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macOS has full support for Apple Silicon unified memory and discrete GPU detection on Intel Macs.

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Apple Silicon Unified Memory

All Apple Silicon Macs (M1, M2, M3, M4 series) use unified memory where GPU and CPU share the same RAM pool.

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Detection Method

llmfit uses system_profiler to detect Apple Silicon GPUs: 
system_profiler SPDisplaysDataType
Detection criteria:
  • Searches for “Apple M” or “Apple GPU” in chipset line
  • Example output: 
    Chipset Model: Apple M4 Max
Type: GPU

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Unified Memory Behavior

VRAM = Total System RAM:
  • 16 GB Mac → 16 GB VRAM
  • 32 GB Mac → 32 GB VRAM
  • 128 GB Mac → 128 GB VRAM
No CPU Offload Path:
  • GPU and CPU share the same memory pool
  • Run mode is always GPU (unified) or CPU (no separate offload)
  • unified_memory flag set to true

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Metal Backend

All Apple Silicon GPUs use Metal for GPU acceleration: 
llmfit system
# GPU: Apple M4 Max (unified memory, 128.00 GB shared, Metal)
Memory Bandwidth: llmfit uses actual unified memory bandwidth for speed estimation:
ChipBandwidth (GB/s)
M168
M1 Pro200
M1 Max400
M1 Ultra800
M2100
M2 Pro200
M2 Max400
M2 Ultra800
M3100
M3 Pro150
M3 Max400
M3 Ultra800
M4120
M4 Pro273
M4 Max546
M4 Ultra819
Source: hardware.rs:1584-1632

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Available RAM Detection

Recent macOS versions (Sequoia, Tahoe) sometimes report 0 for available memory via sysinfo. llmfit has fallbacks:

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1. Total - Used

let used = sys.used_memory();
let available = total_bytes - used;

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2. vm_stat Parsing

vm_stat
# Mach Virtual Memory Statistics: (page size of 16384 bytes)
# Pages free:                               123456.
# Pages inactive:                           234567.
# Pages purgeable:                           12345.
Calculation: 
let available_bytes = (free + inactive + purgeable) * page_size;
  • Apple Silicon default page size: 16 KB (16384 bytes)
  • Intel Macs: 4 KB (4096 bytes)

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3. Conservative Fallback

If both fail, assume 80% of total RAM is available: 
total_ram_gb * 0.8

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Intel Mac Support

Intel Macs have discrete GPUs (AMD or NVIDIA) and do not use unified memory.

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NVIDIA GPUs (Older Intel Macs)

Some Intel Macs have discrete NVIDIA GPUs: 
# Check if nvidia-smi is available
nvidia-smi
If nvidia-smi works, llmfit detects VRAM and uses CUDA backend. Note: NVIDIA stopped official macOS support after macOS 10.13 (High Sierra). Most Intel Macs with discrete GPUs have AMD cards.

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AMD GPUs (Intel Macs)

Intel MacBook Pro / iMac Pro with AMD Radeon GPUs:
  • Detection: system_profiler SPDisplaysDataType
  • VRAM: Not reported by system_profiler (shows “Metal: Supported”)
  • llmfit falls back to CPU detection (no GPU reported)
Workaround: Use manual override: 
# 16-inch MacBook Pro with Radeon Pro 5500M (8GB)
llmfit --memory=8G system

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Installation Methods

brew install llmfit

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Quick Install Script

# Install to /usr/local/bin (requires sudo)
curl -fsSL https://llmfit.axjns.dev/install.sh | sh

# Install to ~/.local/bin (no sudo)
curl -fsSL https://llmfit.axjns.dev/install.sh | sh -s -- --local

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From Source

git clone https://github.com/AlexsJones/llmfit.git
cd llmfit
cargo build --release
cp target/release/llmfit /usr/local/bin/

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Runtime Providers

llmfit integrates with local runtime providers for downloading and running models on macOS:

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Ollama

Install Ollama: 
brew install ollama

# Start Ollama service
ollama serve
llmfit auto-detects Ollama at http://localhost:11434: 
llmfit
# System bar shows: Ollama: ✓ (N installed)

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llama.cpp

Install llama.cpp: 
brew install llama.cpp

# Verify installation
which llama-cli
which llama-server
llmfit detects llama.cpp runtime and uses local GGUF cache.

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MLX (Apple Silicon Only)

MLX is optimized for Apple Silicon unified memory: 
pip install mlx
pip install mlx-lm
llmfit detects MLX models in ~/.cache/huggingface/hub/.

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Troubleshooting

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GPU Not Detected (Apple Silicon)

  1. Check system_profiler: 
    system_profiler SPDisplaysDataType | grep -i "chipset\|apple"
  2. Expected output: 
    Chipset Model: Apple M4 Max
  3. If not detected, llmfit falls back to CPU detection (still works, but no GPU indication)

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Available RAM Shows 0

This is a known issue on macOS Sequoia and newer: 
# Check vm_stat
vm_stat
llmfit should automatically fall back to vm_stat parsing. If it shows unrealistic values, file a bug report.

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Intel Mac Discrete GPU Not Detected

Intel Macs with AMD Radeon GPUs are not auto-detected. Use manual override: 
# Check GPU via system_profiler
system_profiler SPDisplaysDataType
# Chipset Model: AMD Radeon Pro 5500M
# VRAM (Dynamic, Max): 8 GB

llmfit --memory=8G system

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OLLAMA_HOST Connection Issues

If Ollama is running but llmfit doesn’t detect it: 
# Check if Ollama is running
curl http://localhost:11434/api/tags

# If using custom port
export OLLAMA_HOST="http://localhost:11434"
llmfit

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MLX Not Detected

  1. Check if MLX is installed: 
    python3 -c "import mlx; print(mlx.__version__)"
  2. Check MLX cache: 
    ls ~/.cache/huggingface/hub/ | grep mlx
  3. Install MLX if missing: 
    pip install mlx mlx-lm

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Performance Issues

Unified Memory Pressure: Apple Silicon shares memory between GPU and CPU. Check memory pressure: 
# Activity Monitor > Memory tab
# Watch for "Memory Pressure" indicator
If memory pressure is high:
  • Close unused apps
  • Use smaller models or lower context lengths
  • Use --max-context to cap memory estimation: 
    llmfit --max-context 4096
Swap Usage: Apple Silicon Macs use swap aggressively. Check swap usage: 
sysctl vm.swapusage
High swap usage degrades performance. Consider models that fit in ~70-80% of physical RAM.

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Next Steps

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