Dataset card

README.md

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+---
+license: cc-by-4.0
+language:
+  - en
+pretty_name: ModelFit Local LLM Hardware Compatibility Dataset
+size_categories:
+  - n<1K
+task_categories:
+  - other
+tags:
+  - local-llm
+  - ollama
+  - llama-cpp
+  - quantization
+  - gguf
+  - vram
+  - unified-memory
+  - apple-silicon
+  - nvidia
+  - hardware-compatibility
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: models.csv
+---
+
+# 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)
+
+```python
+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
+
+| File | What it is |
+|------|------------|
+| [`models.csv`](models.csv) | Flat one-row-per-model table (the loadable split) |
+| [`models.json`](models.json) | Full export with metadata, methodology, and counts |
+| `generate.mjs` | Regenerates both files from the live endpoint |
+| `CITATION.cff` | Machine-readable citation |
+
+## 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 × kv_heads × head_dim × 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](https://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
+
+```bash
+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](https://modelfit.io/data/) (modelfit.io), CC BY 4.0.
+
+## Cite
+
+```
+ModelFit — Local LLM Hardware Compatibility Dataset.
+https://modelfit.io/data/
+```
+
+---
+
+Built and maintained by [ModelFit](https://modelfit.io/) — find the best local
+AI model for your Mac, iPhone, or GPU. Mirrors:
+[GitHub](https://github.com/modelfit/modelfit-hardware-dataset) ·
+[Hugging Face](https://huggingface.co/datasets/modelfit/modelfit-hardware-dataset).