glm-5-specifications

Purpose

Comprehensive analysis of GLM-5 (745 billion parameter MoE model), including specifications, performance benchmarks, hardware requirements, and anticipated Mac Studio deployment guidance for when local deployment becomes available.

Model Overview

Release: February 11, 2026 by Zhipu AI (Z.AI)

Architecture: 745B Mixture-of-Experts (MoE) model

Total parameters: 745 billion
Active parameters per token: ~44 billion (256 experts, 8 activated per token = 5.9% sparsity)
Context window: 200K tokens
Built-in agentic intelligence

Design Philosophy: Frontier-level performance competing with GPT-5.2 and Claude Opus 4.6, while leveraging MoE architecture for efficient compute utilization during inference.

Training Infrastructure: Trained entirely on Huawei Ascend chips using the MindSpore framework, achieving independence from US-manufactured semiconductor hardware.

Key Findings

Positioning

Direct challenger to GPT-5.2 and Claude Opus 4.6
Largest open-weight model anticipated from Zhipu AI (following MIT-licensed GLM-4.7)
Built on proven GLM architecture foundation (GLM-4.5, GLM-4.6, GLM-4.7)

Performance Characteristics

Efficiency: Only 44B active parameters despite 745B total (5.9% sparsity via 8/256 expert activation)
Context: Full 200K token context window
Agentic Intelligence: Built-in agentic capabilities (evolution from GLM-4.5’s 90.6% tool use success rate)

Release Timeline

API Access: February 11, 2026 (Z.AI platform, WaveSpeed API)
Open-Weight Release: Anticipated based on Zhipu AI’s open-source tradition
Local Deployment: Not yet available (as of Feb 11, 2026)

Current Deployment Status

API-Only (Current)

Available Through:

Z.AI Platform: z.ai
WaveSpeed API: WaveSpeed AI

Access Model:

Cloud-hosted API calls only
No local deployment options yet

Anticipated Open-Source Release

Based on Zhipu AI’s established pattern:

GLM-4.7: Released with MIT license for commercial use
GLM-4.5: Open-source with demonstrated tool use capabilities
GLM-5: Likely to follow similar MIT-licensed open-weight release

Expected Availability:

Hugging Face repository (anticipated)
Community quantization efforts (GGUF, MLX formats)
On-premise deployment options (Zhipu AI has existing business model for this)

Hardware Requirements

Estimated Requirements (Based on MoE Architecture Analysis)

Important Note: GLM-5 is not yet available for local deployment. These are extrapolated estimates based on:

GLM-4.7 deployment data (30B-A3B MoE)
Comparable 745B MoE models
Active parameter count (44B vs 745B total)

Full Precision (BF16/FP16)

Memory Required: ~1,490 GB (2 bytes × 745B parameters)
Recommended Hardware:
- Multi-GPU server setups (8× A100 80GB = 640GB insufficient)
- Likely requires 8× H100 80GB (640GB) + CPU offloading
- Not practical for single-machine deployment

8-bit Quantization

Memory Required: ~745 GB (1 byte × 745B parameters)
Recommended Hardware:
- 10× A100 80GB GPUs (800GB total)
- 8× H100 80GB + CPU memory offloading
- Still impractical for most setups

4-bit Quantization (Most Practical)

Memory Required: ~373 GB (0.5 bytes × 745B parameters)
Additional Overhead: +15-20% for KV cache, activations (~440-450GB total)
Recommended Hardware:
- 6× A100 80GB GPUs (480GB total) - viable
- Mac Studio Ultra with 256GB unified memory - MARGINAL (tight fit)
- High-memory workstations (512GB+ RAM with GPU offloading)

Active Parameters Optimization

Key Insight: Only 44B parameters active per token (5.9% sparsity)

Optimized Memory Estimate (4-bit with MoE optimization):

Core Active Parameters: ~22GB (44B params × 0.5 bytes)
Expert Storage: ~373GB (all 256 experts)
Total with optimizations: ~200-250GB (depending on implementation)

This is where Mac Studio Ultra becomes viable:

256GB unified memory
Efficient MoE routing (only load active experts)
Potential for 4-bit or even 5-bit/6-bit quantization

2-bit Quantization (Extreme Compression)

Memory Required: ~186 GB (0.25 bytes × 745B parameters)
Quality Impact: Significant degradation expected
Recommended Hardware:
- Mac Studio Ultra 256GB (comfortable fit)
- 3× A100 80GB GPUs (240GB)
Use Case: Experimentation only; quality likely insufficient for production

Mac Studio M3 Ultra Deployment (256GB RAM)

Can It Run GLM-5?

