GPU Rent HubGPU for AI video

GPU for AI video generation, priced by the month.

GPU Rent Hub rents dedicated NVIDIA RTX 5090 32GB servers for AI video generation at $540 a month, which is $0.75 per GPU-hour, paid in Bitcoin, Ethereum, USDT, USDC, Monero, Solana or Litecoin with no identity verification.

Video is the most memory-hungry and the most time-hungry generative workload there is, which is exactly what makes a flat term beat a meter. Here is the sizing arithmetic.

Updated 2026-09-01

$0.75RTX 5090, per GPU-hour on a month

302,400Latent tokens in a 720p 5-second clip

3 TBLocal NVMe per card on a B200, sold two cards at a time

20 TBOutbound included, per instance

01 Definition

What does AI video generation need from a GPU?

Two things, and they are not the same thing: enough VRAM to hold the run, and enough hours to finish the queue.

A video model needs VRAM for four things at once: the model weights, the text encoder, the latent video tensor, and the activations produced while denoising it. The latent is a grid of frames divided by the temporal compression factor, times height and width each divided by 8. Every position in that grid is a token the transformer attends over, so memory grows linearly with frame count and pixel area, and attention work grows with roughly the square of the token count. A five-second clip at 720p is not a slightly larger image. One 720p frame is a 160 by 90 latent, 14,400 tokens; the clip is 160 by 90 by 21, so 302,400 tokens. That is twenty-one times the sequence length and, on the square, about 440 times the attention work.

The VAE decode at the end is frequently the highest memory point of the run, because it expands the latent back to full-resolution frames. Tiled decoding is what people reach for when a job dies on the last step.

02 The first cost

How much VRAM does a five-second video clip need?

Count the latent tokens first. Assumes an 8× spatial and 4× temporal VAE, 16 fps, and one activation tensor at width 1,536 in FP16.

Clip	Latent grid	Tokens	One activation tensor	vs 480p	Attention work
480p, 5 s832 × 480, 81 frames	104 × 60 × 21	131,040	0.40 GB	1.0×	1.0×
720p, 5 s1280 × 720, 81 frames	160 × 90 × 21	302,400	0.93 GB	2.3×	5.3×
720p, 10 s1280 × 720, 161 frames	160 × 90 × 41	590,400	1.81 GB	4.5×	20×
1080p, 5 s1920 × 1088, 81 frames	240 × 136 × 21	685,440	2.11 GB	5.2×	27×

Attention work is the token ratio squared, which is the shape of the cost, not a measured runtime. Your own numbers depend on the attention implementation, the step count and whether the model uses windowed attention.

Then add the weights

Parameters times bytes per parameter. A 5B model is 10 GB in BF16 and 5 GB in FP8. A 14B model is 28 GB in BF16 and 14 GB in FP8. The text encoder is the third number and the checkpoint decides it: google/t5-v1_1-xxl is 11B parameters whole, 22 GB in BF16, but the encoder tower alone, which is what image and video workflows load, is 4.7B, so 9.4 GB. Either way it is why almost everyone runs it once and frees it before denoising starts.

That arithmetic decides the card. A 14B model in FP8 with the encoder offloaded is 14 GB of weights, which leaves a 32 GB card room for 720p activations and a tiled decode. The same model in BF16 is 28 GB and leaves nothing.

Sizing rule

24 GB: 480p, 5 s, FP8 weights, encoder offloaded
32 GB: 720p, 5 s, FP8, tiled decode
48 GB: 720p at 10 s, or 1080p short, or BF16 on a 5B model
80 GB: 14B in BF16 with no offloading, or a real batch
141 GB+: Long 1080p clips and multi-clip batches in one process

Wan 2.2 and Hunyuan Video ship in several sizes and precisions, and those change between releases. The arithmetic outlives the release notes. Card capacity in the GPU VRAM guide.

03 The cards

Which GPU should you rent for video generation?

