name string | description string | source string | license string | attribution string | methodology dict | updated timestamp[s] | counts dict | models list |
|---|---|---|---|---|---|---|---|---|
ModelFit Local LLM Hardware Compatibility Dataset | Which local AI models (Ollama) fit which hardware, by parameter size, quantization, and estimated memory load. tok/s and load figures are estimates, not measured benchmarks. | https://modelfit.io/data/ | CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/) | ModelFit: https://modelfit.io/ | {
"ramBudgetRatioBase": 0.7,
"ramBudgetRatioMax": 0.85,
"note": "A model fits a device when its estimatedLoadGb is within the memory budget: ~70% of unified memory up to 32GB, scaling linearly to ~85% at 128GB and above (high-RAM Macs can wire more memory to the GPU). Q4_K_M needs roughly 0.6 GB per billion param... | 2026-07-05T00:00:00 | {
"models": 107,
"local": 75,
"cloud": 32,
"families": 21
} | [
{
"model": "Qwen2.5 1.5B Instruct",
"family": "Qwen",
"params": 1.5,
"quantization": "Q4_K_M",
"minRamGb": 4,
"estimatedLoadGb": 1.5,
"runsLocally": true,
"openWeights": true,
"ggufDiy": false,
"runtimes": [
"ollama",
"llama.cpp",
"lm-studio"
],
"bes... |
ModelFit Local LLM Hardware Compatibility Dataset
Which local AI models fit which hardware. Maps 107 LLMs (75 of them local-capable via Ollama, llama.cpp or LM Studio) to RAM/VRAM requirements at Q4_K_M quantization, so you can look up "will this model run on my machine" without guessing.
Source of truth: modelfit.io/data. This dataset is a mirror of the live JSON export at modelfit.io/api/dataset, refreshed from the same GitHub repo that generates it: github.com/Wecko-ai/modelfit-hardware-dataset (CSV + JSON, self-updating).
What it is
One row per model. Each row has the model's parameter count (when disclosed; closed API models like Claude/GPT/Gemini/Grok are marked null, vendors don't publish those numbers), its quantization, the minimum RAM/VRAM to run it, an estimated memory load in GB, whether it runs locally at all, which runtimes support it, what it's best suited for, and the exact ollama pull command if it's on the Ollama registry.
Columns
| column | type | meaning |
|---|---|---|
model |
string | model name |
family |
string | model family (e.g. Llama, Qwen, Gemma) |
params |
number or null | parameter count in billions; null means vendor-undisclosed (closed API models) |
quantization |
string | quantization level the sizing is based on (mostly Q4_K_M, some Q8_0 siblings) |
minRamGb |
number | minimum RAM/VRAM in GB to run it |
estimatedLoadGb |
number | estimated memory footprint once loaded |
runsLocally |
boolean | true when a registry-verified Ollama build fits a consumer RAM tier tracked here (up to 256GB) |
openWeights |
boolean | true when the weights are publicly downloadable; can be true while runsLocally is false (open-weight giants like Nemotron 3 Ultra, 550B ~190GB at 2-bit, exceed every consumer tier) |
ggufDiy |
boolean | 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 |
string | pipe-separated runtimes that can run it (ollama, llama.cpp, lm-studio) |
bestFor |
string | short use-case tag |
ollamaCommand |
string or null | the exact registry-verified ollama pull tag, if one exists |
The 0.6 GB per billion rule
At Q4_K_M quantization, a local LLM needs roughly 0.6 GB of memory per billion parameters. ModelFit sizes its "fits" recommendation to a tiered memory budget: about 70% of unified memory up to 32 GB, scaling linearly to about 85% at 128 GB and above (high-RAM Macs can wire more memory to the GPU). The rest is headroom for the OS, the context window, and KV-cache. An 8B model needs about 5 GB loaded, comfortable on a 16 GB machine. A 70B model needs about 42 GB, so 64 GB minimum with real headroom at 96 GB or more.
Links
- Live site and interactive matrix: modelfit.io/data
- JSON API (same data, machine-readable, CORS open): modelfit.io/api/dataset
- Source repo (generator script + CSV/JSON): github.com/Wecko-ai/modelfit-hardware-dataset
License
CC BY 4.0. Attribution: "ModelFit, https://modelfit.io".
Caveats
Estimated memory load figures are estimates derived from parameter count and quantization, not measured benchmarks. Treat them as a starting point, not a guarantee; actual usage varies with context length, backend, and OS overhead.
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