GLM-4.6 on Mac Studio: Complete Guide

The biggest, baddest GLM-4.6 running locally on Metal GPU

This guide covers everything from zero to serving GLM-4.6 via OpenAI-compatible API on your Mac Studio, plus uploading your merged model to Hugging Face.

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Part 1: Local Setup (Nothing → Serving)

Prerequisites

Hardware:

• Mac Studio (Apple Silicon)
• 192GB Unified Memory
• ~500GB free disk space

OS: macOS (any recent version)

What you'll get: GLM-4.6 running on Metal GPU, accessible via OpenAI-compatible API for use in OpenCode and other IDEs.

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Step 1: Build llama.cpp with Metal

# Clone and build with Metal support
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
cmake . -B build -DGGML_METAL=ON
cmake --build build --config Release -j

Time: 5-10 minutes
What this does: Compiles llama.cpp to use your Mac's GPU via Metal framework

---

Step 2: Download and Merge Model

Choose your quantization:

• Q4_K_M (140GB) - Safe choice, excellent quality, comfortable fit
• Q5_K_M (180GB) - Higher quality, tighter fit, slightly risky

Recommended: Q4_K_M for reliability.

brew install aria2
pip3 install 'huggingface_hub<1.0' hf_transfer --break-system-packages

# Download Q4_K_M
nohup ~/bin/run-aria2c.sh &
disown %1

# Monitor download
watch -n 5 ~/bin/monitor-aria2c.sh

# Merge sharded files into single GGUF
./build/bin/llama-gguf-split --merge glm46/Q4_K_M/GLM-4.6-Q4_K_M-00001-of-*.gguf glm46-q4.gguf

Time: 10-15 hours for download (varies with internet speed), 15-20 minutes for merge
What this does: Downloads 5-6 sharded files from Unsloth's repo and combines them into one file Ollama and llama.cpp can use

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Step 3: Test with CLI (Interactive Mode)

cd ~/llama.cpp

# Run interactive chat
./build/bin/llama-cli \
  --model glm46-q4.gguf \
  --jinja \
  --threads -1 \
  --n-gpu-layers 999 \
  --temp 1.0 \
  --top-p 0.95 \
  --top-k 40 \
  --ctx-size 32768 \
  -ot ".ffn_.*_exps.=CPU" \
  --color \
  -i

What the flags mean:

• --jinja - Required for GLM-4.6's chat template (without this, output is garbage)
• --n-gpu-layers 999 - Push as much as possible to Metal GPU
• -ot ".ffn_.*_exps.=CPU" - Keep MoE expert layers on CPU (prevents memory overflow)
• --ctx-size 32768 - Context window (32K tokens)
• -i - Interactive mode

Test it: Type a message, press Enter, verify you get good responses using Metal GPU.

Exit: Type /bye or press Ctrl+C

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Step 4: Serve as OpenAI-Compatible API

cd ~/llama.cpp

# Start server
./build/bin/llama-server \
  --model glm46-q4.gguf \
  --host 0.0.0.0 \
  --port 8080 \
  --ctx-size 32768 \
  --n-gpu-layers 999 \
  -ot ".ffn_.*_exps.=CPU" \
  --threads -1 \
  --jinja \
  --alias "glm-4.6" \
  --chat-template auto

API endpoint: http://localhost:8080/v1
Model name: glm-4.6

Keep this terminal open. The server runs in foreground. Use Command+Tab to switch to other apps.

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Step 5: Configure OpenCode

In OpenCode settings:

1. Open OpenCode settings/preferences
2. Find "AI Provider" or "Model Configuration"
3. Add new provider:
   • Provider: OpenAI
   • Base URL: http://localhost:8080/v1
   • API Key: not-needed (or leave blank)
   • Model: glm-4.6

Works with: OpenCode, Cursor, Continue, Cline, or any IDE that supports custom OpenAI endpoints.