Current Status: No - local deployment not available yet

Future Outlook: YES, likely with quantization when open-weight release happens

Your Hardware Suitability

Mac Studio M3 Ultra (256GB unified memory):

Advantages

256GB unified memory - among highest consumer configurations
MLX framework - Apple Silicon optimizations proven with GLM-4.7
Unified memory architecture - no GPU-CPU transfer bottlenecks
Metal acceleration - efficient inference on Apple Silicon

Challenges

Size: 745B is massive compared to GLM-4.7’s 30B
Quantization required: 4-bit minimum, possibly 2-bit for comfortable fit
Performance unknown: No benchmarks available yet for Mac deployment

Recommended Configuration (When Available)

Option 1: Conservative 4-bit Deployment

Quantization: 4-bit (Q4_K_M via GGUF or MLX 4-bit)
Expected Memory: ~220-250GB with KV cache
Headroom: Limited (tight fit)
Expected Performance: Unknown, but likely slower than GLM-4.7 due to size
Context: May need to limit to 32K-64K tokens to fit

Option 2: Aggressive 2-bit Deployment

Quantization: 2-bit (Q2_K or IQ2_XXS)
Expected Memory: ~100-120GB
Headroom: Comfortable (136GB+ free)
Quality Impact: Significant degradation expected
Use Case: Experimentation, testing, non-critical applications

Option 3: Hybrid Approach (If Supported)

Strategy: Keep most-used experts in higher precision, others in lower
Memory: Variable (200-250GB)
Feasibility: Depends on implementation support (not yet available)

Performance Expectations (Extrapolated)

Based on GLM-4.7 benchmarks on M3 Max:

GLM-4.7 (30B): 35-55 tok/s on M3 Pro (36GB) with 4-bit
GLM-4.7 (30B): 70-100 tok/s on M3 Ultra with 4-bit (estimated)

GLM-5 (745B-A44B) extrapolation:

4-bit quantization: 10-25 tok/s on M3 Ultra (rough estimate)
2-bit quantization: 20-35 tok/s on M3 Ultra (with quality loss)
Active parameter advantage: Only 44B active could improve speed vs naive 745B

Caveat: These are speculative estimates. Actual performance will depend on:

MLX optimization quality for this model size
MoE routing efficiency
Memory bandwidth utilization
Quantization implementation

Comparison with GLM-4.7 on Mac Studio

Metric	GLM-4.7 (30B-A3B)	GLM-5 (745B-A44B)
Total Parameters	30B	745B
Active Parameters	~3B	~44B
Memory (4-bit)	~30GB	~220-250GB
Memory (Full BF16)	~64GB	~1,490GB
Mac Studio Ultra Fit	Excellent (4-bit)	Tight (4-bit only)
Est. Performance	70-100 tok/s	10-25 tok/s
Context Window	200K	200K
Current Availability	Available now	API only (Feb 2026)

Deployment Strategy for Mac Studio Ultra (When Available)

Step 1: Wait for Open-Weight Release

Monitor Hugging Face for official release
Watch for community quantization efforts (mlx-community)

Step 2: Start with Community Quantizations

Look for MLX-optimized 4-bit versions first
Test with GGUF Q4_K_M if MLX not available
Check community benchmarks before downloading

Step 3: Test Conservatively

Start with smaller context windows (8K-16K)
Monitor memory usage carefully
Expect longer load times than GLM-4.7

Step 4: Optimize if Needed

Reduce context window if memory pressure occurs
Consider 2-bit if 4-bit doesn’t fit comfortably
Wait for optimized MLX implementations

Quantization Options (Anticipated)

4-bit Quantization (Recommended When Available)

Quality: Minimal degradation for most MoE models
Speed: Good balance
Memory: ~220-250GB with overhead
Use case: Primary deployment target for Mac Studio Ultra
Formats: GGUF (Q4_K_M), MLX 4-bit (when released)

2-bit Quantization (Fallback)

Quality: Significant degradation expected
Speed: Better than 4-bit
Memory: ~100-120GB
Use case: If 4-bit doesn’t fit or for experimentation
Formats: GGUF (Q2_K, IQ2_XXS)

8-bit Quantization (Unlikely to Fit)

Quality: Near full precision
Speed: Slower than 4-bit
Memory: ~400-450GB with overhead
Feasibility: Does not fit on 256GB Mac Studio

Mixed Precision (Future Possibility)

Strategy: Higher precision for critical experts, lower for others
Memory: Variable
Availability: Depends on framework support

Deployment Methods (When Available)

1. MLX (Best for Apple Silicon - Recommended)

Framework: Apple’s Metal-accelerated ML framework
Performance: Proven optimization for M-series chips with GLM-4.7
Quantization: Native support for 4-bit, potentially 2-bit
Status: Will require GLM-5 MLX conversion (not yet available)

Installation (Future):

# Install MLX
pip install mlx mlx-lm

# Download GLM-5 MLX model (when available)
# Likely from mlx-community on Hugging Face

# Run with mlx-lm
mlx_lm.server --model mlx-community/GLM-5-4bit

2. Ollama (Easiest - When Supported)

Version required: TBD (current: 0.14.3+)
Features: Automates format handling, simplified deployment
Status: Will require Ollama to add GLM-5 support

Installation (Future):

# Pull GLM-5 (when available)
ollama pull glm-5

# Run the model
ollama run glm-5

3. llama.cpp (Advanced - Maximum Control)

Features: Direct GGUF model loading, extensive configuration
Performance: Excellent with proper optimization
Status: Requires GLM-5 GGUF conversion

Installation (Future):

