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

Best text-to-video models to run locally on a 16GB Mac

A leaderboard of the best text-to-video models you can run locally on a Mac with 16GB of memory. Wan 2.2 TI2V 5B is #1 right now, followed by Wan 2.1 T2V 1.3B and T2V-Turbo (VC2). Models are ranked on human votes and video generation tests. Each model shows the download that fits and the command to run it.

3 models · scores combine 25 benchmarks · updated how it works · find a leaderboard · benchmark your machine
task[all tasks][text to video][image to video][video editing]

Scores use reviewed text to video evaluations only. Compare scores within this task. Models without task measurements are excluded.

Models for 16GB RAM · Apple Silicon → ~11GB fast · local only · reset
+ more
chip[M1][M1 Pro][M2][M2 Pro][M3][M4][M5][M6 (16GB)]
set[all sets][LMArena video · 94][VABench T2AV micro Visual QA · 3]

Filter the model list while keeping task scores. Models without a shared score scale are not yet calibrated; select a set to see its own scores.

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

detecting your OS…

Run curl "whatmodel.sh/video?ram=16&gpu=apple&max=0"to get this list in your terminal (add &format=json for agents).

ram[8][16][24][32][48][64][96][128][192][256]GB
gpu[apple silicon][graphics card][none]
price[local][$0.02][$0.05][$0.1][$0.25][any]max per sec

detecting your OS…

Score vs memory to run

#modeltask score↓where↓speed↓RAM↓get itsources
#modeltask score↓where↓speed↓RAM↓get itsources
52Wan 2.2 TI2V 5B · gguf-q4_k_m + umt5 q4_k_m (sd.cpp) Alibaba47±4local~11GBhow to run › Cosmos 3: Omnimodal World Models for Physical AI (arXiv:2606.02800v4)VABench: A Comprehensive Benchmark for Audio-Video Generation (arXiv:2512.09299v2)Sci-VBench: Evaluating Knowledge- and Reasoning-Intensive Video Generation in Science Domains
61Wan 2.1 T2V 1.3B · fp16 + umt5 q4_k_m (sd.cpp) Alibaba43±4local~8.6GBhow to run › WorldJen: An End-to-End Multi-Dimensional Benchmark for Generative Video ModelsAlibaba Wan technical report (arXiv:2503.20314v2), VBench table (own models)Training-Free Efficient Video Generation via Dynamic Token Carving
68T2V-Turbo (VC2) · fp32 checkpoints (official Gradio demo, MPS) UC Santa Barbara41±3†local~9.9GBhow to run › GenAI-Arena (TIGER-Lab) text-to-video leaderboardT2V-Turbo-v2: Enhancing Video Generation Model Post-Training through Data, Reward, and Conditional Guidance Design (arXiv 2410.05677v3)T2V-Turbo: Breaking the Quality Bottleneck of Video Consistency Model with Mixed Reward Feedback (arXiv:2405.18750v2)

+ 94 more don't fit your machine or budget · show them

97 of 97 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