标签：#tokens

What tok/s really measures, how concurrency changes it, and why a single-user benchmark is not the whole story for local LLM performance. A Few Moments Later… How Fast Is "Fast"? Every interface in the world of local AI eventually shows you that dreaded spinner, and on the wrong setup it sits there long enough that your brain supplies the meme: "A few moments later…" That pause is a number wearing a disguise. Somewhere inside your machine, the model is grinding out tokens — fragments of words — and the only question that matters is how many of them it produces per second. Tokens per second (tok/s) is the universal speedometer of local LLMs, quoted in every benchmark and every GPU review. But it is also one of the most misleading numbers in the field, because the same model can measure 45 tok/s or 793 tok/s depending on how you test it. This guide explains what the number actually means, why it moves so dramatically, and how to read a benchmark without fooling yourself. What a Token Actually Is Before speed makes sense, the unit has to. Models do not read words; they read tokens, which are chunks of text roughly three-quarters of a character on average in English. The word "calculator" might be one token or three, depending on the tokenizer, and this is not idle trivia — it is the reason the same prompt can cost a different amount across providers, as the Token Counter Calculator shows in practice. Because tokens are the unit of both billing and speed, "tokens per second" is the single number that connects all three corners of the local AI decision: how fast the model answers (tok/s), how big the model is (parameters), and what it costs to run (hardware amortized over time). A model doing 50 tok/s reads roughly 100-150 words per second — comfortably faster than you can read. A model stuck at 5 tok/s feels like a slow internet connection in 1998. The Single-User Number Is Not the Whole Story Here is the trap: most consumer benchmarks report tok/s at one user, one requ

Troops received an email informing them that they were rapidly depleting their AI tokens.

Instagram head Adam Mosseri believes companies will eventually need to manage AI token spending the same way they manage payroll or other operating expenses, predicting that engineers could soon face limits on how much they spend using AI tools.

Introduction Although we interact with LLMs using natural language, these models never processes raw text directly. Before a prompt reaches the model, it is converted into a sequence of tokens , the fundamental units that the model understands. Tokenization is one of the earliest stages of the inference pipeline and influences everything from context windows and API pricing to latency and memory usage. What Is a Token? A token is the smallest unit of text processed by a language model, it is not necessarily a word. Depending on the tokenizer, a token may represent: an entire word part of a word punctuation whitespace numbers symbols emojis Different models use different tokenizers, so the same text may be split differently depending on the model. Why Tokens? Simply because language models operate on numbers, not text. Before the transformer can perform any computation, the input must be converted into a numerical representation. The preprocessing pipeline looks like this: Raw Text │ ▼ Tokenizer │ ▼ Tokens │ ▼ Token IDs │ ▼ Embedding Layer │ ▼ Embedding Vectors │ ▼ Transformer The tokenizer splits the input into tokens and each token is then mapped to a unique integer called a token ID , which are passed through the model's embedding layer, which converts them into dense vectors that become the actual input to the transformer. A Real Example Instead of using hypothetical examples, let's look at how OpenAI's tokenizer processes text. Input: I have no enemies. OpenAI tokenizes it to: ["I", " have", " no", " enemies", "."] with the following token IDs: [40, 679, 860, 33974, 13] that have been generated by OpenAI Tokenizer for the "GPT-5.x & O1/3" models. The transformer never sees the original sentence, it only receives the corresponding sequence of token IDs. Token IDs After tokenization, every token is replaced with an integer. Conceptually: " have" → 679 " no" → 860 " enemies" → 33974 ... The exact numbers differ between models because each tokenizer has its own voca

The most AI-obsessed firms are spending roughly $7,500 monthly per employee on AI, per Ramp AI Index. That's not more than an engineer's salary — yet.

"The whole conversation shifted from tokenmaxxing and 'go fast' to 'we need guardrails, how do we control this?'"

Ramp has nearly tripled its valuation over the past year as investors scramble to grab a part of the fast-growing startup.

Large exchanges are designing derivative products around AI tokens, which are increasingly being considered less a computational output and more a raw material input, like electricity or bandwidth.