whatmodel.sh · find the best video models you can run

Best video models to run locally on a 1GB graphics card

A leaderboard of the best video models you can run locally on a 1GB graphics card (with 32GB of system RAM). LTX-2.5 is #1 right now, followed by HunyuanVideo 1.5 and Wan 2.1 14B. Models are ranked by one score that combines 55 published benchmarks. Each model shows the download that fits and the command to run it.

27 models · scores combine 55 benchmarks · updated 2026-10-11 02:20 UTChow it works · find a leaderboard · benchmark your machine

task[all tasks][text to video][image to video][video editing]

Models for 32GB RAM · 1GB graphics card → ~1GB fast · local only · reset

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Detect your exact chip or graphics card:

curl -G whatmodel.sh/video -d max=0 --data-urlencode "mac=$(sysctl -n hw.memsize machdep.cpu.brand_string)"Mac

curl -G whatmodel.sh/video -d max=0 --data-urlencode "ram=$(awk '/MemT/{print $2}' /proc/meminfo)" --data-urlencode "nv=$(nvidia-smi --query-gpu=name,memory.total --format=csv,noheader 2>/dev/null)"Linux

curl.exe -G whatmodel.sh/video -d max=0 --data-urlencode "ram=$((gcim Win32_ComputerSystem).TotalPhysicalMemory)" --data-urlencode "nv=$(nvidia-smi --query-gpu=name,memory.total --format=csv,noheader 2>$null)"Windows

$ terminal

ram[8][16][24][32][48][64][96][128][192][256]GB

gpu[apple silicon][graphics card][none]

vram[1][2][4][8][12][16][24][32][48][64][80][96][128][160]GB

price[local][$0.02][$0.05][$0.1][$0.25][any]max per sec

detecting your OS…

Score vs memory to run

     │                                                           Wan 2.2 TI2V 5B ● LTX-2.5                ∘
     │   ·                                 Pyramid Flow (SD3)   ●───────────∘────┘                        ∘
  40 ┤ · ●──────────────────────●─────────●───────∘─────────────┘· · · · · · · · · · · · · · · · · · · · · · ·
     │   ∘                                ∘       ∘      ∘                       ∘                        ·
     │   ∘            ∘                   ∘                                           ∘
     └───┬───────────────────────────────────────────────┬────────────────────────────────────────────────┬───
        5GB                                            10GB                                             20GB

#	model	score↓	where↓	speed↓	RAM↓	get it	sources

#	model	score↓	where↓	RAM↓	get it	sources
53	LTX-2.5 · fp8 Lightricks	53±6*	local (slow)	~14GB	how to run ›
59	HunyuanVideo 1.5 · bf16 Tencent	52±2	local (slow)	~20GB	how to run ›
80	Wan 2.1 14B · gguf-q4_k_m + umt5 q8_0 (sd.cpp) Alibaba	47±2	local (slow)	~20GB	how to run ›
81	Wan 2.2 TI2V 5B · fp16 + umt5 q8_0 (sd.cpp) Alibaba	47±2	local (slow)	~21GB	how to run ›	+3
85	CogVideoX-5B-I2V · bf16 Zhipu AI (THUDM)	46±4	local (slow)	~13GB	how to run ›
91	CogVideoX-5B · bf16 Zhipu AI (THUDM)	44±2	local (slow)	~13GB	how to run ›
96	Pyramid Flow (SD3) · bf16 Peking University / Kuaishou	42±3*	local (slow)	~8GB	how to run ›
99	Wan 2.1 T2V 1.3B · fp16 + umt5 q8_0 (sd.cpp) Alibaba	40±4	local (slow)	~12GB	how to run ›
100	Mochi 1 · default Genmo	40±2	local (slow)	~6.5GB	how to run ›
107	T2V-Turbo-v2 · fp32 UC Santa Barbara	38±5*	local (slow)	~9.4GB	how to run ›
109	T2V-Turbo (VC2) · fp32 UC Santa Barbara	38±3†	local (slow)	~9.4GB	how to run ›
114	CogVideoX-2B · fp16 Zhipu AI (THUDM)	36±2	local (slow)	~5.3GB	how to run ›
116	Allegro · bf16 Rhymes AI	35±2	local (slow)	~9.3GB	how to run ›
118	VideoCrafter2 · fp32 Tencent AI Lab	34±2	local (slow)	~9.4GB	how to run ›
121	Open-Sora 1.1 · bf16 HPC-AI Tech	32±7*	local (slow)	~14GB	how to run ›
122	LaVie (base T2V) · fp16 Shanghai AI Lab (Vchitect)	32±2	local (slow)	~4.9GB	how to run ›
123	LTX-Video 0.9 (2B) · bf16 Lightricks	32±3*	local (slow)	~5.3GB	how to run ›
125	NOVA-0.6B T2V · fp16 BAAI	29±3*	local (slow)	~9.3GB	how to run ›
126	ZeroScope v2 576w · fp16 cerspense	27±3	local (slow)	~7.9GB	how to run ›
127	Hotshot-XL · fp16 Hotshot	27±2	local (slow)	~10GB	how to run ›
133	VideoCrafter1 T2V · official script (checkpoint as stored) Tencent AI Lab / HKUST	26±2	local (slow)	~8.1GB	how to run ›
135	LVDM (text-to-video) · fp32 HKUST / Tencent AI Lab	25±3*	local (slow)	~6.1GB	how to run ›
136	ModelScope T2V 1.7B · fp16 Alibaba DAMO Academy	25±2	local (slow)	~4.6GB	how to run ›
138	ZeroScope v2 XL (576w → XL pipeline) · fp16 + model CPU offload (576w base, then XL vid2vid) cerspense	24±2	local (slow)	~15GB	how to run ›
—	AnimateDiff SD 1.5 v2 motion adapter · fp32 motion module + SD 1.5 (sd.cpp) AnimateDiff authors	no reviewed results	local (slow)	~7.8GB	how to run ›
—	Stable Video Diffusion (SVD, 14 frames) · fp16 · CPU offload Stability AI	no reviewed results	local (slow)	~8GB	how to run ›
—	Stable Video Diffusion XT (25 frames) · fp16 · CPU offload Stability AI	no reviewed results	local (slow)	~8GB	how to run ›

+ 115 more don't fit your machine or budget · show them

142 of 142 catalogue models listed here · sources and licences

* new or thinly tested: ranked on too few benchmark results · † only the maker’s own numbers so far · ~RAM is estimated; full context may need more memory