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Open Source Local LL Benchmarking and Leaderboards on MacOS

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Anubis anubis_icon (1)

Local LLM Testing & Benchmarking for Apple Silicon | Community Leaderboard
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Anubis is a native macOS app for benchmarking, comparing, and managing local large language models using any OpenAI-compatible endpoint - Ollama, MLX, LM Studio Server, OpenWebUI, Docker Models, etc. Built with SwiftUI for Apple Silicon, it provides real-time hardware telemetry correlated with full, history-saved inference performance - something no CLI tool or chat wrapper offers. Export benchmarks directly without having to screenshot, and export the raw data as .MD or .CSV from the history. You can even OLLAMA PULL models directly within the app. The binary is signed with an Apple Dev certificate. We are trying to get to 75 stars to distribute as a Cask via Homebrew. Please consider submitting your results to the leadersboards, the dataset is also open-source, and we are trying to use the data with researchers to enhance how models are trained and perform.

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Why Anubis?

The local LLM ecosystem on macOS is fragmented:

  • Chat wrappers (Ollama, LM Studio, Jan) focus on conversation, not systematic testing
  • Performance monitors (asitop, macmon, mactop) are CLI-only and lack LLM context
  • Evaluation frameworks (promptfoo) require YAML configs and terminal expertise
  • No tool correlates hardware metrics (GPU / CPU / ANE / power / memory) with inference speed in real time

Anubis fills that gap with three integrated modules - all in a native macOS app.

Leaderboard Submissions Now Available! Submit directly through the app

The dataset is robut and open source - check it out here, please contribute!

Features

Benchmark

Real-time performance dashboard for single-model testing.

  • Select any model from any configured backend
  • Stream responses with live metrics overlay
  • 8 metric cards: Tokens/sec, GPU %, CPU %, Time to First Token, Process Memory, Model Memory, Thermal State, GPU Frequency
  • 7 live charts: Tokens/sec, GPU utilization, CPU utilization, process memory, GPU/CPU/ANE/DRAM power, GPU frequency - all updating in real time
  • Power telemetry: Real-time GPU, CPU, ANE, and DRAM power consumption in watts via IOReport
  • Process monitoring: Auto-detects backend process by port (Ollama, LM Studio, mlx-lm, vLLM, etc.) with manual process picker
  • Detailed session stats: peak tokens/sec, average token latency, model load time, context length, eval duration, power averages
  • Configurable parameters: temperature, top-p, max tokens, system prompt
  • Prompt presets organized by category (Quick, Reasoning, Coding, Creative, Benchmarking)
  • Session history with full replay, CSV export, and Markdown reports
  • Expanded full-screen metrics dashboard
  • Image export: Copy to clipboard, save as PNG, or share - 2x retina rendering with watermark, respects light/dark mode

Arena

Side-by-side A/B model comparison with the same prompt.

  • Dual model selectors with independent backend selection
  • Sequential mode (memory-safe, one at a time) or Parallel mode (both simultaneously)
  • Shared prompt, system prompt, and generation parameters
  • Real-time streaming in both panels
  • Voting system: pick Model A, Model B, or Tie - votes are persisted
  • Per-panel stats grid (9 metrics each)
  • Model manager: view loaded models and unload to free memory
  • Comparison history with voting records

Leaderboard (New in 2.1)

Upload your benchmark results to the community leaderboard and see how your Mac stacks up against other Apple Silicon machines.

  • One-click upload from the benchmark toolbar after a completed run
  • Community rankings sorted by tokens/sec with full drill-down into performance, power, and hardware details
  • Filter by chip or model to compare like-for-like (e.g. all M4 Max results, or all Llama 3.2 runs)
  • Data Explorer — interactive pivot table and charting powered by FINOS Perspective
  • Privacy-first: no accounts, no response text uploaded — just metrics and a display name
  • HMAC-signed submissions with server-side rate limiting

Auto-Update (New in 2.3)

Anubis checks for updates automatically via Sparkle and notifies you when a new version is available.

  • Automatic checks on launch with user-controlled frequency
  • Manual check via the app menu (Anubis OSS > Check for Updates...) or Settings > About
  • Updates are code-signed, notarized, and verified with EdDSA before installation

Vault

Unified model management across all backends.