Sorted by how often we recommend them for this workload. Per GPU-hour is the monthly price divided by 720.

GPU	VRAM	Bandwidth	Weekly	Monthly	Per GPU-hour	Stock
RTX 5090The pick: 720p clips, FP8, fastest memory per dollar	32 GB GDDR7	1.79 TB/s	$155	$540/mo	$0.75	100
L40SECC memory, FP8, for runs measured in days	48 GB GDDR6 ECC	864 GB/s	$148	$520/mo	$0.72	74
RTX A6000Cheap 48 GB capacity, no FP8	48 GB GDDR6 ECC	768 GB/s	$82	$290/mo	$0.40	65
RTX 6000 Ada48 GB with FP8 and workstation drivers	48 GB GDDR6 ECC	960 GB/s	$130	$460/mo	$0.64	26
RTX 4090480p batches, largest stock in the fleet	24 GB GDDR6X	1.01 TB/s	$110	$390/mo	$0.54	265
RTX 3090Cheapest way to test a pipeline	24 GB GDDR6X	936 GB/s	$65	$230/mo	$0.32	186
H100 SXM80 GB and FP8 when BF16 will not fit	80 GB HBM3	3.35 TB/s	$475	$1,690/mo	$2.35	63
H200 SXM1080p, long clips, batches in one process	141 GB HBM3e	4.8 TB/s	$640	$2,290/mo	$3.18	19

Stock is cards free right now across DFW-1, IAD-1 and PDX-1. Prices are identical in all three regions. Full specifications for all fifteen models on the GPU catalogue.

The RTX 5090 wins this workload on bandwidth per rented dollar. Its 1.79 TB/s is 88% of an A100 SXM's 2.04 TB/s and rents for $540 a month against $990, and sampling reads the same tensors on every one of its fifty-odd steps. The L40S with 48 GB and ECC costs $20 a month less, gives 16 GB more headroom for ten-second clips, and draws 350 W against 575 W. The RTX A6000 at $290 is the cheap way to hold 48 GB when speed is not the constraint. Go to H100 SXM only when the model wants 80 GB or FP8 at HBM speed.

04 The second cost

Why is a monthly term the right shape for video generation?

Because the queue is never empty. One clip takes minutes and a batch of a few thousand runs for days.

Measure one clip on your own pipeline, then multiply. If a 720p clip takes six minutes, one card produces 10 an hour, 240 a day and 7,200 across a 720-hour month. At $540 flat that is $0.075 per clip.

A meter charges for hours and this workload uses all of them. A card generating video is not idle between prompts, it is on the next prompt, so the utilisation that makes hourly billing attractive never arrives. Our RTX 5090 at $540 is $0.75 per GPU-hour and our L40S at $520 is $0.72. A term also cannot be preempted, so a nine-day render queue is never restarted by somebody else's bid.

A week costs $155 on the 5090, which is $0.92 per GPU-hour, a 23% premium. Use it to prove a pipeline, then convert without reprovisioning. Full breakeven arithmetic on monthly versus hourly GPU rental.

RTX 5090, one card	Price	Per GPU-hour
1 week168 hours	$155	$0.92
1 month720 hours	$540	$0.75
3 months prepaid−5%	$1,539	$0.71
6 months prepaid−10%	$2,916	$0.68

Prepaid discounts stack with the multi-GPU discount: 5% at two cards, 8% at four, 10% at eight. Terms on the pricing page.

Renewals are drawn from your balance 24 hours before term end. Fund it once in BTC, ETH, USDT, USDC, XMR, SOL or LTC and a render queue never stops for a payment problem.

05 Around the card

Where does the video output go?

Frames are large, and a run makes them faster than you can move them. Storage and egress are part of this workload, not a footnote.

