GLM-4-9B-Chat-1M
Parameters
9B
Context Length
1M
Modality
Text
Architecture
Dense
License
MIT License
Release Date
30 Jun 2024
Knowledge Cutoff
Jan 2024
API Pricing (per 1M)
Self-hosted only
System Requirements
VRAM requirements for different quantization methods and context sizes
1,024 tokens
Consumer
1x RTX 4090
24GB VRAM
Datacenter
1x NVIDIA A100
80GB VRAM
Apple Silicon
1x Apple M3 Max
128GB VRAM
1,000,000 tokens
Consumer
3x RTX 4090
24GB VRAM
Datacenter
1x NVIDIA A100
80GB VRAM
Apple Silicon
1x Apple M3 Max
128GB VRAM
Architecture Diagram
Evaluation Benchmarks
No evaluation benchmarks for GLM-4-9B-Chat-1M available.
Rankings
Overall Rank
-
Coding Rank
-
About GLM-4-9B-Chat-1M
GLM-4-9B-Chat-1M is a specialized open-weights model by Z.ai engineered for ultra-long document analysis and multi-hour transcript processing. It delivers high needle-in-a-haystack retrieval accuracy across a massive 1M token context window.
Technical Specifications
Attention
Attention Structure
Multi-Head Attention
Attention Heads
32
Key-Value Heads
2
Attention Head Dimension
128
Position Embedding
Absolute Position Embedding
RoPE Theta
-
Sliding Window Attention
No
Sliding Window Size
-
Sliding Window Ratio
-
Linear Attention
-
Linear Attention Ratio
-
Normalization
RMS Normalization
Activation Function
SwigLU
Dimensions
Hidden Dimension Size
4,096
Number of Layers
40
FFN Intermediate Size (Dense)
13,696
Multi-Token Prediction Heads
-
Tokenizer
Vocabulary Size
151,552
About GLM Family
General Language Models from Z.ai