What the Leaked Llama 3.1 405B Reveals About Meta’s Newest LLM

On the early morning of July 23, a user reported that Meta’s upcoming Llama 3.1 405B model had been leaked on 4chan and that preliminary benchmarks suggested it could surpass GPT‑4o on many tests. The leak also hinted at an imminent official release of the largest 405B model and a 70B variant.

The leaked repository on GitHub is now 404, but archived download links indicate a file size of roughly 763.84 GB. A Hugging Face repository appeared earlier but was subsequently removed, apparently because the owner failed to privatize it in time.

According to the leaked model card, the Llama 3.1 family includes 8B, 70B, and 405B parameter models for both pre‑training and instruction‑tuned variants. The instruction‑tuned models are optimized for multilingual dialogue and claim to outperform many open‑source and closed‑source chat models on standard industry benchmarks.

Meta Llama 3.1 multilingual large language model (LLM) collection comprises 8B, 70B, and 405B sized pre‑training and instruction‑tuned models. The instruction‑tuned pure‑text models (8B, 70B, 405B) are optimized for multilingual conversation use cases and outperform many available open‑source and closed chat models on common industry benchmarks.

Benchmark tables released with the leak show the 405B model achieving state‑of‑the‑art (SOTA) scores on several tests, comparable to GPT‑4o and Sonnet 3.5, while the 70B model displayed an unexpected regression on the HumanEval code‑generation benchmark.

Performance notes indicate that the 70B model can infer at roughly three times the cost of GPT‑4o mini, with noticeably poorer coding ability. The 405B model also trails GPT‑4o on HumanEval despite strong overall scores.

Comparisons between Llama 3.1 and its predecessor 3.0 suggest a substantial improvement for the 8B variant, modest gains for 70B, and the 405B still lagging behind the flagship models in certain areas.

The model card discloses that Llama 3.1 was trained on about 15 trillion tokens from publicly available sources, with fine‑tuning data that includes over 25 million synthetic examples. Pre‑training data was collected up to December 2023.

Technical details reveal a self‑regressive transformer architecture employing Grouped‑Query Attention (GQA) for scalable inference. Training used Meta‑custom GPU clusters (H100‑80GB) consuming 39.3 million GPU‑hours and an estimated 11,390 tons of CO₂‑equivalent emissions.

While the model weights are released for free, running the 405B model demands high‑end hardware (e.g., multiple H100 GPUs), making it impractical for most individual developers. The 70B variant may be more accessible on consumer‑grade hardware.

Safety guidance in the model card emphasizes that Llama 3.1 is not intended for isolated deployment; it should be integrated into broader AI systems with additional safeguards. Developers are urged to implement tool‑use policies, evaluate third‑party services for integrity, and conduct thorough security testing before production use.

Tool use: Developers must define clear integration strategies and assess the security of any third‑party services used with the LLM.

Multilingual support: Although Llama 3.1 supports eight languages (English, German, French, Italian, Portuguese, Hindi, Spanish, Thai), performance on non‑English outputs may not meet safety or usefulness thresholds.

References:

https://x.com/mattshumer_/status/1815444612414087294

https://pastebin.com/9jGkYbXY