Show HN: mlx-chronos - benchmark MLX inference engines on Apple Silicon

A new open-source tool called mlx-chronos provides a standardized benchmark suite and community leaderboard for comparing local LLM inference engines on Apple Silicon Macs. The tool runs reproducible benchmarks, generates sealed JSON results, and allows users to compare performance across different engines and Mac models.

Benchmark suite and community leaderboard for local LLM inference on Apple Silicon. Run a reproducible benchmark, save a sealed JSON result, and compare engines across Macs. Overview overview Supported Engines supported-engines Quick Start quick-start CLI Reference cli-reference Configuration configuration Benchmark Protocol benchmark-protocol Leaderboard Rules leaderboard-rules Submit Results submit-results Roadmap roadmap mlx-chronos is a standardized benchmark tool for local LLM inference engines on Apple Silicon. It detects your Mac, runs a fixed benchmark protocol against an OpenAI-compatible engine endpoint, and writes structured result files for local analysis or public leaderboard submission. The public leaderboard is available at igurss.github.io/mlx-chronos https://igurss.github.io/mlx-chronos . | Metric | Meaning | Public comparison use | |---|---|---| | TTFT cold | Time from request start to first non-empty streamed token with cache-avoiding prompts | Yes | | TTFT cached | Time to first token after a cache-priming call with the same prompt | Yes | | Request throughput | Completion tokens divided by full client-observed request time | Yes, when engine token usage is reliable | | Sustained throughput | Optional long throughput run for heat buildup and late-run degradation | Yes, under the sustained profile | | System RAM peak | Peak total Mac RAM in use during the benchmark | Yes | | Engine RSS | Post-warmup RSS of the engine server process when identifiable | Diagnostic only | | Thermal state | Start, end, worst state, samples, and affected benchmark phases when available | Context metadata | | Tool calling | Planned future success-rate benchmark | Not yet available | 0.3.1 simplifies public model identity metadata to model name, quantization, model format, and the required model reference URL, while keeping the guided workflows, timing metadata, and stricter leaderboard integrity checks introduced in 0.3.0 . | Engine | Project | Notes | |---|---|---| | Ollama | | jundot/omlx https://github.com/jundot/omlx raullenchai/Rapid-MLX https://github.com/raullenchai/Rapid-MLX waybarrios/vllm-mlx https://github.com/waybarrios/vllm-mlx ml-explore/mlx-lm https://github.com/ml-explore/mlx-lm NoteThe engine server must already be running before mlx-chronos run , mlx-chronos models , or mlx-chronos validate can query it. See CONTRIBUTING.md for engine setup details. pip install mlx-chronos Optional thermal-state support through macOS Foundation/PyObjC: pip install "mlx-chronos thermal " mlx-chronos --version mlx-chronos upgrade When run in an interactive terminal, mlx-chronos performs a best-effort background PyPI version check. If a newer release is available, it prints a short notice recommending: mlx-chronos upgrade Set MLX CHRONOS DISABLE UPDATE CHECK=1 to disable the automatic check. mlx-chronos engines mlx-chronos models --engine omlx mlx-chronos validate --engine omlx --model "Qwen3.5-4B-OptiQ-4bit" mlx-chronos wizard The wizard provides a terminal menu for common actions and a guided benchmark builder with engine, model, profile, token bounds, output format, cooldown, preflight, notes, and other run options. When the selected engine server is running, the wizard loads /models and lets you select a model from the exposed IDs, with manual entry as a fallback. Before launching a benchmark, it shows the equivalent mlx-chronos run ... command so the same configuration can be reused in scripts. You can return to the main menu from benchmark setup without starting a run. mlx-chronos run --engine omlx --model "Qwen3.5-4B-OptiQ-4bit" Results are written to results/local/ by default. Write both JSON and Markdown outputs mlx-chronos run --engine omlx --model "Qwen3.5-4B-OptiQ-4bit" --format all Choose a custom output directory mlx-chronos run --engine omlx --model "Qwen3.5-4B-OptiQ-4bit" --output-dir ~/Desktop/benchmarks Request throughput output token bounds for local experiments mlx-chronos run --engine omlx --model "Qwen3.5-4B-OptiQ-4bit" --max-tokens 100 --min-tokens 80 Run the longer heat/throttling-sensitive sustained profile mlx-chronos run --engine omlx --model "Qwen3.5-4B-OptiQ-4bit" --profile sustained Enforce cooldown after a recent run in the same output directory mlx-chronos run --engine omlx --model "Qwen3.5-4B-OptiQ-4bit" --cooldown-seconds 300 Fail fast with an extra model access probe before measured work starts mlx-chronos run --engine omlx --model "Qwen3.5-4B-OptiQ-4bit" --preflight Include a model reference URL, required for public leaderboard submissions mlx-chronos run --engine omlx \ --model "Qwen3.5-4B-OptiQ-4bit" \ --model-url "https://huggingface.co/mlx-community/Qwen3.5-4B-OptiQ-4bit" | Command | Purpose | |---|---| mlx-chronos --version | Print the installed package version | mlx-chronos wizard | Open an interactive menu for common commands and guided benchmark setup | mlx-chronos upgrade | Check PyPI and upgrade the current Python environment if a newer release exists | mlx-chronos engines | List supported engines and local installed/running status | mlx-chronos models --engine <name | List model IDs exposed by a running engine server | mlx-chronos validate --engine <name --model <model | Validate hardware, engine, server, and optional model access | mlx-chronos run --engine <name --model <model | Run a benchmark and save local result files | mlx-chronos submit --file <result.json --dry-run | Validate whether a result is publishable | mlx-chronos submit --file <result.json | Send a validated result to the maintainer inbox | | Setting | Example | What it changes | |---|---|---| MLX CHRONOS <ENGINE PORT | MLX CHRONOS OMLX PORT=8002 | Overrides an engine server port | MLX CHRONOS CACHED TTFT RATIO | MLX CHRONOS CACHED TTFT RATIO=0.8 | Sets the cached-TTFT warning threshold | MLX CHRONOS DISABLE UPDATE CHECK | MLX CHRONOS DISABLE UPDATE CHECK=1 | Disables automatic background update checks | MLX CHRONOS SUBMIT ENDPOINT | https://example.test/form | Overrides the maintainer inbox endpoint | Default engine ports: | Engine | Default port | |---|---| | oMLX | 8000 | | Rapid-MLX | 8001 | | vllm-mlx | 8000 | | mlx-lm | 8080 | | Ollama | 11434 | oMLX and vllm-mlx both default to port 8000 . To avoid mislabeling results, mlx-Chronos checks the oMLX listener process with lsof ; if that process cannot be inspected, oMLX validation may fail even when /v1/models responds. mlx-chronos run executes a fixed protocol against the running engine. The JSON result records exact prompt text, token bounds, benchmark profile, timing metadata, hardware metadata, and an integrity seal. | Phase | What happens | |---|---| | Hardware detection | Captures chip, machine model, memory, macOS, Python, architecture, battery state, Low Power Mode, and thermal context when available | | Warmup | Uses a separate prompt so same-run prefix/KV cache hits do not remove throughput prefill work | | Cold TTFT | Uses unique prompts inside the run to avoid same-run cache hits | | Cached TTFT | Primes one fixed prompt, then measures consecutive cached trials | | Throughput | Uses fixed protocol prompts and deterministic generation parameters | | RAM and thermal tracking | Samples system RAM, diagnostic engine RSS, phase timings, and thermal state where available | | Result sealing | Adds a tamper-evident integrity seal for public-submission validation | - Requests use deterministic generation parameters: temperature=0.0 and top p=1.0 . - Throughput is end-to-end request throughput, not pure decode speed. It includes request overhead, prefill, and decode. - Timed TTFT and throughput requests are never retried. A transient request failure invalidates the run instead of becoming part of a published timing. - Cached TTFT is recorded only after cache priming completes successfully. - Decode throughput records first-content-to-stream-end elapsed time so the value can be reconstructed from raw completion-token counts. - Throughput prompts intentionally vary to reduce cache artifacts, so run standard deviation includes workload variation plus system and engine noise. - If an engine cannot provide reliable usage.completion tokens , the run falls back to a local estimate and is marked as not leaderboard-comparable. - p95 is reported only when at least 20 trials are available. - The default baseline run uses 5 trials. The maximum prompt pool supports 30 unique cold and throughput prompts. --profile sustained runs one long throughput trial with max tokens=1000 by default and records progress samples every 100 generated output units. Intermediate samples are estimates when the stream only reports exact token usage at the end. If the sustained run observes a thermal-state change or non-nominal thermal state, result metadata includes a sustained throttling warning. The warning compares early and late progress-window averages, not a single first/last sample. Before each run, mlx-Chronos checks the latest prior JSON result in the same output directory. The elapsed time is saved as meta.elapsed since last benchmark seconds . Use --cooldown-seconds to enforce a pause before starting another run. The default recent-run warning threshold is 300 seconds. For a fuller explanation, see docs/methodology.md