigabytes of VRAM with the most compute in that memory tier.

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–14B models who want them to move quickly. Those sizes run fast on this card at 4-bit with context to spare. The 32B–34B tier is out of reach — at ~20–21 GB for 4-bit, it does not fit 16 GB.

Pros:

• The highest CUDA core count in the 16 GB tier, so the highest token throughput at that memory ceiling.
• Comfortable for the 7B–14B range that covers most everyday local-AI work, including 7B–8B at Q8.
• Lower power and PSU requirement than the 4090, so an easier build.

Cons:

• The 16 GB ceiling is shared with much cheaper cards, so you are paying for speed, not more capacity.
• 32B–34B models do not fit at any quantization worth running.
• 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 $1,599.99.

Also worth knowing — specs only

These three cards come up constantly in local-LLM discussions and belong in the picture. We are not tracking a verified price on any of them, so this section is specifications only: no price, no score, no buy link. Check current pricing yourself before deciding.

NVIDIA GeForce RTX 4070 Ti Super 16GB

The lower-power route into the 16 GB tier.

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

Same memory ceiling as the 4080 Super, so it runs the same model sizes, with fewer CUDA cores and meaningfully lower power draw. If your target models are 7B–14B and build simplicity matters, the capability difference against the 4080 Super is throughput, not capacity.

NVIDIA GeForce RTX 4060 Ti 16GB

The lowest-power way to reach 16 GB.

• 16 GB GDDR6, 128-bit memory bus
• 4,352 CUDA cores
• 165W TGP
• 550W power supply recommended

Note the two real compromises against the other 16 GB cards: GDDR6 rather than GDDR6X, on a 128-bit bus rather than 256-bit. Memory bandwidth matters for token generation, and this card has substantially less of it. What it does give you is the full 16 GB memory tier — so the same which models fit answer as the cards above — at 165W and a 550W PSU recommendation, which is a genuinely modest build. Treat it as the capacity-at-low-power option, not a throughput option.

Used RTX 3090 24GB — the used-market route to 24 GB

• 24 GB GDDR6X, 384-bit memory bus
• 10,496 CUDA cores
• 350W TGP
• 750W power supply recommended

The RTX 3090 is a previous-generation card and is no longer manufactured, so this is a used-market option only. That 24 GB on a 384-bit bus is the same memory tier as the 4090, which is exactly why it stays in the conversation for local AI: memory capacity is the ceiling that decides which models load, and this card has the top consumer amount of it.

We are deliberately not quoting a price or linking a listing. Used-GPU pricing swings week to week, and condition varies enormously — these cards are often ex-mining or ex-gaming hardware with years of thermal load on them. If you go this route, vet the seller, ask about the card's history and hours, confirm the return window, and budget for the 350W TGP and 750W PSU recommendation in your build. That diligence is yours to do; we cannot do it for a listing we have not seen.

Quick comparison

Product	VRAM	Memory bus	CUDA cores	TGP	Price	Score
GIGABYTE RTX 4090 24GB	24 GB GDDR6X	384-bit	16,384	450W	~$2,999–$3,399	82.4
GIGABYTE RTX 4080 Super 16GB	16 GB GDDR6X	256-bit	10,240	320W	~$1,599.99	79.4
RTX 4070 Ti Super 16GB	16 GB GDDR6X	256-bit	8,448	285W	—	—
RTX 4060 Ti 16GB	16 GB GDDR6	128-bit	4,352	165W	—	—
RTX 3090 24GB (used market)	24 GB GDDR6X	384-bit	10,496	350W	—	—

A dash in the Price or Score column means we are not tracking a verified price for that card, so we publish neither a price nor a Value-weighted score for it. Specs are from NVIDIA's published specifications. Prices for the two tracked cards are list prices captured in July 2026 and change frequently — check the current price on Amazon before buying.

Local LLM buying guidance

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–14B range, 16 GB is enough — that covers those sizes at 4-bit with context headroom, and 7B–8B at Q8 as well. The single reason to pay for 24 GB is the 32B–34B tier, which needs roughly 20–21 GB at 4-bit and therefore does not fit 16 GB at all. There is no partial credit here: a 16 GB card does not run a 34B model slowly, it does not run it. In short: 16 GB covers most everyday local-AI use; 24 GB is the lever for mid-size models.

Does memory bandwidth matter, or just capacity?