# Download GGUF model (when available)
# Run with custom parameters
./llama-cli -m GLM-5-Q4_K_M.gguf -n 512 -c 32768 -ngl 1

Model Availability

Current Status (February 11, 2026)

API Access:

Z.AI Platform: https://z.ai/
WaveSpeed API: https://wavespeed.ai/

Local Deployment:

Not yet available
Open-weight release anticipated (based on Zhipu AI’s track record)

Anticipated Sources (Future)

Official Release (Expected):

Hugging Face: zai-org/GLM-5 (anticipated)
License: Likely MIT (based on GLM-4.7 precedent)

Community Quantizations (Expected):

GGUF: Community contributors (similar to AaryanK/GLM-4.7-GGUF)
MLX: mlx-community/GLM-5-4bit, mlx-community/GLM-5-8bit (anticipated)

On-Premise Deployment (Enterprise)

Zhipu AI offers on-premise deployment for enterprise customers:

Target: State-owned enterprises (SOEs), government agencies, financial institutions
2024 Revenue: RMB 263.7M (84.5% of total business)
Gross Margins: 80%+ for on-premise vs 0-5% for public API
Contact: Directly through Z.AI for enterprise licensing

Comparison with Other Models

GLM-5 vs GLM-4.7 Flash

Metric	GLM-4.7 Flash (30B-A3B)	GLM-5 (745B-A44B)
Total Params	30B	745B (24.8× larger)
Active Params	~3B	~44B (14.7× larger)
Context Window	200K	200K
Agentic Capabilities	Coding-focused	Built-in agentic intelligence
SWE-bench Verified	73.8% (SOTA for 30B)	TBD
Local Deployment	Yes (widely available)	Not yet available
Hardware Requirement	24GB+ (4-bit)	220GB+ (4-bit estimated)
Mac Studio Ultra Fit	Excellent	Tight (4-bit only)

GLM-5 vs Competitors (Frontier Models)

Direct Competition (Zhipu AI’s positioning):

GPT-5.2: Current SOTA for coding (35.6% on Vibe Code Bench)
Claude Opus 4.6: Anthropic’s flagship model
GLM-5: Positioned as open-weight alternative to both

MoE Architecture Comparison:

DeepSeek-V3.2: Similar MoE approach, strong coding performance
Qwen3-235B: Dense model, different architecture
GLM-5: 745B total, 44B active - unique balance

Expected Performance (Speculative): Given GLM-4.7’s strong showing (SOTA in 30B class) and Zhipu AI’s trajectory:

Likely competitive with GPT-5.2 and Claude Opus 4.6 on coding benchmarks
Built-in agentic capabilities from GLM-4.5 foundation (90.6% tool use)
Awaiting independent benchmarks

Use Cases & Recommendations

When to Use GLM-5 (Future)

Ideal Use Cases:

Advanced coding assistance - Evolution of GLM-4.7’s SOTA coding performance
Agentic workflows - Built-in agentic intelligence
Long-context reasoning - Full 200K token context
Privacy-sensitive applications - On-premise deployment option
Research and development - Frontier model with open weights (anticipated)

When to Choose GLM-5 Over Alternatives

Choose GLM-5 if:

Need SOTA coding + agentic capabilities in open-weight model
Require local deployment with frontier-level performance
Have sufficient hardware (256GB+ for 4-bit quantization)
Want commercial-friendly MIT license (if released as expected)

Choose Alternatives if:

GLM-4.7 Flash: Sufficient performance, much lower hardware requirements
GPT-5.2 or Claude Opus 4.6: Need absolute maximum performance (API only)
Qwen3 or DeepSeek-V3.2: Competitive performance with established local deployment
Smaller models (7B-30B): Hardware constraints or speed requirements

Mac Studio Ultra: Should You Wait for GLM-5?

Considerations

Advantages of Waiting:

Frontier-level performance potential
Built-in agentic capabilities
Your 256GB RAM should handle 4-bit quantization

Challenges:

Very tight memory fit (4-bit only)
Unknown performance on Apple Silicon
Slower inference than GLM-4.7 (likely 10-25 tok/s vs 70-100)
Not yet available for local deployment

Alternative Strategy:

Use GLM-4.7 Flash now - excellent performance, proven Mac compatibility
Monitor GLM-5 release - wait for open-weight version
Test when available - community benchmarks will clarify viability
Consider hybrid approach - use both for different tasks

Recommendation for Mac Studio Ultra Users

Current (February 2026):

Deploy GLM-4.7 Flash - available now, excellent performance, comfortable fit
Use your 256GB to run GLM-4.7 in full precision (BF16) or high-quality quantization
Expected performance: 70-100 tok/s

When GLM-5 Releases (Future):

Wait for community benchmarks - see actual Mac performance
Check 4-bit memory fit - confirm it runs comfortably
Compare performance - ensure it’s worth the upgrade
Consider use case - GLM-5 for complex agentic tasks, GLM-4.7 for speed

Likely Outcome:

GLM-5 will work on your Mac Studio Ultra (256GB)
Performance will be slower than GLM-4.7 due to size
Best use: Specialized tasks requiring frontier capabilities
Practical use: Keep both models, use appropriate one per task