---

Performance Expectations

Speed: 5-10 tokens/second on Mac Studio with 192GB
Memory usage (Q4_K_M):

• Model: ~140GB
• Metal GPU: ~30-40GB (attention + shared layers)
• CPU/RAM: ~100-110GB (MoE expert layers)
• KV cache: ~20-30GB
• Total: ~160-170GB working set

Memory usage (Q5_K_M):

• Model: ~180GB
• Metal GPU: ~40-60GB
• CPU/RAM: ~120-140GB
• KV cache: ~20-30GB
• Total: ~200-220GB working set (tight!)

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Key Concepts Explained

GGUF vs Safetensors:

• GGUF = Format llama.cpp uses (optimized for inference)
• Safetensors = Format Hugging Face uses (optimized for training/storage)

Sharded vs Unsharded:

• Sharded = Model split into multiple files (Ollama doesn't support yet)
• Unsharded = Single file (works everywhere)
• Unsloth ships sharded, we merged it to unsharded

Quantization (Q4_K_M, Q5_K_M, Q8_0):

• Q8_0 = Highest quality, ~250GB
• Q5_K_M = Excellent quality, ~180GB
• Q4_K_M = Very good quality, ~140GB
• Q2_K = Low quality, ~70GB
• Higher Q number = better quality, bigger file

Metal vs CPU:

• Metal = Apple's GPU framework (like CUDA for NVIDIA)
• Unified Memory = On Mac, GPU and CPU share the same RAM pool
• MoE offloading = Keep some layers on CPU to avoid GPU memory limits

llama.cpp vs Ollama:

• llama.cpp = Lower-level inference engine with full control
• Ollama = User-friendly wrapper around llama.cpp (easier but less flexible)
• Ollama doesn't support sharded files yet, llama.cpp does everything

---

Part 2: Upload to Hugging Face

Prep Your Model Card

Create README.md with the content from Part 3 below.

Upload Files

# Login to Hugging Face (you'll need an account and API token)
huggingface-cli login

# Create repo on HF website first:
# Go to https://huggingface.co/new
# Repository name: GLM-4.6-Q4_K_M-Unsharded-Metal
# License: Same as original GLM-4.6
# Click "Create model"

# Upload the merged GGUF file
huggingface-cli upload YOUR_USERNAME/GLM-4.6-Q4_K_M-Unsharded-Metal \
  ~/llama.cpp/glm46-q4.gguf \
  glm46-q4.gguf

# Upload README
huggingface-cli upload YOUR_USERNAME/GLM-4.6-Q4_K_M-Unsharded-Metal \
  README.md \
  README.md

# Create .gitattributes for large file handling
echo "*.gguf filter=lfs diff=lfs merge=lfs -text" > .gitattributes

huggingface-cli upload YOUR_USERNAME/GLM-4.6-Q4_K_M-Unsharded-Metal \
  .gitattributes \
  .gitattributes

Time: 1-3 hours depending on upload speed (140GB file)

---

Part 3: Hugging Face Metadata & Content

Model Card (README.md)

# GLM-4.6-Q4_K_M-Unsharded-Metal

**Single-file GGUF for Mac Studio and Apple Silicon**

Unsharded Q4_K_M quantization of GLM-4.6 optimized for Apple Silicon Macs with Metal GPU acceleration.

