# Is the RTX 3090 still the best GPU for local AI in 2026?

> Mostly, yes. A used RTX 3090 is still the cheapest way to get 24GB of fast CUDA memory: about $1,050 in October 2026 for 936 GB/s, enough for Qwen3.8 27B at Q5_K_M (~34 tok/s estimated) or Qwen3.6 35B-A3B (~159 tok/s estimated). It also scales to two or four cards more cheaply than anything else. Buy new instead if you want a warranty and FP4 support (RTX 5070 Ti at 16GB, RTX 5090 at 32GB), and look elsewhere if you need more than 24GB on one card.

Mostly, yes. Six years after launch, a used [RTX 3090](/gpus/rtx-3090) is still the cheapest way to get 24GB of fast CUDA memory into a desktop. Used cards sold for about $1,050 to $1,450 in October 2026, and for that you get 936 GB/s of bandwidth, enough to run [Qwen3.8 27B](/models/qwen3-8-27b) at Q5_K_M at ~34 tok/s or [Qwen3.6 35B-A3B](/models/qwen3-6-35b-a3b) at ~159 tok/s by our estimates. Nothing new at that price has 24GB, and nothing scales to two or four cards as cheaply.

It's not the right card for everyone anymore, though. If you want a warranty and FP4 support, buy new: the [RTX 5070 Ti](/gpus/rtx-5070-ti) at 16GB or the [RTX 5090](/gpus/rtx-5090) at 32GB. If you need more than 24GB on a single card, the 3090 can't help you. And it's gotten more expensive. The $700 to $850 I called a good deal in 2025 is gone, and street prices in October 2026 are moving week to week because of the memory shortage, so treat every price here as a snapshot.

## Why I still have one

When NVIDIA launched the 3090 in September 2020, its biggest customers were Ethereum miners buying cards before they ever reached a store shelf and gamers camping outside Best Buy. Local AI wasn't really a thing yet. OpenAI had published the GPT-3 paper a few months earlier, and the state of the art most of us could touch at home was telling a banana from a cat.

My first 3090 was an EVGA card I bought from a friend in Turkey who'd been mining Ethereum with more than a hundred of them. It has a special place for me, partly because the 30 series was EVGA's last generation of graphics cards before it left the GPU business.

The 3090 also took over from NVIDIA's Titan cards, the Titan X, Titan RTX and Titan V, which were NVIDIA's earlier experiments with prosumer GPUs. I bought a Titan on r/hardwareswap in college for about 70% of what it was worth, and the pattern was the same then: gamers paid for these cards, but researchers and tinkerers got the most out of them. The 3090 brought the Titan RTX's 24GB down to a $1,499 GeForce card, and it kept NVLink, which the 4090 dropped.

I sold all my 4090s ahead of the 5090 launch. I kept my last 3090, and I don't plan on selling it.

## What 24GB gets you in 2026

**Still the value pick for 24GB: NVIDIA GeForce RTX 3090** (24GB, 936 GB/s, ~$1,050 used). The default value pick for 24GB of CUDA memory and the backbone of most multi-GPU builds. Details: https://llamabuilds.ai/gpus/rtx-3090

The models have changed a lot since I first wrote this post, and 24GB goes further than it used to. Here's what our estimator says one 3090 handles at 8K context:

- **Qwen3.8 27B at Q5_K_M**: about 21GB, ~34 tok/s estimated. This is the dense model I'd run on one card.
- **Qwen3.6 35B-A3B at Q3_K_M**: about 19GB, ~159 tok/s estimated. Q4_K_M squeezes in at about 24GB, but it's tight.
- **[Gemma 4 31B](/models/gemma-4-31b) at Q4_K_M**: about 22.5GB, ~33 tok/s estimated, also tight.
- **[gpt-oss-20b](/models/gpt-oss-20b)** in its native MXFP4: about 15GB, ~119 tok/s estimated, with plenty of room for long context.
- **[gpt-oss-120b](/models/gpt-oss-120b)** with its experts offloaded to 64GB of system RAM: ~21 tok/s estimated.

