# How many tokens will an old 3090 produce? > Source: > Published: 2026-08-26 01:00:33+00:00 Oh. It depends on the backend you’re using, but that **seems to be about the expected speed**: 482 tokens in about 12 seconds works out to roughly **40 tokens/s**. If those 12 seconds are the actual generation/decode time, that number does not look suspiciously high for Qwen3.8-27B on a 3090. As a sanity check rather than an exact apples-to-apples benchmark, the current crowd-sourced [llamabench.ai results for Qwen3.8-27B on an RTX 3090](https://llamabench.ai/browse/rtx3090/qwen3-8-27b) include non-speculative runs at **38.2, 40.0, 41.1, and 45.6 tok/s**. Those runs use llama.cpp and somewhat different quant/cache settings, so I would not treat them as a reproduction of your Ollama run, but your ~40.2 tok/s sits right in that range. The cheapest way to verify what you are seeing is probably: ``` ollama run --verbose ollama ps ``` For the first command, the useful lines are approximately: ``` prompt eval count: ... prompt eval duration: ... prompt eval rate: ... eval count: ... eval duration: ... eval rate: ... tokens/s ``` Ollama’s [API usage documentation](https://docs.ollama.com/api/usage) makes the distinction explicit: `prompt_eval_*` is processing the input prompt.`eval_count` is the number of generated output tokens.`eval_duration` is the time spent generating those output tokens.`total_duration` also includes other work such as model loading and prompt processing.So if Ollama itself reports an `eval rate` around 40 tok/s, there is not much ambiguity left: you really are getting about 40 generated tokens/s. `ollama ps` gives the other high-value check. According to the [Ollama FAQ](https://docs.ollama.com/faq), `100% GPU` means the model is loaded entirely on the GPU, while a CPU/GPU percentage indicates that it is split between system RAM and VRAM. A useful decision tree is therefore: ``` Is ~12 s the generation time / Ollama eval_duration? | +-- Yes | | | +-- ~482 / ~12 s = ~40 tok/s | | | +-- ollama ps says 100% GPU | | | | | +-- This looks quite normal for this model/GPU class. | | | +-- CPU/GPU split | | | +-- Then context size/offloading becomes important | before comparing the number with other benchmarks. | +-- No / not sure | +-- Use --verbose (or the API metrics) once and compare eval_rate, rather than dividing visible tokens by wall-clock time. ``` In other words, I would not worry that the 3090 has somehow produced an impossible result. Its age is a bit misleading here: **24 GB of VRAM is still a very useful configuration for local inference**, because a quantized model of this size can fit without having to spill a large part of it into CPU memory. So, for the result you posted, my default interpretation would simply be: **yes, ~40 tok/s is believable.** If `ollama run ... --verbose` reports roughly that `eval rate` and `ollama ps` says `100% GPU` , I would consider the basic mystery solved. The fact that a 3090 is an older card does not prevent it from still being a very capable local-LLM card when a ~27B quantized model fits comfortably inside its 24 GB VRAM.