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

Best video models to run locally on a 64GB Mac

A leaderboard of the best video models you can run locally on a Mac with 64GB of memory. Wan 2.2 I2V A14B is #1 right now, followed by LTX-2.5 and HunyuanVideo 1.5. Models are ranked by one score that combines 55 published benchmarks. Each model shows the download that fits and the command to run it.

14 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 64GB RAM · Apple Silicon → ~48GB fast · local only · reset
+ more
chip[M1 Max][M1 Ultra][M2 Max][M2 Ultra][M3 Max (16-core CPU / 40-core GPU)][M4 Pro][M4 Max (16-core CPU / 40-core GPU)][M5 Pro][M5 Max (40-core GPU)]

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=64&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
42Wan 2.2 I2V A14B · gguf-q8_0 ×2 experts + umt5 q8_0 (sd.cpp) Alibaba56±2local~45GBhow to run › WorldReasonBench: Human-Aligned Stress Testing of Video Generators as Future World-State Predictors (arXiv v1)CRONOS: Benchmarking Counterfactual Physical Consistency in Video Models (arXiv:2605.23699v1), Table 1PAI-Bench: A Comprehensive Benchmark For Physical AI (arXiv:2512.01989v1)MindWorldBench: Evaluating Mental-State-to-Behavior Reasoning in Image-to-Video Generation (arXiv:2609.39147v1)Cosmos 3: Omnimodal World Models for Physical AI (arXiv:2606.02800v4)VGI-Bench: Probing Visual Intelligence in Video Generation Models+2
53LTX-2.5 · gguf-q8_0 22B dev + Gemma 4 12B q8_0 (sd.cpp) Lightricks53±6*local~48GBhow to run › Kandinsky 6.0 Video: Foundation Models for Synchronized Video and Audio Generation (arXiv:2610.05608v1)Datapoint Video Bench leaderboard (datapointai/text-to-video-human-preferences-326k dataset card)
59HunyuanVideo 1.5 · fp16 720p T2V (sd.cpp) Tencent52±2local~44GBhow to run › LMArena leaderboard datasetCosmos 3: Omnimodal World Models for Physical AI (arXiv:2606.02800v4)WorldJen: An End-to-End Multi-Dimensional Benchmark for Generative Video ModelsVideoArgus: Agentic Rubric-Grounded Unified Evaluation for Video Generation and Editing (arXiv:2608.05485v1)Sci-VBench: Evaluating Knowledge- and Reasoning-Intensive Video Generation in Science DomainsHumanScore: Benchmarking Human Motions in Generated Videos
67Wan 2.2 A14B · gguf-q8_0 ×2 experts + umt5 q8_0 (sd.cpp) Alibaba50±2local~45GBhow to run › LMArena leaderboard datasetCosmos 3: Omnimodal World Models for Physical AI (arXiv:2606.02800v4)WorldJen: An End-to-End Multi-Dimensional Benchmark for Generative Video ModelsVideoVerse: Does Your T2V Generator Have World Model Capability to Synthesize Videos?VideoArgus: Agentic Rubric-Grounded Unified Evaluation for Video Generation and Editing (arXiv:2608.05485v1)
80Wan 2.1 14B · gguf-q8_0 + umt5 q8_0 (sd.cpp) Alibaba47±2local~27GBhow to run › VideoPhy / VideoPhy-2 official repository leaderboardsSkyReels-V2: Infinite-length Film Generative ModelCosmos 3: Omnimodal World Models for Physical AI (arXiv:2606.02800v4)T2VPhysBench: A First-Principles Benchmark for Physical Consistency in Text-to-Video GenerationWan-AI/Wan2.1-T2V-14B model cardAlibaba Wan technical report (arXiv:2503.20314v2), VBench table (own models)
81Wan 2.2 TI2V 5B · fp16 + umt5 q8_0 (sd.cpp) Alibaba47±2local~21GBhow 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
83LTX-2 19B Distilled · Draw Things q6p Lightricks47±8*local~41GBhow to run › Sci-VBench: Evaluating Knowledge- and Reasoning-Intensive Video Generation in Science Domains
99Wan 2.1 T2V 1.3B · fp16 + umt5 q8_0 (sd.cpp) Alibaba40±4local~12GBhow 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
105HunyuanVideo · Draw Things q8p Tencent39±3local~28GBhow to run › VideoPhy / VideoPhy-2 official repository leaderboardsSkyReels-V2: Infinite-length Film Generative ModelVideoVerse: Does Your T2V Generator Have World Model Capability to Synthesize Videos?Training-Free Efficient Video Generation via Dynamic Token Carving
107T2V-Turbo-v2 · fp32 checkpoints (official Gradio demo, MPS) UC Santa Barbara38±5*local~16GBhow to run › T2V-Turbo-v2: Enhancing Video Generation Model Post-Training through Data, Reward, and Conditional Guidance Design (arXiv 2410.05677v3)WorldModelBench: Judging Video Generation Models As World Models
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)
112LTX-2.3 · gguf-ud-q4_k_m 22B dev + Gemma 3 12B q4 (sd.cpp) Lightricks36±3local~34GBhow to run › WorldReasonBench: Human-Aligned Stress Testing of Video Generators as Future World-State Predictors (arXiv v1)MindWorldBench: Evaluating Mental-State-to-Behavior Reasoning in Image-to-Video Generation (arXiv:2609.39147v1)Sci-VBench: Evaluating Knowledge- and Reasoning-Intensive Video Generation in Science DomainsPhyGround: Benchmarking Physical Reasoning in Generative World Models (arXiv:2605.10806v1)
123LTX-Video 0.9 (2B) · openvino-int8 (OpenVINO GenAI, one-time export) Lightricks32±3*local~35GBhow to run › GenAI-Arena (TIGER-Lab) text-to-video leaderboard
—AnimateDiff SD 1.5 v2 motion adapter · fp32 motion module + SD 1.5 (sd.cpp) AnimateDiff authorsno reviewed resultslocal~7.8GBhow to run ›

+ 128 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