Video GenerationCogVideoApache-2.0

CogVideoX-5b

THUDM/CogVideoX-5b

Zhipu CogVideoX 5B, video generation on 24GB GPUs.

Parameters

5B

Context

32K

Downloads

360K

Likes

1.8K

Architecture

cogvideox

View on HuggingFace

GPU Requirements

VRAM / RAM / disk estimates by quantization and usage scenario

QuantizationMinimumRecommendedProduction
FP1616bit
23.9 GB
RAM 35.9 · Disk 53.5
28.5 GB
RAM 42.7 · Disk 79.5
36.9 GB
RAM 55.4 · Disk 118.5
FP88bit
14.7 GB
RAM 32.0 · Disk 46.3
18.8 GB
RAM 32.0 · Disk 72.3
26.9 GB
RAM 40.3 · Disk 111.3
INT88bit
14.7 GB
RAM 32.0 · Disk 46.3
18.8 GB
RAM 32.0 · Disk 72.3
26.9 GB
RAM 40.3 · Disk 111.3
AWQ4bit
10.3 GB
RAM 32.0 · Disk 42.8
14.2 GB
RAM 32.0 · Disk 68.8
22.0 GB
RAM 33.0 · Disk 107.8
GPTQ4bit
10.3 GB
RAM 32.0 · Disk 42.8
14.3 GB
RAM 32.0 · Disk 68.8
22.1 GB
RAM 33.1 · Disk 107.8
GGUF4bit
10.5 GB
RAM 32.0 · Disk 42.9
14.4 GB
RAM 32.0 · Disk 68.9
22.2 GB
RAM 33.3 · Disk 107.9

GPU Recommendations

Best ValueBEST

1× RTX 5090

32 GB VRAM

$257/mo

≈ 67.7 video-sec/GPU-h

Effective production throughput ≈ 40.6 video-sec/GPU-h

score 0.6158

Deploy on vast ↗
Cheapest

1× RTX 4070S Ti

16 GB VRAM

$49.5/mo

≈ 648 video-sec/GPU-h

Effective production throughput ≈ 388.8 video-sec/GPU-h

score 0.6523

Deploy on vast ↗
Performance

8× H100 SXM

640 GB VRAM

$9,362/mo

≈ 52.3 video-sec/GPU-h

Effective production throughput ≈ 31.4 video-sec/GPU-h

score 0.2415

Deploy on vast ↗
Min Complexity

1× RTX 3090

24 GB VRAM

$76.5/mo

≈ 28.5 video-sec/GPU-h

Effective production throughput ≈ 17.1 video-sec/GPU-h

score 0.6529

Deploy on vast ↗

💬 Community — real deployment experience

0 Articles · 0 Benchmarks

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GPUs that fit (single card)

RTX 4070S Ti

16 GB VRAM

$49.5

/mo · vast

RTX 4080S

16 GB VRAM

$50.0

/mo · vast

RTX 3090

24 GB VRAM

$76.5

/mo · vast

RTX 5070 Ti

16 GB VRAM

$86.3

/mo · vast

Tesla V100

16 GB VRAM

$88.4

/mo · vast

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