Homebench – Benchmark local LLMs for speed, memory, and quality
# homebench
**Benchmark the local LLMs you already have — speed, memory, *and* quality — as a live terminal leaderboard.**
[](https://github.com/david-g-3654/homebench/actions/workflows/ci.yml) [](https://pypi.org/project/homebench/)  
`homebench` is a single-command TUI that discovers the models installed in your local runner (**Ollama**, **LM Studio**, **llama.cpp**, **vLLM**, or any **OpenAI-compatible** server), runs a curated quality suite, measures **tokens/sec**, **time-to-first-token**, and **memory footprint** on *your actual machine*, and renders a live comparison leaderboard.
```bash pip install homebench homebench ```
That's it. No config, no API keys, no cloud.
---
## Why
There are great tools for *one* half of this problem, but nothing local-first that does both:
- [`llama-bench`](https://github.com/ggml-org/llama.cpp) (inside llama.cpp) measures **speed only**. - [`lm-evaluation-harness`](https://github.com/EleutherAI/lm-evaluation-harness) measures **quality** but has no polished laptop UX and isn't built around the model runners most people actually use locally.
`homebench` fills the gap: **local-first, zero-config, UX-driven.** Clone-and-run, point it at the models you already pulled, and get an at-a-glance answer to *"which of my local models is actually good, and how fast is it on this laptop?"*
## What it measures
| Metric | How | | --- | --- | | **tok/s** | Output tokens ÷ generation time. Ollama reports server-side eval timing; OpenAI-compatible backends are timed client-side from the token stream. Excludes prompt processing and model load. | | **TTFT** | Wall-clock time to the first streamed token (minus model-load time where the runner reports it). | | **Memory** | Resident model size when the runner exposes it (Ollama `/api/ps`, LM Studio `/api/v0`), plus a best-effort peak-RSS sample of the backend's processes. | | **Quality** | 31 deterministically-graded tasks across math, reasoning, factual recall, instruction-following/structured-output, extraction, and code understanding. Optional **LLM-as-judge** adds open-ended tasks (summaries, email, haiku, explanations). |
## Install
```bash pip install homebench # then run: homebench ```
Prefer an isolated install? Use [pipx](https://pipx.pypa.io):
```bash pipx install homebench ```
Or from source:
```bash git clone https://github.com/david-g-3654/homebench cd homebench pip install . ```
Requires **Python 3.9+**.
## Usage
```bash homebench # fast default: 3 smallest models, quick suite (TUI) homebench --all # benchmark every discovered model homebench --full # run the full quality suite (not just the fast subset) homebench --no-tui # plain live renderer (great for piping / CI) homebench -m llama3.2,qwen3:8b # only these models homebench --limit 3 # cap the number of models homebench --provider lmstudio # use LM Studio instead of auto-detect homebench --provider llamacpp # llama.cpp server (llama-server) homebench --provider vllm # vLLM homebench --provider openai --host http://localhost:5000 # any OpenAI-compatible server homebench --refresh-cache # recompute instead of reusing cached responses homebench --no-quality # speed + memory only (fast) homebench --no-speed # quality only homebench --judge qwen3:8b # enable LLM-as-judge (adds open-ended tasks) homebench --tasks mypack.yaml # use a custom task pack instead of the built-in suite homebench --add-tasks mypack.yaml # add a pack on top of the built-in suite homebench --label "before tuning" # tag this run for later diffing homebench --md results.md # also export a Markdown report homebench --json results.json # also export raw JSON
homebench list # just list discovered models homebench tasks # show the quality suite (add --tasks to preview a pack) homebench history # list past runs (saved automatically) homebench diff # diff the two most recent runs homebench diff 3 1 # diff run #3 (base) against run #1 (newer) homebench throughput # batch-throughput sweep (concurrency 1,2,4,8) homebench throughput --concurrency 1,8,16 --provider vllm homebench fit # which popular models fit YOUR hardware? ```
Run `homebench --help` for the full flag list.
### Example output
A real quick-suite run on an Apple M1 (16 GB), via Ollama:
``` Final leaderboard ┏━━━┳━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━┳━━━━━━┳━━━━━━━┳━━━━━━━━┳━━━━━━━━┓ ┃ # ┃ Model ┃ Params ┃ Quality ┃ Pass ┃ tok/s ┃ TTFT ┃ Memory ┃ ┡━━━╇━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━╇━━━━━━╇━━━━━━━╇━━━━━━━━╇━━━━━━━━┩ │ 1 │ llama3.2:latest │ 3.2B │ 75% │ 6/8 │ 16.8 │ 545 ms │ 2.4 GB │ │ 2 │ alibayram/smollm3 │ 3.1B │ 38% │ 3/8 │ 16.9 │ 829 ms │ 2.1 GB │ └───┴──────────────────────┴────────┴─────────┴──────┴───────┴────────┴────────┘ ```
(Numbers are for *that* laptop at *that* moment — see [Limitations](#limitations).)
