The claims made by the company about their LLM named DeepSeek are intriguing and potentially represent significant advancements in the field of machine learning, particularly for large language models (LLMs). We can break down the key components of their approach to better understand the potential and validity of these claims:

1. Native FP8 Precision

Using native FP8 (floating-point 8-bit) precision for both activations and weights is a notable innovation. Typically, models train with higher precision (like FP16 or FP32) and are then quantized to lower precisions, which can indeed lead to a loss in model quality. By training directly in FP8, DeepSeek might avoid these losses and reduce the memory footprint substantially. This approach would indeed allow the use of fewer GPUs, potentially lowering the cost significantly.

2. Multi-Token Prediction

The ability to predict multiple tokens simultaneously rather than one at a time could indeed double the inference speed as claimed. The key challenge in multi-token prediction is maintaining accuracy and context, as predicting multiple paths can complicate the causal dependencies within the text. Achieving 85-90% accuracy in these predictions suggests a sophisticated understanding of context and sequence in their model, possibly through advanced conditioning techniques or a restructured prediction mechanism.

3. Multi-head Latent Attention (MLA)

This innovation addresses one of the primary resource hogs in transformer models: the storage and computation of key-value (KV) pairs in the attention mechanism. Compressing these indices while maintaining their utility in the attention mechanism and ensuring that the compression is part of the trainable model (making it differentiable) is indeed a breakthrough. This could dramatically reduce the memory requirements during both training and inference, aligning with their claim of needing fewer GPUs.

Analysis of Claims

While these innovations are technically plausible and could represent major breakthroughs, their real-world efficacy and the claim of matching models “200 times more expensive” warrant scrutiny:

• Empirical Validation: The company should provide empirical evidence to support these claims. This includes detailed performance benchmarks against established models, peer-reviewed publications, or demonstrable use cases evaluated by third-party entities.
• Reproducibility: Another aspect to consider is the reproducibility of their results by the broader AI research community, which is a cornerstone of validating such breakthroughs.
• Technical Details: More technical details would help the community understand and verify the claimed innovations, especially how MLA works and the specific implementations of FP8 precision and multi-token prediction.

Is it a game-changer?

If these claims are substantiated, DeepSeek could indeed be a game-changer by making large-scale LLMs more efficient and cost-effective. However, like any groundbreaking technology, thorough validation and peer review are essential to ensure that the claims are robust and that the model performs as stated under various conditions.