NVIDIA / discrete

NVIDIA GeForce RTX 3090

24 GB decides what fits. 936 GB/s decides how fast it runs once it does.

Open in the calculator →Best models for 24 GB, on every card that size →

Find one: Amazon ↗ · eBay (new and used) ↗Store links may pay us a commission. They never decide which card is suggested — the memory arithmetic does.

Quick answer

The NVIDIA GeForce RTX 3090 fits 214 of 320 sized models entirely in its 24 GB; the largest widely used one is Qwen3.6-35B-A3B (23.1 GB at Q4_K_M). Memory decides what fits; its 936 GB/s of bandwidth decides how fast it answers.

Largest popular model that fitsQwen3.6-35B-A3B36B params · Q4_K_M · needs 23.1 GB~343 tok/s Faster than you readOpen in the calculator →
Most downloaded that fitsQwen3.8-27B28B params · Q4_K_M · needs 18.6 GB~43 tok/s Faster than you readOpen in the calculator →

At 8,192 tokens of context with the whole model in device memory. Speeds are estimates from memory bandwidth, not benchmarks run on this card.

NVIDIA GeForce RTX 3090 · 24 GB · 936 GB/s · Q4_K_M where published · 8,192 tokensSpecificationpublished dataDecode speedestimate

214 of 320 fit entirely

214 of the 320 models the engine can size fit entirely in device memory at Q4_K_M where it is published, otherwise the nearest published format, and 8,192 tokens. 19 run with some layers on system memory (32 GB assumed), and 87 do not run at all.

67%of the models the engine can size fit entirely

24GB of device memory

936GB/s memory bandwidth

19run with system memory

87do not run at 8,192 tokens

Featured models · Q4_K_M at 8,192 tokens, including offload

Model	Needs	Verdict	Decode	Calculator
Qwen3.5-2B2.3B parameters	2.32 GB	fits	~516 tok/s	Open →
NVIDIA-Nemotron-3-Nano-4B-BF164.0B parameters	3.46 GB	fits	~239 tok/s	Open →
Qwen3-VL-8B-Instruct8.8B parameters	7.04 GB	fits	~108 tok/s	Open →
Qwen3.5-9B9.7B parameters	7.05 GB	fits	~131 tok/s	Open →
gemma-4-12B-it12B parameters	9.54 GB	fits	~78 tok/s	Open →
gpt-oss-20b21B parameters	13.8 GB	fits	~254 tok/s	Open →
Qwen3.8-27B28B parameters	18.6 GB	fits	~43 tok/s	Open →
Qwen3.6-27B28B parameters	18.6 GB	fits	~43 tok/s	Open →
Kimi-Linear-48B-A3B-Instruct49B parameters	30.4 GB	6 of 27 layers on system RAM (6.55 GB)	~113 tok/s with offload	Open →
Qwen3.8-Flash-Next180B parameters	111.6 GB	needs 89.8 GB of system RAM; 32 GB assumed	does not run	Open →
DeepSeek-V4-Flash-Vision-Exp305B parameters	188.1 GB	needs 165.5 GB of system RAM; 32 GB assumed	does not run	Open →
GLM-5.3-Flash321B parameters	198.3 GB	needs 175.3 GB of system RAM; 32 GB assumed	does not run	Open →

These examples are selected from prominent labs using the catalogue’s latest Hugging Face 30-day downloads and repository-creation freshness signal, with newer releases guaranteed a place. Offloaded rows assume 32 GB of system RAM, and a speed is only shown for a row that runs. Fitting in memory is not the same as loading: whether the runtime and version you have supports each architecture and format on this machine has not been tested here. Each “Open” link carries the same model, format, context and RAM into the calculator.

Fits entirely in NVIDIA GeForce RTX 3090 memory — 214 of 320 sized models

Every model that fitswhole model resident · offload off

The most demanding model that fits is Qwen2.5-VL-32B-Instruct at Q4_K_M: 23.5 GB of the 24 GB, leaving 0.52 GB spare.

Fully resident at 8,192 tokens (or the model’s own maximum, where that is shorter), offload off, at 936 GB/s. Each row is a run of the engine for this configuration; the rows start with current, prominent releases and “fits” is memory, not a tested runtime. Older or less prominent models remain available through this search and “Show all”.

Find a model in this list

Showing 40 of 214 models that fit.