The full list is on the [RTX 3090 best models page](/gpus/rtx-3090/best-models). The 3090's 936 GB/s is about 93% of the RTX 4090's bandwidth, and since token generation mostly comes down to how fast the card can read weights, the gap in chat speed is smaller than the gap in price. Where the 3090 shows its age is compute: it has no FP8 or FP4 hardware, so prompt processing and image generation are slower than on newer cards.

## How it compares in 2026

| GPU | Memory | Bandwidth | Price |
| --- | --- | --- | --- |
| RTX 3090 | 24GB | 936 GB/s | ~$1,050 used |
| RTX 5060 Ti 16GB | 16GB | 448 GB/s | ~$620 |
| RTX 5070 Ti | 16GB | 896 GB/s | ~$1,200 |
| RTX 4090 | 24GB | 1,008 GB/s | ~$2,500 used |
| RTX 5090 | 32GB | 1,792 GB/s | ~$4,300 |
| RX 7900 XTX | 24GB | 960 GB/s | ~$1,050 used |
| R9700 | 32GB | 640 GB/s | ~$1,800 |
| Arc Pro B60 | 24GB | 456 GB/s | ~$650 |
| MI50 32GB | 32GB | 1,024 GB/s | ~$600 used |

### RTX 5060 Ti 16GB and RTX 5070 Ti

The [RTX 5060 Ti 16GB](/gpus/rtx-5060-ti-16gb) is the cheapest new NVIDIA card with 16GB at about $620, but its 448 GB/s is under half the 3090's, and 16GB forces Qwen3.8 27B down to Q3_K_M at ~26 tok/s estimated. The RTX 5070 Ti, at about $1,200 new, gets close on bandwidth with 896 GB/s and runs gpt-oss-20b at ~125 tok/s estimated. It's still a 16GB card, though, so the 27B model runs at Q3_K_M (~51 tok/s estimated) instead of Q5_K_M. You get a warranty, FP4 support and lower power draw. For LLMs I'd still take the extra 8GB. More in our [RTX 3090 vs RTX 5070 Ti comparison](/compare/rtx-3090-vs-rtx-5070-ti).

### RTX 4090 and RTX 5090

The [RTX 4090](/gpus/rtx-4090) has the same 24GB with 1,008 GB/s and costs about $2,500 used. It runs Qwen3.8 27B at Q5_K_M at ~40 tok/s estimated against the 3090's ~34, adds FP8 support and processes prompts much faster, but you pay about 2.4 times as much for the same memory ([RTX 3090 vs RTX 4090](/compare/rtx-3090-vs-rtx-4090)).

The RTX 5090 is the best consumer card you can buy: 32GB of GDDR7 at 1,792 GB/s, Qwen3.8 27B at Q6_K at ~60 tok/s and Qwen3.6 35B-A3B at Q5_K_M at ~196 tok/s, both estimated. It also costs about $4,300 new against a $1,999 MSRP and draws up to 575W. Four used 3090s cost about the same and give you 96GB ([RTX 3090 vs RTX 5090](/compare/rtx-3090-vs-rtx-5090)).

### Radeon RX 7900 XTX and Radeon AI PRO R9700

The [RX 7900 XTX](/gpus/rx-7900-xtx) is the closest match on paper: 24GB at 960 GB/s for about $1,050 used, the same price as a 3090. ROCm and llama.cpp's Vulkan backend both run it, and our estimate is ~31 tok/s on Qwen3.8 27B at Q5_K_M, because we assume AMD gets a bit less out of its bandwidth than CUDA does. The real gap is software: some tools are still CUDA-first, and there's no NVLink ([RTX 3090 vs RX 7900 XTX](/compare/rtx-3090-vs-rx-7900-xtx)).

