Best GPUs for AI in 2026

For a local AI build, pick the GPU by the model you will actually run, then by power and case: the NVIDIA GeForce RTX 5090 and its 32 GB of GDDR7, documented on NVIDIA's US spec page, is the most capable single GeForce card here; the RTX 5070 Ti's 16 GB, documented by NVIDIA, is the mainstream buy; and an existing RTX 4090 with 24 GB is often still the right card to keep. Specs, model sizes and power requirements checked November 4, 2026, from NVIDIA's pages, Ollama's library and its GPU documentation.

• 32 GBRTX 5090 GDDR7, per NVIDIA's spec page
• 43 GBOllama's listed size for llama3.3:70b, before context memory
• 1,000 WNVIDIA's minimum reference system power for the RTX 5090

Quick picks

Compare specifications and all conditions

NVIDIA GeForce RTX 5090

Documented memory

32 GB GDDR7

Total graphics power

575 W

Reference system power

1,000 W

All specifications

Best for

Larger models and long context on one card

NVIDIA GeForce RTX 5070 Ti

Documented memory

16 GB GDDR7

Total graphics power

300 W

Reference system power

750 W

All specifications

Best for

Mainstream budgets and mid-size models

NVIDIA GeForce RTX 5080

Documented memory

16 GB GDDR7

Total graphics power

360 W

Reference system power

850 W

All specifications

Best for

Larger single-card jobs on a smaller PSU

NVIDIA GeForce RTX 4090

Documented memory

24 GB GDDR6X

Total graphics power

450 W

Reference system power

850 W

All specifications

Best for

The card many local-AI users already own

Full comparison table ↔

Swipe to compare. Row labels stay visible.

Pick	Documented memory	Total graphics power	Reference system power	Best for
NVIDIA GeForce RTX 5090	32 GB GDDR7	575 W	1,000 W	Larger models and long context on one card
NVIDIA GeForce RTX 5070 Ti	16 GB GDDR7	300 W	750 W	Mainstream budgets and mid-size models
NVIDIA GeForce RTX 5080	16 GB GDDR7	360 W	850 W	Larger single-card jobs on a smaller PSU
NVIDIA GeForce RTX 4090	24 GB GDDR6X	450 W	850 W	The card many local-AI users already own

Pick	Documented memory	Total graphics power	Reference system power	Best for
NVIDIA GeForce RTX 5090	32 GB GDDR7	575 W	1,000 W	Larger models and long context on one card
NVIDIA GeForce RTX 5070 Ti	16 GB GDDR7	300 W	750 W	Mainstream budgets and mid-size models
NVIDIA GeForce RTX 5080	16 GB GDDR7	360 W	850 W	Larger single-card jobs on a smaller PSU
NVIDIA GeForce RTX 4090	24 GB GDDR6X	450 W	850 W	The card many local-AI users already own

Define the model before the VRAM

A card is only a candidate when the completed job fits in its memory. Start from a real model's published size, not a parameter count, and add context and runtime memory on top. Ollama's library lists concrete package sizes:

These are published package sizes, not measured VRAM allocations. Ollama documents that a default context window of 4,096 tokens applies unless you change it, that parallel requests multiply context memory (a 2K context with 4 parallel requests becomes an 8K context), and that a model must fit completely in VRAM to load alongside another model. Our RTX 5090 vs 4090 comparison works through a full context budget for Qwen3-32B; the same method applies to any card here.

• RTX 5070 Ti16GB
• RTX 508016GB
• RTX 409024GB
• RTX 509032GB

Source: NVIDIA's linked full specifications; memory capacity is not an inference benchmark

The same check rules out romantic upgrades: the 243 GB listed for llama3.1:405b and the 142 GB listed for qwen3:235b on Ollama's library pages are workstation-class memory plans, not single-GeForce-card projects.