## Providers
At least one local model runner must be reachable:
| Provider | `--provider` | Default host | Host env var | Notes | | --- | --- | --- | --- | --- | | Ollama | `ollama` | `http://localhost:11434` | `OLLAMA_HOST` | Native API; reports model memory via `/api/ps`. | | LM Studio | `lmstudio` | `http://localhost:1234` | `LMSTUDIO_HOST` | Enriches metadata + memory via native `/api/v0`. | | llama.cpp | `llamacpp` | `http://localhost:8080` | `LLAMACPP_HOST` | `llama-server`, OpenAI-compatible. | | vLLM | `vllm` | `http://localhost:8000` | `VLLM_HOST` | Set `VLLM_API_KEY` if started with `--api-key`. | | OpenAI-compatible | `openai` | — | `OPENAI_BASE_URL` | Any `/v1` server (Jan, LocalAI, TGI, …); pass `--host`. |
Auto-detection tries Ollama → LM Studio → llama.cpp → vLLM (the generic `openai` provider is explicit-only). Force one with `--provider`. Override host with `--host` or the env var above.
## How quality grading works
The suite is small on purpose — enough tasks across categories to *separate* models, few enough that every model runs in a couple of minutes on a laptop. Each task is graded deterministically (exact numeric match, multiple-choice letter, substring, valid-JSON, regex). Temperature is 0 and a fixed seed is used for reproducibility. See `homebench tasks` for the list.
The optional `--judge MODEL` flag turns on an LLM-as-judge (any local model) that scores open-ended tasks 1–5 against a reference answer. It's a signal, not an oracle.
### Fast by default
Benchmarking every model on the full suite takes a while on a laptop, so the defaults are tuned for a quick first look:
- **3 smallest models** by default (smallest first, so results appear fast) — `--all` for everything, `-m` to choose. - **A fast quality subset** (~8 tasks across all categories) — `--full` for all 31. - **Response caching**: quality runs use temperature 0 + a fixed seed, so responses are deterministic and cached under `~/.homebench`. Re-running only regenerates *new* models/tasks (unchanged ones are re-graded from cache in milliseconds); `--refresh-cache` forces recompute, `--no-cache` disables it.
In practice this turns a first run from ~15–25 min (all models, full suite) into ~1–2 min, and a re-run into seconds. For a thorough pass (CI, final numbers) use `homebench --all --full`.
## Custom task packs
Bring your own evals with a JSON or YAML pack — no Python required. `--tasks` replaces the built-in suite; `--add-tasks` appends to it. YAML needs the optional extra (`pip install "homebench[yaml]"`); JSON works out of the box.
```yaml # mypack.yaml — homebench --tasks mypack.yaml name: my-pack tasks: - id: capital_japan category: factual prompt: "What is the capital of Japan? Answer with just the city name." grader: {type: contains_any, values: ["Tokyo"]} reference: Tokyo - id: add category: math prompt: "What is 12 + 30? End with the answer on its own line." grader: {type: exact_number, value: 42} - id: explain # no grader -> open-ended, scored only with --judge category: open prompt: "Explain photosynthesis in one sentence." reference: "Plants convert sunlight, water, and CO2 into glucose and oxygen." ```
Grader `type` values: `exact_number` (`value`, `tol`), `multiple_choice` (`value`), `contains_any` (`values`), `regex` (`pattern`, `ignorecase`), `valid_json` (`keys`), `valid_json_array` (`length`). Omit `grader` for a judge-only task. Runnable examples live in [`examples/`](examples/); preview any pack with `homebench tasks --tasks mypack.yaml`.
## History & diffing
Every run is saved automatically to `$HOMEBENCH_HOME/runs` (default `~/.homebench/runs`); disable with `--no-save`, and tag runs with `--label`.
```bash homebench history # table of past runs (newest first) homebench diff # previous run -> latest homebench diff 3 # run #3 -> latest homebench diff 3 1 # run #3 (base) -> run #1 (newer) ```
`diff` compares models by name and shows per-model deltas in quality and throughput, plus which models were added or removed between runs — handy for "did that quantization / setting actually help?"