  • Aggregated model list with search and backend filter chips
  • Running models section with live VRAM usage
  • Model inspector: size, parameters, quantization, family, context window, architecture details, file path
  • Automatic metadata enrichment for OpenAI-compatible models - parses model IDs for family and parameter count, scans ~/.lmstudio/models/ and ~/.cache/huggingface/hub/ for disk size, quantization, and path
  • Pull new models, delete existing ones, unload from memory
  • Popular model suggestions for quick setup
  • Total disk usage display

Screenshots

GPU Core detail
Screenshot 2026-02-25 at 4 08 44 PM

Arena Mode
Screenshot 2026-02-25 at 4 21 50 PM

Settings (add connections with quick presets)
Screenshot 2026-02-25 at 4 24 00 PM

Vault - View model details, unload, and Pull models directly for Ollama
Screenshot 2026-02-25 at 4 14 57 PM

Supported Backends

Backend Type Default Port Setup
Ollama Native support 11434 Install from ollama.com - auto-detected on launch
LM Studio OpenAI-compatible 1234 Enable local server in LM Studio settings
mlx-lm OpenAI-compatible 8080 pip install mlx-lm && mlx_lm.server --model <model>
vLLM OpenAI-compatible 8000 Add in Settings
LocalAI OpenAI-compatible 8080 Add in Settings
Docker ModelRunner OpenAI-compatible user selected Add in Settings

Any OpenAI-compatible server can be added through Settings > Add OpenAI-Compatible Server with a name, URL, and optional API key.

Hardware Metrics

Anubis captures Apple Silicon telemetry during inference via IOReport and system APIs:

Metric Source Description
GPU Utilization IOReport GPU active residency percentage
CPU Utilization host_processor_info Usage across all cores
GPU Power IOReport Energy Model GPU power consumption in watts
CPU Power IOReport Energy Model CPU (E-cores + P-cores) power in watts
ANE Power IOReport Energy Model Neural Engine power consumption
DRAM Power IOReport Energy Model Memory subsystem power
GPU Frequency IOReport GPU Stats Weighted average from P-state residency
Process Memory proc_pid_rusage Backend process phys_footprint (includes Metal/GPU allocations)
Thermal State ProcessInfo.thermalState System thermal pressure level

Process Monitoring

Anubis automatically detects which process is serving your model:

  • Port-based detection: Uses lsof to find the PID listening on the inference port (called once per benchmark start)
  • Backend identification: Matches process path and command-line args to identify Ollama, LM Studio, mlx-lm, vLLM, LocalAI, llama.cpp
  • Memory accounting: Uses phys_footprint (same as Activity Monitor) which includes Metal/GPU buffer allocations - critical for MLX and other GPU-accelerated backends
  • LM Studio support: Walks Electron app bundle descendants to find the model-serving process
  • Manual override: Process picker lets you select any process by name, sorted by memory usage

Metrics degrade gracefully - if IOReport access is unavailable (e.g., in a VM), Anubis still shows inference-derived metrics.

Requirements

  • macOS 15.0 (Sequoia) or later
  • Apple Silicon (M1 / M2 / M3 / M4 / M5 +) - Intel is not supported
  • 8 GB unified memory minimum (16 GB+ recommended for larger models)
  • At least one inference backend installed (Ollama recommended)

Getting Started

1. Install Ollama (or another backend) 

# macOS - install Ollama
brew install ollama

# Start the server
ollama serve

# Pull a model
ollama pull llama3.2:3b
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2. Build & Run Anubis 

git clone https://github.com/uncSoft/anubis-oss.git
cd anubis-oss/anubis
open anubis.xcodeproj
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In Xcode:

  1. Set your development team in Signing & Capabilities
  2. Build and run (Cmd+R)

Anubis will auto-detect Ollama on launch. Other backends can be added in Settings.

3. Run Your First Benchmark

  1. Select a model from the dropdown
  2. Type a prompt or pick one from Presets
  3. Click Run
  4. Watch the metrics light up in real time

4. Submit to the Leaderboard

After a benchmark completes, click the Upload button in the benchmark toolbar to submit your results to the community leaderboard. Enter a display name and your run will appear in the rankings — no account required. Only performance metrics and hardware info are submitted; response text is never uploaded.

