modelfit/modelfit-hardware-dataset

ModelFit: Local LLM Hardware Compatibility Dataset

An open dataset of which local AI models (Ollama) fit which hardware, by parameter size, quantization, minimum RAM, and estimated memory load, across Apple Silicon Macs, iPhones, and NVIDIA GPUs.

Maintained by [ModelFit](https://modelfit.io/). Browse it as an interactive table at [modelfit.io/data](https://modelfit.io/data/); the canonical machine-readable source is [modelfit.io/api/dataset](https://modelfit.io/api/dataset/).

• —141 models across 24 families (106 with a registry-verified local build, 35 cloud-only APIs tracked for comparison)
• —License: CC BY 4.0: reuse freely, including commercially, with attribution
• —Updated: see the updated field in models.json (regenerated from the live endpoint)

from datasets import load_dataset

ds = load_dataset("modelfit/modelfit-hardware-dataset", split="train")
# which models fit 16 GB of unified memory?
ds.filter(lambda r: r["runsLocally"] and r["minRamGb"] <= 16).to_pandas()

Files

Columns

• —model: display name (e.g. Qwen3.5 9B Instruct)
• —family: model family (Qwen, Llama, Gemma, DeepSeek, …)
• —params: parameter count, in billions (null when the vendor does not disclose it, e.g. closed API models)
• —quantization: e.g. Q4_K_M
• —minRamGb: minimum unified memory / VRAM to load it
• —estimatedLoadGb: approximate memory footprint at this quantization
• —kvKbPerToken: exact fp16 KV-cache cost in KB per token for hybrid linear-attention models (Qwen3.5/3.6, Qwen3-Next), computed from the published HF config (full-attention layers × kvheads × headdim × 2 × 2 bytes; only full-attention layers cache KV). null for standard GQA models, whose KV is estimated by size class. Example: Qwen3.6 35B-A3B is 20 KB/token, so a full 262k-token fp16 cache is ~5 GB
• —runsLocally: true when a registry-verified Ollama build fits at least one consumer RAM tier tracked here (up to 256GB)
• —openWeights: true when the weights are publicly downloadable. Can be true while runsLocally is false: open-weight giants like NVIDIA Nemotron 3 Ultra (550B, ~190GB at 2-bit) or Kimi K2 exceed every consumer tier
• —ggufDiy: true when the weights are open and a ~Q4 GGUF (0.6 GB per billion parameters) fits a 256GB-class machine via llama.cpp, but no Ollama build exists (e.g. DeepSeek V4 Flash 284B, Xiaomi MiMo-V2-Flash 309B): runnable DIY, not scored for local fit
• —runtimes: apps the model runs in (ollama, llama.cpp, lm-studio; pipe-separated in the CSV, empty for cloud rows)
• —bestFor: primary workloads
• —ollamaCommand: exact ollama run … command (local models)

Methodology

A model fits a device when its estimatedLoadGb is within the memory budget: ~70% of unified memory on machines up to 32GB, scaling linearly to ~85% at 128GB and above (high-RAM Macs can wire more memory to the GPU via iogpu.wired_limit_mb; the rest goes to the OS, context, and KV-cache). At Q4_K_M, a model needs roughly 0.6 GB per billion parameters. Memory-load and tokens/sec figures are estimates, not measured benchmarks. Local model tags are verified against the Ollama registry. Full estimate policy: modelfit.io/about.

Intended use and limitations

• —Built to answer "does this model run on this machine?" for consumer hardware (Apple Silicon, iPhone, NVIDIA consumer GPUs).
• —LLM inference speed, real-world context limits, and quality are not in this dataset.
• —Consumer tiers only: no datacenter SKUs or multi-GPU rigs are scored here.

Updating

node generate.mjs   # pulls the latest from https://modelfit.io/api/dataset/

License & attribution

Released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). You may share and adapt the data, including commercially, with attribution:

Data: ModelFit Local LLM Hardware Compatibility Dataset (modelfit.io), CC BY 4.0.

Cite

ModelFit: Local LLM Hardware Compatibility Dataset.
https://modelfit.io/data/

Built and maintained by ModelFit. Find the best local AI model for your Mac, iPhone, or GPU. Mirrors: GitHub · Hugging Face.