The [Radeon AI PRO R9700](/gpus/radeon-ai-pro-r9700) has 32GB and a blower cooler made for multi-GPU workstations, but only 640 GB/s, and it sold for about $1,800 in October 2026 against a $1,299 launch price. That's ~18 tok/s estimated on Qwen3.8 27B at Q6_K. It makes sense when you want four cards in a tower ([RTX 5090 vs R9700](/compare/rtx-5090-vs-radeon-ai-pro-r9700)).

### Arc Pro B60, Instinct MI50 and Tesla P40

The [Arc Pro B60](/gpus/arc-pro-b60) is the cheapest new 24GB card at about $650, but at 456 GB/s it runs Qwen3.8 27B at Q5_K_M at ~13 tok/s estimated ([RTX 3090 vs Arc Pro B60](/compare/rtx-3090-vs-arc-pro-b60)). The [Instinct MI50 32GB](/gpus/instinct-mi50-32gb) has 1,024 GB/s of HBM2 and was the budget favorite in 2025 at around $150. Listings now ask about $600 to $830, current ROCm 7 releases no longer support it, and it's passively cooled, so it needs a fan shroud ([RTX 3090 vs MI50](/compare/rtx-3090-vs-instinct-mi50-32gb)). The [Tesla P40](/gpus/tesla-p40) still gets you 24GB for about $230, but it's a 2016 Pascal card with 346 GB/s and no tensor cores. CUDA 13 dropped Pascal, and [NVIDIA has said](https://nvidia.custhelp.com/app/answers/detail/a_id/5706) the R580 driver branch is the last for Maxwell, Pascal and Volta GPUs ([RTX 3090 vs Tesla P40](/compare/rtx-3090-vs-tesla-p40)).

## Where the 3090 really shines: more than one

One 3090 is a good card. Two or four of them is where nothing else at the price comes close.

**Two cards** give you about 46.6GB usable. That runs Qwen3.8 27B at Q8_0 (~22 tok/s estimated), Gemma 4 31B at Q8_0, or Qwen3.6 35B-A3B at Q6_K (~85 tok/s estimated). gpt-oss-120b still needs some offloading, at ~32 tok/s estimated. The [portable dual 3090 build](/builds/portable-25l-nvlinked-dual-3090-llm-rig) fits two NVLinked cards in a 25-liter case by limiting each to 300W, and the builder reports about 63°C on the GPUs at 37 to 40 dB.

**Four cards** give you about 93GB, enough to hold gpt-oss-120b entirely in VRAM (~56 tok/s estimated), [Qwen3.5 122B-A10B](/models/qwen3-5-122b-a10b) at Q4_K_M (~39 tok/s estimated) or [GLM-4.5-Air](/models/glm-4-5-air) at Q5_K_M (~31 tok/s estimated). The [four-3090 IKEA table build](/builds/local-ai-workstation-4x-nvidia-3090-ikea) is a good template; the builder paid about $4,300 for the whole machine by buying everything used.

**Twelve cards** is where it gets silly. [The Machine](/builds/the-machine-12x-rtx-3090) has 288GB of VRAM on an EPYC board and ran Llama 3.1 405B. If one card is all you need, the [sleeper 3090 build](/builds/sleeper-rtx-3090) is a nice way to hide it in a normal-looking desktop.

A few things worth knowing before you build one:

- **You don't need NVLink.** llama.cpp and Ollama split a model across cards by layer by default, which passes very little data between GPUs. NVLink helps more with tensor-parallel runtimes and training.
- **Power limit the cards.** A 3090 is a 350W card with sharp transient spikes. Capping each card at 280 to 300W cuts heat and noise, and because generation is mostly limited by memory bandwidth, you give up less speed than you'd expect. Our [Linux setup guide](/guides/nvidia-gpus-on-linux) shows how.
- **Blowers are worth it if you find them.** Blower-style 3090s are rare and loud, but they exhaust heat out the back, which is what you want with cards packed next to each other.
- **Plan the power and the room.** Four cards at 300W plus a workstation CPU needs a serious power supply, and twelve needs a conversation with an electrician.

