124 tokens per second (TPS) is the model’s output decoding rate: after generation begins, it emits 124 tokens every second. Using OpenAI’s rough estimate of 0.75 English words per token, that is approximately 93 words per second. It describes generation speed—not answer quality, total request time, or cost—and it does not include the wait before the first token.
What 124 tokens per second actually measures
Tokens are the small text units a language model processes. A token can be a whole short word, part of a longer word, punctuation, whitespace, or a symbol. The token count is therefore not identical to the number of words or characters in a response.
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A throughput figure of 124 TPS normally refers to output decoding: the rate at which the model produces new output tokens once it has started generating. It is a generation-rate metric, not a score for intelligence, factual accuracy, reasoning quality, or instruction following. Two models can both sustain 124 TPS while producing very different answers.
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OpenAI describes this type of rate as the actual speed at which an LLM processes tokens, commonly expressed as tokens per minute or tokens per second. In a streaming interface, the client receives those tokens progressively. In a non-streaming interface, the service may generate at that rate internally but show nothing until the complete response is ready.
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How many words is 124 TPS?
OpenAI’s token guidance uses two useful approximations: one token is about four characters, and one token is about three-quarters of an English word. Applying the latter estimate:
- 124 tokens per second × 0.75 words per token = about 93 words per second.
- 93 words per second × 60 = about 5,580 words per minute.
These are planning estimates, not a universal conversion. Tokenization depends on the model’s vocabulary and encoding, language, spelling, punctuation, spaces, code, and the proportion of numbers or special characters. English prose with common words may be close to the estimate; source code, mathematical notation, and some other languages can produce a noticeably different words-per-token ratio.
How long do common answers take at 124 TPS?
For a response whose output length is known, divide output tokens by 124. The following figures describe decoding time only, beginning when the first output token is generated.
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|---|---|---|
| 100 tokens | 100 ÷ 124 | 0.81 seconds |
| 500 tokens | 500 ÷ 124 | 4.0 seconds |
| 1,000 tokens | 1,000 ÷ 124 | 8.1 seconds |
| 2,000 tokens | 2,000 ÷ 124 | 16.1 seconds |
| 4,000 tokens | 4,000 ÷ 124 | 32.3 seconds |
Round these values rather than treating them as a service-level promise. A provider can report an average 124 TPS while an individual request varies because of scheduling, thermal limits, batching, network transfer, or a changing model workload.
Why 124 TPS is considered very fast
Oracle’s generative-AI performance guidance uses 5 tokens per second as a reference point for matching average human reading speed and 15 tokens per second as an example of faster near-real-time dialogue. At 124 TPS, text can arrive far faster than most people can comfortably read it. A user interface may need buffering, a typewriter animation, a pause-and-read control, or other pacing rather than displaying every token at full speed.
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Fast decoding is most visible when the response is long or when a user is watching a stream. For a short answer, the initial wait and network round trips can dominate, so a very high TPS figure may make little perceptible difference. For batch generation, summarizing many documents, or producing long structured outputs, sustained throughput can reduce processing time substantially.
TPS is not the same as time to first token
Time to first token (TTFT) is the delay between submitting a request and receiving its first generated token. It includes work such as request queuing, loading or selecting the model, processing the prompt (prefill), and sometimes network overhead. TPS begins after that initial stage.
Inter-token latency (ITL) is the interval between successive output tokens. A nominal 124 TPS corresponds to an average ITL of roughly 8.1 milliseconds per token (1,000 milliseconds ÷ 124), but real streams can have bursts and pauses.
End-to-end latency combines TTFT, output decoding, and any remaining transfer or post-processing time. A useful approximation is:
end-to-end time ≈ TTFT + (output tokens ÷ TPS) + transfer and processing overhead
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For example, if a service reports 124 TPS and the request has a 1.2-second TTFT, a 1,000-token streamed answer would take approximately 1.2 + 8.1 = 9.3 seconds under those stated conditions. That is an arithmetic illustration, not a measured result for a particular model or provider.
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Per-user TPS versus total system TPS
A benchmark can use “TPS” to mean different things. NVIDIA distinguishes TPS per user from total TPS per system. Per-user TPS describes the stream seen by one request. Total system TPS is the combined output-token throughput across simultaneous requests.
Adding concurrent requests can increase aggregate throughput because the serving system keeps the accelerator busy and batches work. It can also make each user wait longer or receive tokens more slowly. Once GPU memory, compute, or scheduling capacity is saturated, additional concurrency may stop increasing total throughput and can eventually reduce it.
| Workload condition | Likely effect | What to measure |
|---|---|---|
| One active request | Shows interactive per-user behavior with little contention | TTFT, ITL, completion time, per-user TPS |
| Several simultaneous requests below saturation | Aggregate TPS may rise as hardware is utilized | Total TPS and each user’s p50/p95 latency |
| At or beyond saturation | Queues grow; per-user speed and tail latency worsen | Queue time, p95/p99 latency, dropped or timed-out requests |
This is why a single-user test can be much faster than the rate a production customer experiences, while a provider’s aggregate benchmark can look higher than any one user’s stream.
What must be disclosed for a credible 124 TPS benchmark?
“124 TPS” is incomplete without the conditions that produced it. When comparing models or providers, record every item below:
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- Model identity: exact model name, release or version, parameter scale, and context configuration.
- Hardware: accelerator type and count, memory, host CPU, and whether requests share the machine.
- Quantization: precision or quantized format, because lower precision can change speed and output behavior.
