Large language models (LLMs) have revolutionized how we interact with technology, but their impressive capabilities come at a cost. Generating text with LLMs is computationally expensive, demanding significant time and energy. Each word in a sentence is usually generated one by one, creating a bottleneck for real-time applications. Imagine a writer painstakingly crafting a sentence word by word, consulting a thesaurus for each choice. Traditional LLM inference works much the same way, generating one token (word) at a time. This process, called autoregressive decoding, is inherently slow. Researchers have explored faster methods like speculative decoding, where a "draft model" predicts several tokens, which a larger "editor model" verifies. While this speeds things up, each word is still chosen in isolation, potentially creating nonsensical phrases. A new approach called multi-token joint decoding (MTJD) offers a solution. MTJD analyzes multiple tokens simultaneously, considering the likelihood of entire phrases and even sentences at once. This reduces output perplexity—a measure of how predictable text is, with lower perplexity often correlating to better quality. However, pure MTJD is too complex for current hardware. This research introduces multi-token assisted decoding (MTAD), a method that borrows from speculative decoding to make MTJD practical. MTAD uses a smaller, faster auxiliary model to draft multiple tokens from their joint distribution (like MTJD), then verifies the draft tokens in parallel with the larger model. The key innovation is to accept the *longest coherent sub-sequence* among the drafts. Theoretical analysis and empirical results confirm the advantages of MTAD. Experiments on various tasks, including chat, summarization, text-to-SQL, and challenging benchmarks like MT-Bench, reveal that MTAD lowers perplexity by an average of 21.2% compared to traditional methods. Furthermore, MTAD achieves a speed-up of 1.42x and reduces energy consumption by a substantial 1.54x compared to existing speculative decoding methods. This research paves the way for more efficient and sustainable use of LLMs. By generating text in chunks rather than individual words, MTAD represents an essential step toward faster and more articulate language generation. The method also opens new possibilities for real-time LLM applications in areas such as chatbots, translation, and content creation.