## Batch throughput
The main leaderboard measures **single-stream** tok/s. Servers that batch requests (vLLM, llama.cpp continuous batching, Ollama with `OLLAMA_NUM_PARALLEL>1`) can do far more total work under concurrency — `homebench throughput` measures that:
```bash homebench throughput -m my-model --concurrency 1,2,4,8 ```
It fires N requests at each concurrency level (N defaults to 3×concurrency) and reports **aggregate** tok/s (total output ÷ wall-clock), the speedup vs. concurrency 1, mean per-request rate, and latency (mean / p95):
``` Batch throughput — my-model (vllm) ┏━━━━━━┳━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━┓ ┃ Conc ┃ Reqs ┃ Agg tok/s ┃ Speedup ┃ Req tok/s ┃ Mean lat ┃ p95 lat ┃ Errors ┃ ┡━━━━━━╇━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━┩ │ 1 │ 4 │ 95.0 │ 1.00× │ 95.0 │ 1.35 s │ 1.4 s │ 0 │ │ 4 │ 12 │ 320.0 │ 3.37× │ 82.0 │ 1.56 s │ 1.9 s │ 0 │ │ 8 │ 24 │ 540.0 │ 5.68× │ 70.0 │ 1.83 s │ 2.6 s │ 0 │ └──────┴──────┴───────────┴─────────┴───────────┴──────────┴─────────┴────────┘ ```
On a non-batching setup, aggregate throughput stays flat while latency climbs — which is itself a useful thing to see. Add `--json FILE` to export.
## What can my machine run?
Before benchmarking, `homebench fit` captures your hardware (RAM, CPU, GPU/VRAM, Apple unified memory) and checks a **catalog of ~50 popular models** — SmolLM2, Qwen2.5, Llama 3.x, Gemma 2, Phi-3.5/4, Mistral/Mixtral, DeepSeek-R1, CodeLlama, Yi, Command-R, and more, from 135M up to 141B — against your memory budget, showing which fit and at what quantization:
```bash homebench fit # what fits, at the best quant homebench fit --all # include models that don't fit homebench fit --context 8192 # budget a larger KV cache homebench fit --quant Q4_K_M # evaluate a specific quant homebench fit --vram 24 # what-if: "if I had a 24 GB GPU…" homebench fit --catalog my.json # add your own models to the catalog ```
### Live list from HuggingFace
Instead of the built-in catalog, pull the **currently most popular models straight from the HuggingFace Hub** — their parameter counts (from safetensors metadata) are sized against your hardware in real time:
```bash homebench fit --online # top 50 text-generation models by downloads homebench fit --online --top 100 # cast a wider net homebench fit --online --sort trending # or: likes homebench fit --online --refresh # bypass the 1-day cache ```
Results are cached under `$HOMEBENCH_HOME` (`~/.homebench`), so repeat runs are fast and work offline; if the Hub is unreachable, `homebench` falls back to the cache (or the built-in catalog).
The built-in catalog also ships each model's **Ollama tag** (`ollama pull …`) and **HuggingFace repo** (which LM Studio and vLLM pull from). Add your own with a JSON catalog (see [`examples/models.example.json`](examples/models.example.json)): a list of `{name, params_b, family?, ollama?, hf?}`. Sizes are estimates (weights + KV cache + overhead), so treat "fits"/"tight" as guidance. Add `--json FILE` to export the hardware profile and results.
## Limitations
`homebench` is a fast, local **first look** — not a rigorous benchmark of record. Keep these in mind:
- **Quality is a signal, not a leaderboard of record.** The suite is small and English-only (8 tasks in the fast default, 31 with `--full`); it's designed to *separate* your models, not to rank them authoritatively. For serious evals use [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness). The optional LLM-as-judge is noisy, especially with small local judges. - **Speed is your-machine-at-that-moment.** tok/s and TTFT depend on current load, thermal state, and memory pressure — a busy laptop (or swapping when low on RAM) will read slower. Numbers are meaningful *relative* to each other on the same run, not as absolute model specs. - **Memory is best-effort.** It uses the runner's resident size where exposed (Ollama `/api/ps`, LM Studio `/api/v0`) plus RSS sampling; on unified-memory Macs it's approximate, and client-timed for OpenAI-compatible backends. - **`fit` sizes are estimates** (weights + KV cache + overhead) — treat "fits/tight" as guidance, not a guarantee. HuggingFace param counts come from safetensors metadata, which is missing for GGUF-only or gated repos. - **Throughput scaling only appears on batching servers** (vLLM, etc.); a single local model serializes requests.
## Development
```bash git clone https://github.com/david-g-3654/homebench cd homebench python -m venv .venv && source .venv/bin/activate pip install -e ".[dev]" pytest -q ```
The codebase is small and layered: `providers/` (pluggable backends), `quality/` (tasks, graders, judge), `metrics/` (memory sampling), `runner.py` (orchestration), `report.py` (export + tables), and `tui/` + `plainui.py` (rendering). Adding a provider means subclassing `Provider` (or `OpenAICompatibleProvider`) and registering it; adding a task means appending to the suite in `quality/tasks.py` with a reference that satisfies its grader (enforced by the tests).
Contributions welcome — new providers, task packs, and metrics especially.
## Roadmap
- [x] PyPI release - [ ] HTML / shareable report export - [ ] Per-run environment capture (OS, RAM, GPU) for comparable results - [ ] Community task-pack sharing - [ ] GitHub Action for automated benchmarking in CI
## License
MIT