## Quick Start

```bash
# Download
huggingface-cli download YOUR_USERNAME/GLM-4.6-Q4_K_M-Unsharded-Metal

# Run with llama.cpp
./llama-server \
  --model glm46-q4.gguf \
  --host 0.0.0.0 \
  --port 8080 \
  --n-gpu-layers 999 \
  -ot ".ffn_.*_exps.=CPU" \
  --jinja \
  --alias "glm-4.6"

Access at http://localhost:8080/v1 (OpenAI-compatible API)

What This Is

The original GLM-4.6 Q4_K_M from Unsloth comes as 5-6 separate files. Ollama doesn't support sharded files yet. This is those files merged into one, ready to use with llama.cpp and Metal GPU.

Why Q4_K_M?

• Sweet spot for quality vs size
• Fits comfortably in 192GB Mac Studio
• Excellent performance (minimal quality loss vs Q8_0)
• Fast inference with Metal acceleration

Technical Details

Model: GLM-4.6 (355B parameters, MoE architecture)
Quantization: Q4_K_M
File size: ~140GB
Context window: 200K tokens
Hardware tested: Mac Studio, 192GB unified memory

Memory usage:

• Model: 140GB
• Metal GPU: ~30-40GB (attention layers)
• CPU/RAM: ~100-110GB (MoE experts)
• Total working set: ~170GB

Performance: 5-10 tokens/sec on Mac Studio M2 Ultra

Why Unsharded?

Sharded (original Unsloth):

• 5-6 separate files
• Ollama can't use it
• Annoying to manage

Unsharded (this repo):

• Single file
• Works with Ollama (once they add GLM-4.6 support)
• Works with llama.cpp now
• Easier to download and use

Usage with llama.cpp

Interactive Chat

./llama-cli \
  --model glm46-q4.gguf \
  --jinja \
  --n-gpu-layers 999 \
  -ot ".ffn_.*_exps.=CPU" \
  --ctx-size 32768 \
  -i

OpenAI-Compatible Server

./llama-server \
  --model glm46-q4.gguf \
  --host 0.0.0.0 \
  --port 8080 \
  --n-gpu-layers 999 \
  -ot ".ffn_.*_exps.=CPU" \
  --jinja \
  --alias "glm-4.6" \
  --chat-template auto

Use with any OpenAI-compatible client:

• Endpoint: http://localhost:8080/v1
• Model: glm-4.6
• API Key: Not required

Works in: OpenCode, Cursor, Continue, Cline, or any IDE supporting custom OpenAI endpoints.

Key Flags Explained

• --jinja - REQUIRED for GLM-4.6 chat template
• --n-gpu-layers 999 - Load everything possible to Metal GPU
• -ot ".ffn_.*_exps.=CPU" - Keep MoE expert layers on CPU (prevents OOM)
• --ctx-size 32768 - Context window (use up to 200K if you have RAM)

Without --jinja the output will be gibberish. This flag tells llama.cpp to use GLM-4.6's special chat format.

Hardware Requirements

Minimum:

• Apple Silicon Mac (M1/M2/M3/M4)
• 160GB+ RAM
• 150GB free disk space

Recommended:

• Mac Studio with 192GB unified memory
• 200GB free disk space
• macOS Sonoma or later

Will NOT work on:

• Intel Macs (no Metal support)
• Macs with <160GB RAM
• NVIDIA/AMD GPUs (use CUDA builds instead)

Comparison: Q4_K_M vs Other Quants

Quant	Size	Quality	Speed	Fits in 192GB?
Q8_0	250GB	Highest	Slower	Tight fit
Q5_K_M	180GB	Excellent	Fast	Yes
Q4_K_M	140GB	Very Good	Faster	Yes (safe)
Q3_K_M	110GB	Good	Fastest	Yes
Q2_K	70GB	Poor	Very fast	Yes

Q4_K_M is the sweet spot for most users. Q5_K_M if you want maximum quality and can spare the extra 40GB.

Known Issues

1. Tool calling format differs from original - Uses Qwen3-style JSON instead of XML (Ollama limitation)
2. Ollama native support pending - Use llama.cpp directly for now
3. First token slow with full context - Expected with 200K context window

Build Process (How This Was Made)

# 1. Build llama.cpp with Metal
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
cmake . -B build -DGGML_METAL=ON
cmake --build build --config Release -j

# 2. Download sharded files
pip3 install huggingface-hub
huggingface-cli download unsloth/GLM-4.6-GGUF --include "Q4_K_M/*" --local-dir ./glm46

# 3. Merge into single file
./build/bin/llama-gguf-split --merge \
  glm46/Q4_K_M/GLM-4.6-Q4_K_M-00001-of-*.gguf \
  glm46-q4.gguf

Total time: ~15 hours (mostly download)

Credits

• Original Model: Zhipu AI (GLM-4.6)
• Quantization: Unsloth (sharded Q4_K_M GGUF)
• Merge: BrianMac (this unsharded version)
• Runtime: llama.cpp team
• Inspiration: Frustration with Ollama's lack of sharded GGUF support

License

Same as original GLM-4.6 model from Zhipu AI.

Related Models

• unsloth/GLM-4.6-GGUF - Original sharded versions (all quants)
• MichelRosselli/GLM-4.6 - Ollama package (Q4_K_M)
• zai-org/GLM-4.6 - Original full-precision model

Support

Having issues? Check these:

1. Did you use --jinja flag? (99% of problems)
2. Is your Mac Studio running low on RAM? (check Activity Monitor)
3. Are you on Apple Silicon? (Intel Macs won't work)
4. Try reducing --ctx-size if running out of memory

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Benchmarks

Tested on Mac Studio M2 Ultra, 192GB unified memory:

• Tokens/sec: 6-8 t/s average
• First token latency: 2-3 seconds (empty context)
• First token latency: 10-15 seconds (32K context)
• Memory usage: 165-175GB total
• GPU utilization: 40-60% (attention layers)
• CPU utilization: 20-40% (MoE layers)

For comparison:

• DeepSeek-V3 Q4_K_M: 4-6 t/s (similar architecture)
• Qwen2.5-72B Q5_K_M: 10-12 t/s (smaller model)
• Llama-3.1-70B Q8_0: 12-15 t/s (non-MoE)

MoE models are inherently slower due to routing overhead, but GLM-4.6 offers significantly better reasoning and coding than similarly-sized dense models.

---

### Hugging Face Web UI Fields

**Model name:** `GLM-4.6-Q4_K_M-Unsharded-Metal`

**Short description:**  
`Unsharded Q4_K_M quantization of GLM-4.6 for Mac Studio and Apple Silicon. Single-file GGUF optimized for Metal GPU. 140GB, runs at 6-8 tokens/sec on 192GB Mac Studio.`

**Tags:**

glm glm-4.6 gguf quantized q4_k_m unsharded metal apple-silicon mac-studio llama-cpp conversational code reasoning moe mixtral

**Language:**

en (English)

**License:**

other (Same as original GLM-4.6 from Zhipu AI)

**Model type:**

Text Generation

**Library:**

GGUF

**Base model:**

zai-org/GLM-4.6

**Datasets used:**

(Leave blank - inference only)

**Additional metadata (in model card YAML frontmatter):**