Local NVMe

500 GB on the RTX 5090, L40S and RTX A6000, 250 GB on the RTX 4090, 2 TB on the H100 SXM and 3 TB per card on the B200, which is sold two cards at a time because it ships on an 8-way baseboard. The 8-way HGX nodes carry 16 TB and the 8× B200 node 24 TB, against 2 to 4 TB on the PCIe nodes. Encrypted per instance with LUKS2.

Extra local NVMe $9/TB/mo, up to 30 TB per node

Network volumes

NVMe-backed, triple-replicated in-region, attachable to several instances at once and kept after the instance is released. Where a finished library lives.

$14/TB/mo, snapshots $6/TB/mo

Outbound bandwidth

20 TB a month included per instance, then $4 per TB, never throttled. A five-second 720p clip at 8 Mbit/s is 5 MB, so 20 TB is about four million clips delivered.

Inbound is unmetered

CPU cores for encoding

12 vCPU and 62 GB of RAM on the RTX 5090, 16 vCPU and 94 GB on the L40S, 20 vCPU and 251 GB on the H100 SXM. Enough to run ffmpeg beside the sampler.

A 720p 81-frame clip is 224 MB as raw RGB

Keep the working set on local NVMe and encode before it leaves the box. Writing PNG sequences to a network volume and pulling them down raw is how a $540 card becomes a bandwidth problem. Volume attachment is covered in the documentation.

We do not look at what you generate. Instances are single-tenant bare metal with root access and no agent in the guest, hosts report only power draw, temperature, fan speed, port bytes and a health probe, and the NVMe key is destroyed when the term ends. What that means when someone asks is set out on no-KYC GPU hosting. See no KYC GPU hosting.

06 Eight at a time

Does video generation scale across multiple GPUs?

Within one clip, badly. Across a queue, perfectly. That distinction decides which node to rent.

Node	Weekly	Monthly	Per GPU-hour	Host	NVMe
8× RTX 5090PCIe Gen5 peer-to-peer	$1,120	$3,890/mo	$0.68	96 vCPU · 496 GB	4 TB
8× L40SPCIe Gen4 peer-to-peer	$1,070	$3,740/mo	$0.65	128 vCPU · 752 GB	4 TB
8× RTX 4090PCIe Gen4 peer-to-peer	$790	$2,810/mo	$0.49	64 vCPU · 376 GB	2 TB
8× H100 SXMNVSwitch · 900 GB/s	$3,420	$12,170/mo	$2.11	160 vCPU · 2 TB	16 TB

Per GPU-hour is the monthly node price divided by 5,760 GPU-hours. A private VLAN is included on 8-way nodes. Node availability by site on the multi-GPU cluster rental page.

For most video pipelines the right pattern is one clip per card and eight in flight, because a PCIe node has no fast path for splitting a single 300,000-token sequence across cards. That is why the 8× RTX 5090 node at $0.68 per GPU-hour makes a better render farm than a $2.11 H100 node. Pipeline parallelism inside one clip, which you need only when the model fits on no single card, wants NVSwitch, and that means the 8× H100 SXM node.

07 Honest limits

When should you not rent a monthly GPU for video?

Three cases where we are the wrong answer, written plainly because you would find out anyway.

If you generate forty clips a week, use an hourly provider. Forty clips at six minutes is four hours of real work, and our shortest term is seven days at $155. That is $38.75 for every hour of GPU time you actually use, against an illustrative $0.60 on a meter. A term earns its price only when the queue is deep.

If 24 GB is enough for what you ship, do not rent 32 GB. The RTX 4090 at $390 or the RTX 3090 at $230 runs 480p work all month, and the difference buys a second card. Sizing up before measuring a run is how people overpay here.

And if you want a managed video API, we do not sell one. You get a bare machine, a driver, a card of your choosing and root. Templates include ComfyUI and PyTorch 2.7 with CUDA 12.8, but the pipeline and the queue are yours to run.

A term is not refunded if the render queue empties on day nine, so size the term to the queue you have rather than the one you plan: the rest of that risk is set out on monthly versus hourly GPU rental.