Building from Source 

# Clone
git clone https://github.com/uncSoft/anubis-oss.git
cd anubis-oss/anubis

# Build via command line
xcodebuild -scheme anubis-oss -configuration Debug build

# Run tests
xcodebuild -scheme anubis-oss -configuration Debug test

# Or just open in Xcode
open anubis.xcodeproj
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Dependencies

Resolved automatically by Swift Package Manager on first build:

Package Purpose License
GRDB.swift SQLite database MIT
Sparkle Auto-update framework MIT
Swift Charts Data visualization Apple

Architecture

Anubis follows MVVM with a layered service architecture:

┌─────────────────────────────────────────────────────────────┐
│                    PRESENTATION LAYER                       │
│   BenchmarkView    ArenaView    VaultView    SettingsView   │
├─────────────────────────────────────────────────────────────┤
│                      SERVICE LAYER                          │
│   MetricsService   InferenceService   ModelService   Export │
├─────────────────────────────────────────────────────────────┤
│                    INTEGRATION LAYER                        │
│  OllamaClient  OpenAICompatibleClient  IOReportBridge  ProcessMonitor │
├─────────────────────────────────────────────────────────────┤
│                    PERSISTENCE LAYER                        │
│   SQLite (GRDB)              File System                    │
└─────────────────────────────────────────────────────────────┘
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Views display data and delegate to ViewModels. ViewModels coordinate Services. Services are stateless and use async/await. Integrations are thin adapters wrapping external systems (Ollama API, IOReport, etc.).

Project Structure 

anubis/
├── App/                    # Entry point, app state, navigation
├── Features/
│   ├── Benchmark/          # Performance dashboard
│   ├── Arena/              # A/B model comparison
│   ├── Vault/              # Model management
│   └── Settings/           # Backend config, about, help, contact
├── Services/               # MetricsService, InferenceService, ExportService
├── Integrations/           # OllamaClient, OpenAICompatibleClient, IOReportBridge, ProcessMonitor
├── Models/                 # Data models (BenchmarkSession, ModelInfo, etc.)
├── Database/               # GRDB setup & migrations
├── DesignSystem/           # Theme, colors, reusable components
├── Demo/                   # Demo mode for App Store review
└── Utilities/              # Formatters, constants, logger
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Backend Abstraction

All inference backends implement a shared protocol, making it straightforward to add new ones:

protocol InferenceBackend {
    var id: String { get }
    var displayName: String { get }
    var isAvailable: Bool { get async }

    func listModels() async throws -> [ModelInfo]
    func generate(prompt: String, parameters: GenerationParameters)
        -> AsyncThrowingStream<InferenceChunk, Error>
}
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Data Storage

All data is stored locally - nothing leaves your machine.

Data Location
Database ~/Library/Application Support/Anubis/anubis.db
Exports Generated on demand (CSV, Markdown)
Preferences UserDefaults

Troubleshooting

Ollama shows "Disconnected" 

# Make sure Ollama is running
ollama serve

# Verify it's accessible
curl http://localhost:11434/api/tags
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No GPU metrics

  • GPU metrics require IOReport access via IOKit
  • Some configurations or VMs may not expose these APIs
  • Anubis will still show inference-derived metrics (tokens/sec, TTFT, etc.)

High memory usage

  • Use Sequential mode in Arena to run one model at a time
  • Unload unused models via Arena > Models > Unload All
  • Choose smaller quantized models (Q4_K_M over Q8_0)

Model not appearing

  • Click Refresh Models in Settings
  • Ensure the model is pulled: ollama pull <model-name>
  • For OpenAI-compatible backends, verify the server is running and the URL is correct

Contributing

Contributions are welcome. A few guidelines:

  1. Follow the existing patterns - MVVM, async/await, guard-let over force-unwrap
  2. Keep files under 300 lines - split if larger
  3. One feature per PR - small, focused changes are easier to review
  4. Test services and integrations - views are harder to unit test, but services should have coverage
  5. Handle errors gracefully - always provide errorDescription and recoverySuggestion

Adding a New Backend

  1. Create a new file in Integrations/ implementing InferenceBackend
  2. Register it in InferenceService
  3. Add configuration UI in Settings/
  4. That's it - the rest of the app works through the protocol

Support the Project

If Anubis is useful to you, consider buying me a coffee on Ko-fi or sponsoring on GitHub. It helps fund continued development and new features.

A sandboxed, less feature rich version is also available on the Mac App Store if you prefer a managed install.

License

GPL-3.0 License — see LICENSE for details.

Other projects: DevPad · Nabu

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