Inside Grok-1: Elon Musk’s Open‑Source 314B LLM Architecture Revealed

Elon Musk’s AI startup xAI announced on March 17 that its first large language model, Grok‑1, is now open‑source.

Grok‑1 is a 314‑billion‑parameter mixture‑of‑experts model trained from scratch by xAI over three months, using a custom training stack built on Rust and the JAX deep‑learning framework.

The released checkpoint includes the model weights and network architecture under the Apache License 2.0, allowing commercial use, modification and distribution, but prohibiting trademark registration and providing no warranty.

Unlike some open‑source LLMs such as Gemma or Llama, Grok‑1 does not expose its full training data or code, limiting transparency compared with models like Pythia, Bloom or OLMo.

Key technical specifications:

Parameter count: 314 billion (86 billion active)

Mixture of 8 experts, 2 active per token

Tokenizer vocab size: 131 072

Embedding dimension: 6 144

Transformer layers: 64

Rotary positional embeddings size: 6 144

Context length: 8 192 tokens, precision bf16

Multi‑head attention: 48 query heads, 8 key/value heads, KV size 128

Dense feed‑forward block: widening factor 8, hidden size 32 768

The model weights (~300 GB) are available via a GitHub repository, with a magnet link for torrent download:

magnet:?xt=urn:btih:5f96d43576e3d386c9ba65b883210a393b68210e&tr=https%3A%2F%2Facademictorrents.com%2Fannounce.php&tr=udp%3A%2F%2Ftracker.coppersurfer.tk%3A6969&tr=udp%3A%2F%2Ftracker.opentrackr.org%3A1337%2Fannounce

Community response has been enthusiastic, with developers analyzing the model.py file and noting features such as GeGLU activation, sandwich normalization, and the use of rotary embeddings.

“Here’s your DEEP DIVE into @grok’s architecture! I just went through the model.py for this 314B open‑source behemoth with *no strings attached*.” – Andrew Kean Gao, Stanford CS student

Grok‑1 is positioned as a competitor to OpenAI’s ChatGPT, marketed as a more humorous and less censored alternative, though it still lacks real‑time internet access and the full training corpus.

Since its release, the repository has garnered over 22 k stars, indicating strong interest from the developer community.