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124 tokens per second (TPS) means a language model is generating 124 output tokens every second while it decodes a response. Using OpenAI’s rough English estimate of 0.75 words per token, that is approximately 93 English words per second, or about 5,580 words per minute. The conversion is only an estimate: tokenization varies by model, encoding, language, spelling, punctuation and spaces.
For a response that contains N output tokens, the decoding portion takes approximately N ÷ 124 seconds. A 500-token answer therefore takes about 4.0 seconds to generate, 1,000 tokens about 8.1 seconds, and 2,000 tokens about 16.1 seconds. Those figures begin when decoding starts; they do not include the wait for the first token, prompt processing, queueing or network transfer.
What 124 TPS actually measures
TPS is a generation-rate metric. It describes how quickly the serving system emits output tokens after it has begun decoding. It does not measure factual accuracy, reasoning quality, instruction following, safety, uptime or the total time a user waits for a completed answer.
A token is a piece of text chosen by the model’s tokenizer. A token can be a whole short word, part of a longer word, punctuation, whitespace or a symbol. In English, OpenAI’s token guidance gives two useful approximations: one token is roughly four characters and roughly three-quarters of a word. Neither is a fixed conversion. Code, numbers, uncommon names, non-English languages and heavily punctuated text can produce very different ratios.
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Output speed, not prompt speed
When a service advertises 124 TPS, check whether it means output tokens only. Prompt tokens are processed during the prefill stage, before streamed decoding. A long prompt can add substantial time even when the decoder maintains 124 TPS. Some dashboards also report a system-wide total, while others report the rate observed by one request. Those are different measurements.
How long common responses take at 124 TPS
The following calculation uses only the advertised decoding rate. It assumes the entire response is generated at 124 TPS and rounds to one decimal place.
| Output length | Decoding time at 124 TPS | Approximate English words |
|---|---|---|
| 100 tokens | 0.8 seconds | 75 words |
| 500 tokens | 4.0 seconds | 375 words |
| 1,000 tokens | 8.1 seconds | 750 words |
| 2,000 tokens | 16.1 seconds | 1,500 words |
| 4,000 tokens | 32.3 seconds | 3,000 words |
The word column is an English-language approximation, not a promise about the response’s visible word count. The token count is the quantity that controls the timing calculation.
Is 124 tokens per second fast?
For one streaming response, yes. Oracle’s performance guidance uses 5 tokens per second as an example for matching average human reading speed and 15 tokens per second as a faster near-real-time dialogue target. At 124 TPS, text can arrive much faster than most people can comfortably read it.
That does not mean the user experiences a 124-TPS conversation. A chat interface may buffer output, render it in chunks, wait for moderation, run tools between model calls or pause while the model produces a final answer. The user’s practical experience depends on when the first useful text appears and how the interface presents subsequent text.
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Why a faster stream can feel worse
If every token is displayed immediately, the response may scroll faster than the reader can follow. Applications often improve usability by rendering complete words or short groups of tokens, keeping the viewport stable, allowing pause and resume, and exposing a stop button. A high decoder rate is valuable because it lets the application choose a comfortable presentation speed without making the model itself slow.
TPS, TTFT, inter-token latency and total latency
Several timing metrics answer different questions. Treating them as interchangeable is a common source of misleading comparisons.
Time to first token (TTFT)
TTFT is the delay from submitting a request until the first output token becomes available. It includes work such as request routing, queueing and prompt prefill, depending on how the provider defines the measurement. A model can have excellent TPS but a long TTFT, making the first response feel slow.
Inter-token latency (ITL)
ITL is the spacing between successive output tokens. A nominal 124 TPS corresponds to an average spacing of approximately 8.1 milliseconds per token because 1 ÷ 124 seconds is about 0.0081 seconds. Real streams can arrive in bursts, so an average TPS does not guarantee that every gap is exactly 8.1 milliseconds.
End-to-end latency
End-to-end latency is the elapsed time from request submission to completion. A simplified planning equation is:
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Suppose a service reports 124 TPS and a 1.2-second TTFT. A 1,000-token streamed answer would require approximately 1.2 + 8.1 = 9.3 seconds before completion, plus any unmeasured client or network overhead. This is an arithmetic illustration under the stated conditions, not a measured result for a particular model or provider.
Why your single-user result may differ from a benchmark
Benchmarks often describe either the speed of one request or the total output of many simultaneous requests. NVIDIA’s metrics documentation distinguishes TPS per user from total TPS per system; total system TPS is the output-token throughput across all simultaneous requests.