https://github.com/igurss/mlx-chronos/blob/main/docs/methodology.md . Local runs are intentionally flexible. You can change trial count, profile, output token bounds, cooldown, connection mode, notes, and other parameters for your own diagnostics. Public leaderboard submissions are stricter so rows remain comparable. | Profile | Trials | max tokens | Minimum generated output | min tokens | |---|---|---|---|---| | Baseline | 5 | 100 | 80 tokens | Not allowed | | Sustained | 1 | 1000 | 800 tokens | Not allowed | - Throughput must use the engine response's usage.completion tokens . - The result must include model.reference url , a link to the model used. - The inference engine version must be known; engine.version=unknown is not accepted for public comparison. - Hardware must report an Apple M-series chip, arm64 , and a valid macOS version; timestamps may not be more than 10 minutes in the future. - All warmup calls must complete successfully warmup failures=0 . - System RAM, engine RSS, and continuous Foundation thermal monitoring must complete without sampling errors. - macOS Low Power Mode must be disabled. - Decode throughput must include reconstructible raw decode elapsed time. - The JSON must pass mlx-chronos submit --dry-run . - The result must include a valid integrity seal. - The archive rejects duplicate integrity digests and duplicate run identities. - Custom token bounds, fallback token estimates, custom public-profile trial counts, short-output runs, and Low Power Mode runs are valid local records but are not accepted into the public leaderboard. Result JSON also contains internal benchmark-protocol labels used by validators to detect incompatible result formats. Treat labels such as 1 , 2 , and 3 as implementation compatibility markers, not public protocol release versions. Model reference URLs point to the model page used for the run. Model pages can change over time when maintainers update files or tags. Leaderboard comparisons keep model name, quantization, format, and model reference URL separate so distinct variants are not grouped together. - Run mlx-chronos run on your Mac. - Find the generated JSON in results/local/ . - Validate it locally: mlx-chronos submit --file results/local/your-result.json --dry-run - Copy the checked JSON into results/submitted/ with a clear filename. - Open a pull request with only that JSON file changed. - GitHub Actions labels the PR as result-submission , validates schema and integrity, and the maintainer reviews it before merge. WarningDo not edit submitted JSON by hand after the run. Public submissions include an integrity seal over the canonical result payload; changing any benchmark field invalidates that seal. If opening a PR is inconvenient, send a validated result directly: mlx-chronos submit --file results/local/your-result.json Maintainers can override the inbox endpoint with --endpoint or MLX CHRONOS SUBMIT ENDPOINT . See CONTRIBUTING.md https://github.com/igurss/mlx-chronos/blob/main/CONTRIBUTING.md for detailed contributor instructions. - Core benchmark runner with repeated trials, warmup, cache priming, and phase-separated metrics - Engine support for oMLX, Rapid-MLX, vllm-mlx, mlx-lm, and Ollama - Hardware detection for chip, machine model, memory, macOS, Python, architecture, and thermal state - Strict JSON schema validation with raw-trial consistency checks - Continuous system RAM peak sampling, with post-warmup engine RSS kept as a diagnostic field - Preflight validation for engine, server, and model access - GitHub Actions validation for submitted results - PR-based result submissions with automatic result-submission , code , and documentation labels - GitHub Pages leaderboard with model/chip/RAM engine comparison and configurable raw-data columns - JSON and Markdown result export - mlx-chronos submit for sending validated JSON results to the maintainer inbox - Warnings for battery mode, Low Power Mode, non-nominal thermal state, and unavailable thermal state - Integration tests against mock OpenAI-compatible servers - Larger fixed cold-prompt pool with optional p95 reporting for larger runs - Request-throughput timing metadata and client-observed streaming decode throughput - Phase timing metadata and lightweight continuous thermal monitoring - Sustained benchmark profile, cooldown metadata, and strict local-vs-public leaderboard policy - Public submission trust model with lightweight anti-spoofing checks - External contributor workflow for code PRs and leaderboard result submissions - CLI update notifications and mlx-chronos upgrade - Evaluate a clearer TTFT naming model without breaking the v0.1 JSON contract - Add tool-calling success-rate benchmarks - Collect more results from M3, M4, and M5 systems Apache 2.0. See LICENSE https://github.com/igurss/mlx-chronos/blob/main/LICENSE .