Llama 3.1 8B
Parameters
8B
Context Length
131K
Modality
Text
Architecture
Dense
License
Llama 3.1 Community License
Release Date
23 Jul 2024
Knowledge Cutoff
Dec 2023
API Pricing (per 1M)
Input: $0.02 · Output: $0.05
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
131,072 tokens
Consumer
2x RTX 4090
24GB VRAM
Datacenter
1x NVIDIA A100
80GB VRAM
Apple Silicon
1x Apple M3 Max
128GB VRAM
Architecture Diagram
Evaluation Benchmarks
Rank
#172
| Benchmark | Score | Rank |
|---|---|---|
Professional Knowledge | 0.483 | 46 |
Graduate-Level QA | 0.304 | 106 |
Coding Index | 0.05 | 136 |
General Text | 1211 | 157 |
Intelligence Index | 0.07 | 198 |
StackEval Archived | 0.502 | 17 |
Summarization Archived | 0.491 | 27 |
General Knowledge Reference | 0.694 | 27 |
Rankings
Overall Rank
#172
Coding Rank
#130
About Llama 3.1 8B
Llama 3.1 8B is an efficient open foundation model by Meta engineered for multilingual dialogue, classification, and edge deployment across 8 languages. It features an expanded 128K token context window and enhanced tool-calling capabilities.
Technical Specifications
Attention
Attention Structure
Grouped-Query Attention
Attention Heads
32
Key-Value Heads
8
Attention Head Dimension
-
Position Embedding
ROPE
RoPE Theta
-
Sliding Window Attention
-
Sliding Window Size
-
Sliding Window Ratio
-
Linear Attention
-
Linear Attention Ratio
-
Normalization
RMS Normalization
Activation Function
-
Dimensions
Hidden Dimension Size
4,096
Number of Layers
32
FFN Intermediate Size (Dense)
-
Multi-Token Prediction Heads
-
Tokenizer
Vocabulary Size
-
About Llama 3.1
Llama 3.1 is Meta's advanced large language model family, building upon Llama 3. It features an optimized decoder-only transformer architecture, available in 8B, 70B, and 405B parameter versions. Significant enhancements include an expanded 128K token context window and improved multilingual capabilities across eight languages, refined through data and post-training procedures.