Best overall for local AI: NVIDIA GeForce RTX 5090

NVIDIA's RTX 5090 page documents 32 GB GDDR7 on a 512-bit interface, with a PCI Express Gen 5 host interface, 575 W total graphics power and a 1,000 W minimum system power based on a PC configured with a Ryzen 9 9950X.

• 32 GB GDDR7: the only single GeForce card here with a plausible shot at the 19 GB and 20 GB models above plus real context
• CUDA capability 12.0, documented by NVIDIA on the card's spec table
• Supplemental power: four PCIe 8-pin cables into the included adapter, or one 600 W PCIe Gen 5 cable; the preparation panel says the card supports three or four PCIe 8-pin cables
• NVLink: not supported

The trade-offs are the host, not the card. NVIDIA's page also carries a lower preparation panel (850 W, three cables) that conflicts with the full specifications; Ollama's GPU documentation lists the RTX 5090 under compute capability 12.0 and requires driver version 550 or newer. Our RTX 5090 vs 4090 guide covers the clearance conflict and cable rules in detail.

Check the exact RTX 5090 and buying options at NVIDIA. Check the exact board: NVIDIA says specifications vary by add-in-card manufacturer, so check the add-in-card manufacturer's published system-power requirement rather than assuming the 1,000 W Founders Edition/reference figure applies.

Best mainstream pick: NVIDIA GeForce RTX 5070 Ti

NVIDIA's RTX 5070 family page documents the RTX 5070 Ti at 16 GB GDDR7 on a 256-bit interface, 300 W total graphics power, a 750 W minimum system power, and 2x PCIe 8-pin cables or one 300 W or greater PCIe Gen 5 cable.

• 16 GB GDDR7 for the 5.2 GB and 9.3 GB models with generous headroom, and the 19 GB and 20 GB models as context-dependent candidates
• Slot count and SFF-Ready status vary by add-in-card manufacturer, per NVIDIA's card-dimensions table
• CUDA capability 12.0, listed by Ollama's GPU documentation alongside the other RTX 50xx cards

The trade-offs: 16 GB cannot hold the larger single-card jobs, and this is the one pick with no Founders Edition — NVIDIA's table lists its length and width as varying by manufacturer, so buy by the specific partner board's dimensions and warranty, not the model name.

Check RTX 5070 Ti boards and buying options at NVIDIA.

Best for larger single-card jobs on a smaller PSU: NVIDIA GeForce RTX 5080

NVIDIA's RTX 5080 page documents the same 16 GB GDDR7 as the 5070 Ti, at 360 W total graphics power, an 850 W minimum system power, and 3x PCIe 8-pin cables or one 450 W or greater PCIe Gen 5 cable.

• Founders Edition: 304 mm long, 137 mm wide, 2-slot, documented by NVIDIA
• Same 16 GB memory class as the 5070 Ti, with a CUDA capability of 12.0

The trade-offs: it shares the 16 GB ceiling, so choose it over the 5070 Ti for host reasons — a Founders Edition board with documented dimensions — rather than for larger models.

Check the RTX 5080 Founders Edition at NVIDIA.

The card you may already own: NVIDIA GeForce RTX 4090

NVIDIA's RTX 4090 page documents 24 GB GDDR6X on a 384-bit interface, 450 W total graphics power, an 850 W minimum system power, and 3x PCIe 8-pin cables or a 450 W or greater PCIe Gen 5 cable.

• 24 GB GDDR6X: every model in the table above except the 43 GB, 142 GB and 243 GB entries is a fit candidate
• PCI Express Gen 4 host interface
• NVLink: not supported

The trade-offs: it is the previous generation, and NVIDIA publishes no local-AI speed comparison between generations. Whether keeping it beats a 32 GB upgrade is a memory question, not a speed question — our RTX 5090 vs 4090 guide answers it job by job.

Check the RTX 4090 at NVIDIA.