```yaml
---
language:
  - en
license: other
tags:
  - glm
  - glm-4.6
  - gguf
  - quantized
  - q4_k_m
  - unsharded
  - metal
  - apple-silicon
  - mac-studio
  - llama-cpp
  - conversational
  - code
  - reasoning
  - moe
library_name: gguf
base_model: zai-org/GLM-4.6
inference: true
pipeline_tag: text-generation
model_type: glm
quantization: q4_k_m
---

---

Troubleshooting

Common Issues

"Model outputs gibberish"

• Add --jinja flag (99% of the time this is the issue)

"Out of memory error"

• Reduce --ctx-size to 16384 or 8192
• Try Q3_K_M instead of Q4_K_M
• Close other apps to free RAM

"Not using Metal GPU"

• Rebuild llama.cpp with -DGGML_METAL=ON
• Check you're running on Apple Silicon (not Intel)
• Verify in Activity Monitor → GPU tab

"Too slow / hanging"

• Add -ot ".ffn_.*_exps.=CPU" flag
• Reduce --n-gpu-layers to 50-60
• First token with large context is always slow (expected)

"Download interrupted"

• Resume with same huggingface-cli download command
• It will continue from where it left off

---

Why This Matters

Before this:

• GLM-4.6 comes sharded (6 files)
• Ollama doesn't support sharded files
• Manual merge required for every user
• Confusing for newcomers

After this:

• Single file, ready to use
• Download and run immediately
• Works with all llama.cpp-based tools
• Will work with Ollama once they add GLM-4.6 support

TL;DR: Making GLM-4.6 accessible to Mac Studio users without the hassle.

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That's it! You now have the biggest, baddest GLM-4.6 running locally on your Mac Studio, serving OpenAI-compatible API, and (optionally) uploaded to Hugging Face for others to use.