Model	Format	Needs	Spare	Decode	Downloads / 30d	Calculator
Qwen3.8-27BQwen · 28B params	Q4_K_M	18.6 GB	5.45 GB	~43 tok/s	6.9M	Open →
gemma-4-26B-A4B-itGoogle · 26B params	Q4_K_M	16.8 GB	7.21 GB	~239 tok/s	13M	Open →
gemma-4-31B-itGoogle · 31B params	Q4_K_M	22.2 GB	1.83 GB	~31 tok/s	9.9M	Open →
Qwen3.5-9BQwen · 9.7B params	Q4_K_M	7.05 GB	17.0 GB	~131 tok/s	9M	Open →
Qwen3.5-4BQwen · 4.7B params	Q4_K_M	3.98 GB	20.0 GB	~236 tok/s	7.8M	Open →
Qwen3.6-35B-A3BQwen · 36B params	Q4_K_M	23.1 GB	0.90 GB	~343 tok/s	3.3M	Open →
gemma-4-12B-itGoogle · 12B params	Q4_K_M	9.54 GB	14.5 GB	~78 tok/s	1.9M	Open →
Qwen3.6-27BQwen · 28B params	Q4_K_M	18.6 GB	5.45 GB	~43 tok/s	2.5M	Open →
Qwen3.5-2BQwen · 2.3B params	Q4_K_M	2.32 GB	21.7 GB	~516 tok/s	4.9M	Open →
gemma-4-E4B-itGoogle · 8.0B params	Q4_K_M	5.86 GB	18.1 GB	~210 tok/s	4.4M	Open →
NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16NVIDIA · 32B params	Q4_K_M	20.3 GB	3.71 GB	~33 tok/s	530K	Open →
NVIDIA-Nemotron-3-Nano-4B-BF16NVIDIA · 4.0B params	Q4_K_M	3.46 GB	20.5 GB	~239 tok/s	3.4M	Open →
gemma-4-E2B-itGoogle · 5.1B params	Q4_K_M	4.02 GB	20.0 GB	~556 tok/s	3M	Open →
Qwen3.5-0.8BQwen · 873M params	Q4_K_M	1.46 GB	22.5 GB	~1130 tok/s	2.6M	Open →
Qwen3-VL-8B-InstructQwen · 8.8B params	Q4_K_M	7.04 GB	17.0 GB	~108 tok/s	14.6M	Open →
Qwen3.5-27BQwen · 28B params	Q4_K_M	18.6 GB	5.45 GB	~43 tok/s	1.9M	Open →
North-Micro-Vision-InstructCohereLabs · 2.5B params	Q4_K_M	2.99 GB	21.0 GB	~339 tok/s	180.7K	Open →
Qwen3.5-35B-A3BQwen · 36B params	Q4_K_M	23.1 GB	0.90 GB	~343 tok/s	1.6M	Open →
granite-4.2-8bIBM · 8.8B params	Q4_K_M	7.49 GB	16.5 GB	~98 tok/s	128.4K	Open →
GLM-4.7-Flashzai-org · 31B params	Q4_K_M	20.4 GB	3.59 GB	~32 tok/s	1.8M	Open →
granite-4.1-3bIBM · 3.4B params	Q4_K_M	3.72 GB	20.3 GB	~230 tok/s	521.8K	Open →
LFM2.5-2.6BLiquidAI · 2.7B params	Q4_K_M	2.61 GB	21.4 GB	~369 tok/s	108.1K	Open →
Qwen3-VL-4B-InstructQwen · 4.4B params	Q4_K_M	4.72 GB	19.3 GB	~172 tok/s	3.5M	Open →
granite-4.1-30bIBM · 29B params	Q4_K_M	20.4 GB	3.56 GB	~32 tok/s	301.5K	Open →
Qwen3-VL-2B-InstructQwen · 2.1B params	Q4_K_M	3.05 GB	21.0 GB	~312 tok/s	2.8M	Open →
granite-4.2-3bIBM · 3.7B params	Q4_K_M	3.72 GB	20.3 GB	~230 tok/s	50.6K	Open →
LFM2.5-230MLiquidAI · 230M params	Q4_K_M	1.05 GB	22.9 GB	~2569 tok/s	88.6K	Open →
Qwen3-0.6BQwen · 752M params	Q4_K_M	2.22 GB	21.8 GB	~478 tok/s	29.7M	Open →
gpt-oss-20bOpenAI · 21B params	Q4_K_M	13.8 GB	10.2 GB	~254 tok/s	6.6M	Open →
granite-4.2-30bIBM · 29B params	Q4_K_M	20.7 GB	3.33 GB	~32 tok/s	35.8K	Open →
granite-4.1-8bIBM · 8.8B params	Q4_K_M	7.49 GB	16.5 GB	~98 tok/s	179.3K	Open →
NVIDIA-Nemotron-3-Nano-30B-A3B-BF16NVIDIA · 32B params	Q4_K_M	20.3 GB	3.71 GB	~33 tok/s	875.2K	Open →
LFM2.5-VL-3BLiquidAI · 3.1B params	Q4_K_M	2.85 GB	21.1 GB	~369 tok/s	26.7K	Open →
MiMo-V2.6-Distill-Qwen-9BNEWXiaomiMiMo · 9.4B params	Q4_K_M	6.90 GB	17.1 GB	~131 tok/s	14.6K	Open →
Qwen3-4B-Instruct-2507Qwen · 4.0B params	Q4_K_M	4.72 GB	19.3 GB	~172 tok/s	3.7M	Open →
Ling-3.0-tinyinclusionAI · 7.9B params	Q4_K_M	5.85 GB	18.1 GB	~742 tok/s	17.7K	Open →
Qwen3-8BQwen · 8.2B params	Q4_K_M	7.04 GB	17.0 GB	~108 tok/s	10.7M	Open →
Olmo-3-7B-Instructallenai · 7.3B params	Q4_K_M	7.96 GB	16.0 GB	~92 tok/s	481.7K	Open →
Qwen3-4BQwen · 4.0B params	Q4_K_M	4.51 GB	19.5 GB	~172 tok/s	7.8M	Open →
LFM2.5-8B-A1BLiquidAI · 8.5B params	Q4_K_M	6.06 GB	17.9 GB	~574 tok/s	32.4K	Open →

