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Llama 3 70B

Parameters

70B

Context Length

8K

Modality

Text

Architecture

Dense

License

Meta Llama 3 Community License

Release Date

18 Apr 2024

Knowledge Cutoff

Dec 2023

System Requirements

VRAM requirements for different quantization methods and context sizes

1,024 tokens

148.85 GB VRAM

Consumer

7x RTX 4090

24GB VRAM

Datacenter

2x NVIDIA A100

80GB VRAM

Apple Silicon

2x Apple M3 Max

128GB VRAM

8,192 tokens

151.32 GB VRAM

Consumer

8x RTX 4090

24GB VRAM

Datacenter

2x NVIDIA A100

80GB VRAM

Apple Silicon

2x Apple M3 Max

128GB VRAM

Architecture Diagram

Input TokensToken EmbeddingPosition: RoPEHidden: 8.2k · Context: 8Kx 80 layersNormPre-AttentionGrouped-Query Attention64Q / 8KV headsHead dim: 128+NormPre-FFNFeed-Forward NetworkActivation+Final NormOutput Logits

Evaluation Benchmarks

Rank

#122

BenchmarkScoreRank

General Text

Text Arena

1276

130

Rankings

Overall Rank

#122

Coding Rank

-

About Llama 3 70B

Meta Llama 3 70B is a 70-billion-parameter, decoder-only transformer language model developed by Meta. Released in April 2024, it is provided in both pre-trained and instruction-fine-tuned variants. The instruction-tuned model is specifically optimized for dialogue and assistant-style interactions, supporting a wide array of natural language understanding and generation tasks. These include conversational AI applications, creative content generation, code generation, text summarization, classification, and complex reasoning challenges. The model is made available for both commercial and research applications under the Meta Llama 3 Community License.

Architecturally, Llama 3 70B employs a standard decoder-only transformer design. A key innovation is its tokenizer, which features a vocabulary size of 128,000 tokens, contributing to enhanced language encoding efficiency and optimized inference. To further improve inference scalability and speed, the model integrates Grouped Query Attention (GQA). This attention mechanism is applied across both the 8B and 70B parameter versions of Llama 3. Initial training of the model was conducted on sequences up to 8,192 tokens. For the instruction-tuned variants, supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) were utilized to align model outputs with human preferences for helpfulness and safety.

The Llama 3 70B model is engineered for general-purpose applications, serving as a foundational technology that can be further adapted for domain-specific tasks. Its capabilities extend to powering advanced assistant functionalities, as demonstrated by its integration into Meta AI applications across various platforms. The model's design focuses on enabling developers to build diverse generative AI applications, from complex coding assistants to long-form text summarization tools, while offering control and flexibility in deployment environments, including on-premise, cloud, and local setups.

Technical Specifications

Attention

Attention Structure

Grouped-Query Attention

Attention Heads

64

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

-

Activation Function

-

Dimensions

Hidden Dimension Size

8,192

Number of Layers

80

FFN Intermediate Size (Dense)

-

Multi-Token Prediction Heads

-

Tokenizer

Vocabulary Size

-

About Llama 3

Meta's Llama 3 is a series of large language models utilizing a decoder-only transformer architecture. It incorporates a 128K token vocabulary and Grouped Query Attention for efficient processing. Models are trained on substantial public datasets, supporting various parameter scales and extended context lengths.


Other Llama 3 Models
Llama 3 70B: Specifications and GPU VRAM Requirements