Single card versus dual cards

Ollama's GPU documentation describes the actual behavior: a model that entirely fits on any single GPU loads on that GPU, and a model that does not fit is spread across the available GPUs. NVIDIA lists NVLink as not supported on every card here, so a second card is runtime-managed placement over the PCI bus, with the PCIe generation documented for each card (Gen 5 on the RTX 50xx family, Gen 4 on the RTX 4090) as the interconnect.

• Yes: More total memory for a model that one card cannot hold, via runtime spreading
• Yes: A second model loading simultaneously, when each fits completely in VRAM
• No: A single merged GPU: NVLink is not supported on any card here
• No: A speedup for a model that already fits on one card
• Partly: Different power, cooling and case requirements to validate for the whole host

A dual-card host needs a second card slot, a second set of dedicated power cables, and case airflow for two 300-575 W boards at once. For a job that fits neither one nor two of these cards, a hosted service is the honest alternative: our NAS vs cloud storage guide walks through the same local-versus-hosted tradeoff for data.

How we choose

We have not tested any of these cards. Memory, interface, power and connector figures come from NVIDIA's US specification pages, linked in each section; model sizes, context windows, default context and multi-GPU behavior from Ollama's library and documentation; and compute capability from Ollama's GPU documentation, which lists the RTX 50xx cards and driver requirements. Picks are ordered by the documented memory each card offers a completed job, and every speed comparison between generations is deliberately left out because NVIDIA publishes none for inference. Full method: How we choose.

Buying guide

Power first, after memory. The full specification tables list 1,000 W (RTX 5090), 850 W (RTX 5080 and 4090) and 750 W (RTX 5070 family) minimum system power, each based on a PC configured with a Ryzen 9 9950X and dependent on your configuration. NVIDIA recommends a PCIe CEM 5.1 compliant PSU. Use the included adapter with the specified number of dedicated PCIe 8-pin cables, or a single 600 W, 450 W or 300 W PCIe Gen 5 cable as documented for each card — and an original cable approved for your exact PSU model, never a modular cable from a different PSU.

Then clearance. The documented Founders Edition boards are 304 mm long and 137 mm wide, 2-slot (RTX 5090 and 5080) or 3-slot at 61 mm (RTX 4090), with 1-2 kW-class PSUs and adjacent cards deciding the real fit. Partner boards vary: NVIDIA says so on every page, and the 5070 Ti has no Founders Edition at all, so its length and width vary by manufacturer.

Then software, before ordering. Ollama supports NVIDIA GPUs with compute capability 5.0+ and driver version 550 and newer. Validate the chosen OS, driver and inference runtime together; check the exact model tag's size and context window on its library page; and set the context explicitly rather than accepting a default.

1. 1

   Pick the exact model, quantization and context

   Its published size plus context and runtime memory is the real budget.

2. 2

   Match a documented memory tier to that budget

   16 GB, 24 GB or 32 GB, per NVIDIA's specification pages.

3. 3

   Size power and cables to the full table

   750-1,000 W reference; dedicated cables or one documented Gen 5 cable.

4. 4

   Check clearance for the exact board

   Founders Edition dimensions are documented; partner boards vary.

5. 5

   Validate the software stack before checkout

   Driver 550+, the runtime's GPU support, and the model's own page.

Verdict

The RTX 5090, with 32 GB of GDDR7 documented by NVIDIA, is the best single GPU for local AI when the job needs more than 24 GB and its 1,000 W host requirement is part of the plan. The RTX 5070 Ti is the mainstream pick at 16 GB and a 750 W reference; buy it by the specific partner board. The RTX 5080 gives the same 16 GB in a documented Founders Edition at 360 W. And if you already run an RTX 4090, keep it: 24 GB still covers most of the models above, and an upgrade only pays when the completed memory budget no longer fits. Whichever tier you choose, confirm the model's own page, your PSU's cables and the board's clearance before ordering — our RTX 5090 vs 4090 guide has the detailed worksheet.