## Buying a used 3090 in 2026

Some brands are better than others, especially if you run Linux. I stick to NVIDIA's Founders Edition, EVGA and MSI because their fans respond to the driver's fan control on Linux. Cards from some other partners have had fan-control problems on Linux, which is a big deal when a card runs at full load for days. I'd avoid the Dell OEM version of the 3090 and any 3090 with an AIO liquid cooler attached; a pump that's four or five years old is one more part that can fail.

The 3090's other weak spot is memory heat. Half of its 24 memory chips sit on the back of the board, and on many cards they run hot under long loads; fresh thermal pads often help. Mining history isn't automatically a problem (my own card was a mining card), but run a sustained load for at least half an hour before you commit and watch for throttling, crashes or fan noise. Size your power supply for the 3090's transient spikes, not just its 350W rating.

## When I'd buy something else

- **You want new hardware with a warranty.** Get an RTX 5070 Ti if 16GB is enough, or an RTX 5090 if you can stomach the price.
- **You need more than 24GB on one card.** Look at the RTX 5090, the R9700 or NVIDIA's workstation cards.
- **You care about image and video generation more than LLMs.** Newer cards with FP8 and FP4 hardware are much faster there.
- **You want quiet and low power.** A Mac or a mini PC with lots of unified memory will be slower, but it won't heat your room. See our [Mac and mini PC guide](/best/macs-and-mini-pcs-for-local-llms).

For everyone else, the 3090 is still where I'd start. If you're planning a single-card desktop or a basement rack, our [builds](/builds) and the [GPU buying guide](/best/gpus-for-local-llms) are good next stops. I'm keeping mine.

## Frequently asked questions

### How much does a used RTX 3090 cost in 2026?

Used RTX 3090s sold for about $1,050 to $1,450 in October 2026, up from the $700 to $850 that counted as a good deal in mid 2025. Prices are moving quickly because of the memory shortage, so check current listings before you buy.

### Should I buy an RTX 3090 or an RTX 5070 Ti for local LLMs?

The RTX 3090 for most LLM work. It has 24GB against the 5070 Ti's 16GB, so it runs Qwen3.8 27B at Q5_K_M (about 21GB) entirely on the GPU, while the 5070 Ti has to drop to Q3_K_M. The 5070 Ti is new, has a warranty, supports FP4 and costs about $1,200.

### Is a used RTX 4090 worth it over an RTX 3090?

Only if speed matters more than money. The RTX 4090 has the same 24GB and about 8% more bandwidth (1,008 vs 936 GB/s), so it runs the same models a bit faster, about 40 versus 34 tok/s estimated on Qwen3.8 27B at Q5_K_M, for roughly 2.4 times the used price.

### How many RTX 3090s do I need to run gpt-oss-120b?

Four, if you want it entirely in VRAM. gpt-oss-120b needs about 68GB at 8K context, and four 3090s give about 93GB usable, for about 56 tok/s by our estimate. One or two cards can still run it by offloading experts to 64GB of system RAM, at about 21 to 32 tok/s estimated.

### Do I need NVLink to use two RTX 3090s?

No. llama.cpp and Ollama split a model across cards by layer over PCIe, which sends little data between the GPUs and works fine for single-user inference. NVLink matters more for tensor-parallel runtimes and fine-tuning, where the cards exchange data constantly.

### Which RTX 3090 brands work best on Linux?

Stick to NVIDIA's Founders Edition, EVGA and MSI cards, which support native fan control on Linux. That matters when a card runs at full load for hours. I'd avoid Dell OEM 3090s and any 3090 with an attached AIO liquid cooler.

---
Source: https://llamabuilds.ai/guides/rtx-3090-for-local-ai · Updated October 14, 2026 · Llama Builds. Speeds marked ~ are estimates from our bandwidth model (https://llamabuilds.ai/methodology); measured results are labeled as such.