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

Best video models to run locally on a 16GB Mac

A leaderboard of the best 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 by one score that combines 55 published benchmarks. Each model shows the download that fits and the command to run it.

4 models · scores combine 55 benchmarks · updated how it works · find a leaderboard · benchmark your machine
task[all tasks][text to video][image to video][video editing]
Models for 16GB RAM · Apple Silicon → ~11GB fast · local only · reset
+ more
chip[M1][M1 Pro][M2][M2 Pro][M3][M4][M5][M6 (16GB)]

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

#modelscore↓where↓speed↓RAM↓get itsources
#modelscore↓where↓speed↓RAM↓get itsources
81Wan 2.2 TI2V 5B · gguf-q4_k_m + umt5 q4_k_m (sd.cpp) Alibaba47±2local~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)Target-Bench: Can Video World Models Achieve Mapless Path Planning with Semantic Targets?VideoArgus: Agentic Rubric-Grounded Unified Evaluation for Video Generation and Editing (arXiv:2608.05485v1)Sci-VBench: Evaluating Knowledge- and Reasoning-Intensive Video Generation in Science DomainsPAI-Bench: A Comprehensive Benchmark For Physical AI (arXiv:2512.01989v1)+3
99Wan 2.1 T2V 1.3B · fp16 + umt5 q4_k_m (sd.cpp) Alibaba40±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
109T2V-Turbo (VC2) · fp32 checkpoints (official Gradio demo, MPS) UC Santa Barbara38±3†local~9.9GBhow to run › GenAI-Arena (TIGER-Lab) text-to-video leaderboardT2V-Turbo: Breaking the Quality Bottleneck of Video Consistency Model with Mixed Reward Feedback (arXiv:2405.18750v2)T2V-Turbo-v2: Enhancing Video Generation Model Post-Training through Data, Reward, and Conditional Guidance Design (arXiv 2410.05677v3)
—AnimateDiff SD 1.5 v2 motion adapter · fp32 motion module + SD 1.5 (sd.cpp) AnimateDiff authorsno reviewed resultslocal~7.8GBhow to run ›

+ 138 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