As concurrency rises, aggregate throughput can increase because otherwise idle GPU capacity is used more efficiently. At the same time, each user may wait longer for tokens. Once the hardware reaches saturation, adding requests can reduce total throughput as scheduling, memory pressure and contention grow.
| Measurement | What it answers | What can happen under load |
|---|---|---|
| Per-user TPS | How quickly one request receives output | Usually declines as competing requests consume resources |
| Aggregate system TPS | How many output tokens the whole service produces per second | May rise with concurrency, then flatten or fall after saturation |
| TTFT | How long a user waits for the first token | Often increases when requests queue or prompt workloads grow |
| End-to-end latency | How long the complete request takes | Reflects TTFT, generation time and transport overhead together |
A local test with one short request is therefore not directly comparable with a vendor’s multi-user throughput number. Conversely, a system-wide 124 TPS result does not mean every user receives 124 TPS.
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What a credible 124-TPS claim should disclose
TPS has meaning only alongside the conditions that produced it. Before comparing two numbers, look for these details:
- Model identity: exact model name, version, parameter size and context configuration.
- Hardware: GPU or accelerator type, number of devices, memory and any partitioning.
- Quantization: precision or quantization method, since reduced precision can change both speed and output behavior.
- Serving engine: the inference runtime, batching strategy and any acceleration features.
- Prompt and output lengths: input-token count and generated-token limit; short prompts and short outputs can produce very different results from long ones.
- Sampling settings: temperature, top-p, stop conditions and other settings that influence whether generation ends early.
- Concurrency: number of simultaneous requests and whether the result is per user or aggregate.
- Warm-up policy: whether the first request was excluded and whether weights or compiled kernels were already resident.
- Latency statistics: p50 or p95 TTFT and completion latency, not only a single average.
- Measurement boundary: whether tokenization, network transfer, moderation and client rendering were included.
Without those qualifications, “124 TPS” is a data point rather than a reproducible performance result.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What 124 TPS means for different LLM workloads
Interactive chat
For ordinary chat, TTFT often matters more than maximum stream speed. Once text is arriving at 124 TPS, the interface may deliberately throttle display to a readable pace. The high rate still shortens the period during which the model occupies a connection and lets the application finish a moderate answer quickly.
Code generation and editing
Fast decoding can make short completions feel immediate, but total latency may be dominated by repository search, file retrieval, tool calls, compilation or test execution. Measure the complete edit-and-verify loop rather than model streaming alone.
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An agent may make several model calls, each with its own TTFT and prompt-prefill cost. If a workflow performs six calls, shaving a few seconds from each decoding phase can help, but a slow external tool or a large accumulated prompt can dominate the run. Record latency per step as well as total task time.
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Batch processing
For summarization or extraction jobs, aggregate system TPS and cost per completed item are usually more important than the speed of one stream. Higher concurrency can improve overall throughput until the serving hardware saturates, so test at the concurrency your workload actually needs.
What 124 TPS does not prove about quality or cost
Speed alone does not establish that a model is more accurate, reasons better, follows instructions more reliably or produces safer answers. The cited performance guidance defines throughput and latency measurements; it does not provide a universal rule that a faster model is better for a task.
Nor does TPS determine cost. Pricing may depend on input tokens, output tokens, requests, reserved hardware or time. A fast model can still be expensive if it uses costly hardware or requires repeated retries. A slower model can be cheaper per useful completed task if it solves the task correctly in one pass. Compare quality on your own workload, failure and retry rates, and the cost of a successful result.
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- Record the request: save the exact model version, prompt, system instructions, output limit and sampling settings.
- Separate timing stages: timestamp request submission, first received token, each streamed chunk and final completion.
- Count tokens consistently: use the tokenizer associated with the model rather than estimating visible words.
- Run warm and cold tests: report whether the model was already loaded and whether the first request was discarded.
- Test realistic prompts: include the input lengths, tool context and output sizes your application normally sends.
- Vary concurrency: measure one, several and peak simultaneous requests, recording both per-user and aggregate results.
- Report percentiles: include p50 and p95 TTFT and completion latency so occasional queueing is visible.
- Check useful completion: track correctness, retries, tool failures and cost alongside speed.
A result is actionable when another operator can reproduce the same workload and understand exactly what the reported number includes.
Common mistakes when interpreting 124 TPS
- Converting tokens to words as if the ratio were exact: use the token count for timing and treat word estimates as approximate.
- Calling 124 TPS the total response time: add TTFT, queueing, prompt processing and transport overhead.
- Comparing unlike benchmarks: align model version, hardware, quantization, prompt length, output length, concurrency and sampling settings.
- Assuming aggregate TPS is per-user speed: identify whether the metric covers one stream or all simultaneous requests.
- Ignoring saturation: test the operating point where your service must remain responsive, not only an unloaded demo.
- Using speed as a quality proxy: evaluate task success and cost per useful result independently.
The practical verdict
124 TPS is an exceptionally high single-stream decoding rate: roughly 8.1 milliseconds per output token and about 8.1 seconds of decoding for 1,000 tokens. It can make generation effectively instantaneous from a human-perception standpoint, but only after decoding begins. To predict what users will experience, pair TPS with TTFT, prompt size, concurrency, percentile latency and the application’s own network, tool and rendering overhead.
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