Both, in that order. Capacity is binary — a model either fits in VRAM or it does not, and if it does not, you are into offloading and a large slowdown. Once it fits, memory bandwidth and compute determine how fast tokens come out. That is why the three 16 GB cards here answer the which models fit question identically while differing a lot in practice: the 4080 Super has 10,240 CUDA cores on a 256-bit GDDR6X bus, the 4060 Ti has 4,352 on a 128-bit GDDR6 bus. Same models, different experience. Buy capacity for what you can run, then bandwidth for how it feels.

Frequently Asked Questions

What is the best LLM to run on 16 GB of VRAM?

The useful answer is a size class, not a specific model name, because the leaderboard at each size changes every few months while the memory math does not. With 16 GB you have comfortable room for 7B–8B models at Q4 (~5 GB) or Q8 (~10 GB), and for 13B–14B models at Q4 (~8–9 GB) with context headroom left over. A 13B–14B model at Q8 needs roughly 17 GB, which does not fit. So: pick the strongest current open-weight instruct model in the 13B–14B class and run it at Q4, or drop to 7B–8B at Q8 if you would rather have the higher-precision weights of a smaller model. We do not rank model quality in this guide — that shifts too fast to publish honestly alongside hardware specs.

Which GGUF quantization should I use with 10 GB of VRAM?

Ten gigabytes puts you in the same place as most 12 GB cards, one tier below the 16 GB cards in this guide. Working from the reference table: a 7B–8B model needs ~5 GB at Q4 and ~10 GB at Q8, so Q4 is comfortable with plenty of context room and Q8 sits right at your ceiling with essentially none. A 13B–14B model at Q4 needs ~8–9 GB, which fits with modest context headroom. The practical recommendation for 10 GB is a 4-bit GGUF — the K-quant variants in the Q4 family are the common default — at either size, and stepping up to Q8 only on a 7B model if you can accept a short context. What 10 GB will not reach is the 32B–34B tier, which needs ~20–21 GB even at Q4.

Can I run a 70B model locally?

Not on one consumer GPU. By the reference table above, a 70B model needs roughly 42 GB at Q4, 84 GB at Q8, and 168 GB at FP16 — and the largest consumer card here holds 24 GB. The 4090 can attempt a 70B model only with aggressive quantization plus CPU offload, which trades away both speed and quality, because every layer that spills to system RAM crosses a far slower link. If running 70B locally is a firm requirement, plan for a dual-GPU build, accept the offload penalty, or use a cloud endpoint for those runs and keep the local card for the 7B–34B work it handles well. Do not expect one consumer GPU to do it comfortably.

Is a Mac with unified memory an alternative to a dedicated GPU?

It is a genuinely different architecture, and for some people a better fit. Apple Silicon Macs have no separate VRAM pool — CPU and GPU share one unified memory pool, so the constraint becomes your total system memory rather than a fixed card-level VRAM ceiling. The practical consequence is that a high-memory Mac can hold models that no single consumer NVIDIA card can: the model-size math in our table still applies, but you are sizing against unified memory, not against 16 or 24 GB of GDDR6X. That is the real advantage, and it is why Macs come up constantly in 70B discussions.

The tradeoffs are memory bandwidth, which varies substantially across Apple's chip tiers, and software maturity, since much of the local-AI tooling ecosystem is built CUDA-first — though llama.cpp and its GGUF format run natively on Apple Silicon via Metal. We are not publishing tokens-per-second comparisons in either direction because we have not benchmarked them. If you are weighing a Mac against a GPU build, check the memory bandwidth figure on Apple's published specifications for the specific chip tier you are considering, and size the unified memory against the model classes in our table above.

Which GPUs come with 12 GB of VRAM?

The common 12 GB NVIDIA cards are the RTX 3060 12 GB from the previous generation and the RTX 4070 and 4070 Super from the current consumer lineup. None of them is a pick in this guide — for local AI the 16 GB tier is where the meaningful capability sits — but if a 12 GB card is already in your machine, it is a perfectly good starting point for the 7B–14B model range at Q4 before you spend anything. Our full GIGABYTE RTX 4090 Gaming OC review covers whether the step all the way up to 24 GB is worth it for your workload.

Bottom line

For most people running local LLMs, the honest answer is a 16 GB card — that tier covers the 7B–14B models that make up the bulk of everyday local-AI work. If you want that range to feel fast, the GIGABYTE RTX 4080 Super 16GB has the most compute at the 16 GB ceiling. Step up to the GIGABYTE RTX 4090 24GB only if you genuinely need 32B–34B models, because that tier is the one thing 24 GB unlocks and 16 GB cannot reach at any quantization. If neither budget works, the spec-only cards above reach the same memory tiers at lower power — check their current prices yourself.

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.