Sized at the format most people actually download, not at FP16. The catalogue ordered by downloads →

Rent it instead

Rent an RTX 3090 by the hour

What the NVIDIA GeForce RTX 3090 costs on Vast.ai and RunPod right now, per machine, from one card to eight. Every price is read from the provider's own API.

Prices · 18:30 UTC, 22 Sept

1× RTX 3090 · 24 GB

$0.12per hour

Vast.ai · Marketplace · 99.5% reliableRunPod $0.50/h Secure CloudRent on Vast.ai

2× RTX 3090 · 48 GB

$0.24per hour

Vast.ai · Marketplace · 99.5% reliableRent on Vast.ai

4× RTX 3090 · 96 GB

$0.60per hour

Vast.ai · Marketplace · 99.4% reliableRent on Vast.ai

Buy one or rent one?

Your price, your hours, your electricity. Everything else is arithmetic.

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Resale after 3 years ($)

Hours of use per day4 h

$ per kWh

Watts

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Watts start at the NVIDIA GeForce RTX 3090’s published board power (350 W), an upper bound: decoding rarely holds a card at its limit. Rent starts at the cheapest live price (Vast.ai). The electricity price is a placeholder — put yours in.

At 4 h a day, renting costs $175.20 a year.

That is $14.60 a month, and nothing when the machine is stopped.
Owning would cost $127.75 a year in electricity, on top of the price.
Every $1,000 of purchase price takes 21.1 years of this use to earn back.

Enter the price you would pay to see the exact break-even.

On-demand prices for the whole machine. Vast hosts below 98% measured reliability are left out; RunPod’s Community Cloud is vetted third-party hosts and its Secure Cloud is data-centre capacity. Every rentable card compared.

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Find the NVIDIA GeForce RTX 3090: Amazon ↗ · eBay (new and used) ↗Store links may pay us a commission. They never decide which card is suggested — the memory arithmetic does.

Under the hood

The specification behind every figure

What the manufacturer publishes for this device, and the pages it was read from.

Manufacturer specification

Memory scope	dedicated
Capacity	24 GB
Published options	24 GB
Bandwidth	936 GB/s
Memory type	GDDR6X
Bus width	384-bit
FP32 peak	35.6 TFLOPS
Dense matrix peak	71.2 TFLOPS (FP16 Tensor with FP32 accumulate, dense)
Total Graphics Power (TGP)	350 W

Source ledger

NVIDIA RTX Blackwell GPU Architecture ↗

Retrieved 2026-08-30

GeForce RTX 3090 Series Graphics Cards | NVIDIA ↗

Retrieved 2026-08-30

NVIDIA RTX Blackwell GPU Architecture (v1.1) ↗

Retrieved 2026-08-31

Published capacity is a hardware ceiling, not guaranteed free runtime memory. The calculator shows the runtime reserve separately rather than folding it into a single number.

Run the diagnostic on the NVIDIA GeForce RTX 3090 →