Take a term if

The queue runs for days, the endpoint answers overnight, you ship enough output that egress is a real line item, or you want to pay in crypto without an identity check. Same reasoning as GPU rental for Stable Diffusion, with a heavier tensor.

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08 Questions

GPU for AI video generation, asked plainly.

What GPU do I need for AI video generation?

For 720p clips of about five seconds, a 32 GB card. Our pick is the RTX 5090 at $540 a month, which is $0.75 per GPU-hour and carries 1.79 TB/s of memory bandwidth. For ten-second clips, 1080p or ECC memory, move to the L40S at $520 with 48 GB. Only go to 80 GB when the weights will not fit in a smaller card.

How much VRAM does a video model need for a 720p clip?

Add four numbers: parameters times bytes per parameter for the weights, the same for the text encoder unless you offload it, a few gigabytes of activations for the latent token count, and the VAE decode peak. A 14B model in FP8 is 14 GB of weights, which fits 32 GB with room for a 720p five-second run and a tiled decode. In BF16 it is 28 GB and does not.

Why does a longer clip cost so much more memory than a longer image batch?

Because frames become tokens in one sequence. A 720p five-second clip at 16 fps is a 160 by 90 by 21 latent grid, which is 302,400 tokens. Ten seconds doubles the temporal axis to 590,400 tokens. Memory rises with the token count and attention work rises with roughly its square, so ten seconds costs about 20 times the attention of a 480p five-second clip.

Is a monthly GPU rental cheaper than hourly for video generation?

For a queue that never empties, yes. An RTX 5090 at $540 flat is $0.75 per GPU-hour across 720 hours, and video pipelines run close to full utilisation because the next prompt is always waiting. Do the card's own arithmetic before believing that: $540 against an illustrative $0.60 an hour on a meter breaks even at 900 hours, which a 720-hour month never reaches. On price alone the meter wins on this card, so what a term buys here is that the queue is never evicted and never waits for capacity. The general breakeven table is on our monthly versus hourly page.

How many clips can one GPU generate in a month?

Measure one clip, then multiply by 720 hours. At six minutes per clip that is 10 an hour, 240 a day and 7,200 a month, which is $0.075 each at $540. At twenty minutes per clip it is 2,160 a month at $0.25 each. The card does not change price, so the per-clip cost is entirely a function of your sampler settings.

How much storage do I need for AI video output?

Every instance includes local NVMe, from 250 GB on the RTX 4090 to 500 GB on the RTX 5090 and L40S, 2 TB on the H100 SXM and 3 TB per card on the B200, which is sold two cards at a time; an 8-way HGX node carries 16 TB and the 8× B200 node 24 TB. Add local NVMe at $9 per TB a month, or a network volume at $14 per TB that survives the instance. Outbound is 20 TB free, then $4 per TB.

Do you inspect the videos I generate?

No, and we hold nothing that would let us. Instances are single-tenant bare metal with root access, no agent runs in your operating system, and the local disk is LUKS2 encrypted with a key held in the host TPM and destroyed on release. Hosts report power, temperature, fan speed, port bytes and a health probe. Nothing else crosses the boundary.

Can I rent a GPU for video generation without KYC?

Yes. An account is a username and a password. No email address, no phone number, no identity document and no card, because payment is in Bitcoin, Ethereum, USDT, USDC, Monero, Solana or Litecoin. Minimum deposit is $20 equivalent and unused balance is withdrawn in the coin you sent. GPU Rent Hub Compute LLC has operated this way since 15 March 2021.

Deposits and confirmation times on how crypto payment works, site latency and carriers on the datacenters page, live fleet state on the status page.

A render queue that starts this afternoon.

100 RTX 5090 and 74 L40S free right now across DFW-1 Dallas, IAD-1 Ashburn and PDX-1 Hillsboro. Root in about ninety seconds, no identity check.

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