- Serving engine: inference runtime, compiler, batching strategy, and any speculative-decoding feature.
- Prompt length: input-token count and whether the prompt was reused from a cache.
- Output length: maximum and actual generated tokens; short completions can distort averages.
- Sampling settings: temperature, top-p, stop conditions, tool calls, and structured-output constraints.
- Concurrency: number of simultaneous requests and whether the result is per user or aggregate.
- Warm-up policy: whether the model and runtime were already loaded or the first request was included.
- Statistic and time window: average, median, p50, p95, or p99, and how many requests were measured.
Percentile latency is especially important. A p50 result describes the typical middle request, while p95 shows what at least 95 percent of requests beat and exposes tail behavior that an average can hide.
Why your own single-user speed may differ
A local test can legitimately disagree with a published figure even when both use the same nominal model. Check these causes in order:
- Separate TTFT from decoding. Start the TPS timer at the first token if you are reproducing a decoding benchmark; start at request submission if you want user-perceived latency.
- Count output tokens using the same tokenizer. Counting words or characters produces a different denominator.
- Match prompt and output lengths. A long prompt increases prefill work, while a short output gives too few tokens for a stable rate.
- Check concurrency and queueing. A shared endpoint may batch your request with others or delay it behind a queue.
- Confirm model and precision. A similarly named revision, quantized build, or fallback model can have different performance.
- Repeat after warm-up. The first request may include model loading, memory allocation, kernel compilation, or connection setup.
- Measure several runs. Record median and tail values rather than relying on one unusually fast stream.
Also account for client-side effects: buffering in an SDK, server-sent-event parsing, TLS and network delay, rendering in a browser, and any moderation or tool-processing stage can make a fast backend appear slower.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What 124 TPS means for different real-world tasks
Interactive chat
For chat, TTFT often matters more than peak TPS. Once the first token appears, 124 TPS can finish a short answer quickly, but the interface may need to pace the stream so users can follow it. A lower rate with a consistently low TTFT can feel more responsive than a higher rate preceded by a long queue.
Long-form drafting
Long outputs expose sustained decoding performance. A 2,000-token draft takes about 16.1 seconds of generation at 124 TPS before TTFT and transfer overhead. Editing, safety checks, tool calls, and context retrieval can add time outside that calculation.
Batch summarization
Batch jobs benefit from aggregate system TPS, provided the service can run enough concurrent requests without causing unacceptable tail latency. Measure total completed output tokens per minute and the completion time for the slowest documents, not only the fastest stream.
Code generation and structured output
Code and JSON can tokenize differently from ordinary English, so word-per-second estimates are unreliable. Validate both throughput and correctness: malformed syntax, missing fields, or retries can erase the productivity benefit of a fast initial generation.
How to compare two LLM performance claims
Use a controlled test that matches your workload rather than selecting the largest TPS number. Capture the following comparison axes in one record:
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| Axis | Question to answer |
|---|---|
| TTFT | How long until the first token at p50 and p95? |
| ITL and per-user TPS | How steady and fast is each individual stream? |
| End-to-end latency | How long from submission to the complete answer? |
| Aggregate TPS | How many output tokens per second across all concurrent users? |
| Concurrency | At what request count does performance saturate? |
| Prompt and output lengths | Were both systems tested with identical token counts? |
| Model and version | Are the capabilities and release revisions equivalent? |
| Quantization and hardware | Are precision, accelerator, and memory configurations comparable? |
| Serving and sampling | Are runtime, batching, temperature, and stop rules aligned? |
| Cost per useful task | What does a successful, accepted answer cost after retries and human review? |
Run representative prompts, discard or separately report warm-up behavior, and publish the number of repetitions. If one system produces shorter answers, compare a fixed output-token budget or normalize throughput; otherwise the apparent speed advantage may simply reflect less text being generated.
Does faster generation mean better quality or lower cost?
No universal rule connects TPS with quality or price. A faster model may be less accurate for your domain, require more retries, or produce outputs that need additional checking. A slower model may complete a task correctly on the first attempt and therefore cost less per accepted result.
Cost also depends on input tokens, output tokens, provider pricing, reserved versus on-demand capacity, concurrency limits, and the amount of post-processing. Evaluate cost per useful completed task, not speed in isolation. Include failed calls, retries, tool invocations, human review, and the value of meeting a latency target.
A practical test plan for a 124 TPS claim
- Choose a fixed set of prompts that represent your real requests, including short and long inputs.
- Tokenize each prompt with the target model and set an explicit maximum output length.
- Warm the service according to the provider’s documented procedure, then run separate cold-start and warm-start trials.
- Record submission time, first-token time, every-token arrival time, completion time, input tokens, and output tokens.
- Repeat at one, several, and production-level concurrent requests.
- Calculate per-user TPS from generated output tokens divided by decoding seconds, and aggregate TPS from all completed output tokens over the test window.
- Report p50, p95, and p99 TTFT and end-to-end latency, plus errors, timeouts, and retries.
- Repeat the same test after changing only one variable—hardware, quantization, model revision, or sampling—so the cause of any difference is identifiable.
This procedure turns an impressive headline number into a result you can relate to an actual user experience and operating budget.
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124 TPS tells you that decoding can be extremely rapid under a particular set of conditions. It does not tell you when the response begins, how the stream behaves under load, whether the answer is correct, or what a successful task costs. Treat the figure as one component of a latency profile: TTFT determines the initial wait, ITL determines stream smoothness, end-to-end latency determines completion time, and concurrency tests show whether the rate survives real demand.
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