Everything this chip runs, machine by machine: MacBook Pro 14" M5 Pro, MacBook Pro 16" M5 Pro
Showing 3641 of 3641 models
Multimodal · Alibaba · 2026-02-28
Multimodal · Alibaba · 2026-02-28
Multimodal · Alibaba · 2026-02-28
Multimodal · Alibaba · 2026-02-28
Multimodal · Alibaba · 2026-02-27
Multimodal · Alibaba · 2026-02-27
General · Liquid AI · 2025-11-28
General · Liquid AI · 2025-11-28
Reasoning · Liquid AI · 2025-11-28
General · Liquid AI · 2025-11-28
General · Liquid AI · 2025-11-28
General · Liquid AI · 2025-11-28
General · Liquid AI · 2025-11-28
General · Liquid AI · 2025-11-28
General · Liquid AI · 2025-11-28
General · Liquid AI · 2025-11-28
General · Liquid AI · 2025-11-28
General · Liquid AI · 2025-11-28
General · Liquid AI · 2025-11-28
General · Liquid AI · 2025-11-28
General · Liquid AI · 2025-11-28
Reasoning · Liquid AI · 2025-11-28
General · ibm-granite · 2025-09-16
Chat · Liquid AI · 2025-11-28
Chat · Liquid AI · 2025-11-28
Chat · Liquid AI · 2025-11-28
General · NCAI · 2025-12-29
Reasoning · HuggingFace · 2025-07-08
General · ibm-granite · 2025-09-16
General · Liquid AI · 2025-11-28
General · Liquid AI · 2025-11-28
Multimodal · NCAI · 2025-12-29
Multimodal · Google · 2025-07-30
Multimodal · Google · 2025-06-25
Multimodal · Liquid AI · 2025-11-28
Multimodal · Liquid AI · 2025-11-28
Multimodal · Liquid AI · 2025-11-28
Multimodal · Liquid AI · 2025-11-28
Reasoning · jackrong · 2026-03-16
General · lgai-exaone · 2025-03-12
General · LG AI · 2025-07-15
General · raidium · 2026-06-15
Embedding · taide · 2026-06-12
General · Alibaba · 2025-04-27
General · Alibaba · 2025-04-27
Multimodal · zai-org
Multimodal · Alibaba
General · Alibaba
General · Alibaba
Multimodal · openbmb
Reasoning · DeepSeek
Multimodal · rednote-hilab
Multimodal · datalab-to
General · openbmb
Multimodal · rednote-hilab
General · hmellor
General · distil-labs
General · farbodtavakkoli
General · Liquid AI
General · ibm-granite
General · Google
Multimodal · lkhl
General · jinaai
General · Alibaba
Reasoning · typhoon-ai
General · Upstage
General · openai
General · paddlepaddle
General · Liquid AI
General · pfnet
General · openbmb
General · baidu
General · amd
General · bytedance-seed
General · arcee-ai
General · adamlucek
Coding · shahriarferdoush
General · ahczhg
General · abaryan
General · etherll
General · farbodtavakkoli
Reasoning · khazarai
General · kamilamila
General · getonit
General · paddlepaddle
General · agentica-org
General · novaciano
General · dmusingu
General · kgrabko
General · ordenwills
General · smcleish
General · carsenk
Reasoning · nvidia
General · zero-point-ai
General · ibm-granite
General · openbmb
General · ibm-granite
General · osaurusai
General · pyoakum
General · ibm-granite
General · treadon
General · tencent
General · skis-ai-research
General · menlo
General · thkim0305
General · magistrtheone
Reasoning · jackrong
General · huihui-ai
Coding · rahul7star
General · inclusionai
General · TII
Coding · z-lab
General · osaurusai
General · artificialguybr
General · Microsoft
General · roystar
General · weiboai
General · Microsoft
Reasoning · ai21labs
General · TII
General · tencent
General · primeintellect
General · Alibaba · 2025-04-27
Multimodal · Alibaba · 2026-02-27
Multimodal · Alibaba
Multimodal · datalab-to
Multimodal · Microsoft
General · ibm-granite
Multimodal · nanonets
Multimodal · Alibaba · 2026-02-26
General · nanonets
Multimodal · ibm-granite
Multimodal · typhoon-ai
General · internlm
Multimodal · goekdeniz-guelmez
General · kristaller486
General · zstanjj
General · ibm-granite
General · weiboai
General · x-izhang
General · bytedance
General · ibm-granite
General · bytedance
General · stanford-oval
General · flowaicom
General · bllossom
General · opengvlab
General · z-lab
General · bytedance
General · radheneev
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It depends on the model size and quantization. A 7B parameter model at Q4 quantization needs about 5 GB of RAM, while a 70B model needs 40+ GB. Apple Silicon Macs use unified memory, so your entire RAM pool is available for model weights — no separate VRAM required.
Not comfortably. A 70B model at Q4 quantization needs about 40 GB of RAM. The MacBook Air maxes out at 24-32 GB depending on the generation. You'd need a Mac Studio or MacBook Pro with 48+ GB for a 70B model to run well.
Speed depends on your chip's memory bandwidth and the model size. Smaller models (3-7B) run fastest — expect 40-70+ tokens per second on M2 Pro or better. Use the calculator above to see estimated speeds for your specific Mac.
Quantization reduces model precision to use less memory. Q8 (8-bit) is nearly lossless. Q4 (4-bit) reduces memory by ~75% with minor quality loss — it's the sweet spot for most users. Q2 (2-bit) saves the most memory but noticeably degrades output quality.
Apple Silicon uses unified memory — CPU and GPU share the same RAM pool. A Mac with 32 GB can load a 28 GB model directly. On NVIDIA systems, you're limited by GPU VRAM (typically 8-24 GB on consumer cards), even if the PC has 64 GB of system RAM.
ToolPiper uses Metal GPU acceleration via llama.cpp for LLM inference on Apple Silicon. The GPU and CPU share unified memory, so there's no data transfer overhead. The Neural Engine (ANE) is used for specific tasks like super-resolution and pose detection.
Yes, if you have enough RAM. ToolPiper manages model loading and can keep multiple models in memory simultaneously. When memory gets tight, it automatically evicts the least recently used model to make room for a new one.
GGUF is the standard format for running quantized models with llama.cpp (and ToolPiper). It supports all quantization levels and runs on CPU+GPU. MLX is Apple's format optimized for Apple Silicon. AWQ and GPTQ are NVIDIA-focused formats that don't run natively on Mac.
Model database updated: 2026-07-24 · 3641 models