Benchmark the local LLMs you already have — speed, memory, and quality — as a live terminal leaderboard.

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.

pip install homebench
homebench

That's it. No config, no API keys, no cloud.

There are great tools for one half of this problem, but nothing local-first that does both:

• llama-bench (inside llama.cpp) measures speed only.
• 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?"

pip install homebench        # then run:  homebench

Prefer an isolated install? Use pipx:

Or from source:

git clone https://github.com/david-g-3654/homebench
cd homebench
pip install .

Requires Python 3.9+.

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.

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.)

At least one local model runner must be reachable:

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.

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.

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.

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.

# 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/; preview any pack with homebench tasks --tasks mypack.yaml.

Every run is saved automatically to $HOMEBENCH_HOME/runs (default ~/.homebench/runs); disable with --no-save, and tag runs with --label.

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?"

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:

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.

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:

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

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:

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): 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.

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. 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.

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.

MIT