Meta's Llama 4 models introduce a mixture-of-experts (MoE) architecture that activates only a portion of its parameters, significantly reducing computational needs. For example, Llama 4 Maverick has 400 billion parameters but activates only 17 billion at once. However, the models face challenges in utilizing their touted large context windows effectively, as many developers report encountering memory limitations. Third-party services have restricted the context size to as low as 128,000 tokens, revealing the vast resources required to run larger contexts effectively, evidenced by Meta’s own guidelines requiring multiple high-end GPUs for expanded token usage.
Meta's new Llama 4 models utilize a mixture-of-experts architecture, activating only relevant subsets of their parameters, which optimizes computational efficiency.
Despite boasting a 10 million token context window, developers face significant challenges using large contexts, often working with much lower limits due to memory constraints.
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