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In Richard Devine’s Windows Central example, changing Ollama’s context length from 8k to 4k doubled the reported generation rate—from 43 to 86 tokens per second—while running gpt-oss:20b on an RTX 5080. That is a result from one system and workload, not a guarantee for other hardware or models. The setting to examine is num_ctx, which controls how much information the model can handle in an interaction.

What setting changed the speed?

Ollama’s context length, commonly set with num_ctx, determines how much text the model can consider at once, including conversation history and documents. A larger window can accommodate more material, but it also increases resource demands. If your task is short, a very large context may use memory without providing a practical benefit.

Devine described testing gpt-oss:20b on an RTX 5080. For the short prompt “How much wood would a woodchuck chuck if a woodchuck could chuck wood?”, he reported an evaluation rate of 9 tokens per second at 64k context, 43 at 8k, and 86 at 4k. He also reported 100% GPU use at 4k and 93% at 8k. These are his readings from that particular setup, not standardized benchmark results; the article’s publication year is not confirmed.

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Context length Reported evaluation rate Reported GPU use
64k 9 tokens per second (Richard Devine, Windows Central; gpt-oss:20b on RTX 5080) Not stated in the report
8k 43 tokens per second (same reported setup) 93% (same reported setup)
4k 86 tokens per second (same reported setup) 100% (same reported setup)

The 8k-to-4k result is the specific comparison behind the “doubled” claim. It does not establish that halving context will double speed elsewhere: model, prompt, hardware, memory, software version, and workload all affect performance. Devine’s conclusion was that lowering context can yield better performance when a user is not handling large amounts of data or very long conversations; treat that as guidance from his example, not a universal rule.

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Why can a smaller context window help?

Context takes memory. Reducing it can leave more room for the model and its runtime, and on some workloads may help keep more computation on the GPU or improve generation speed. The trade-off is capacity: if the conversation or document exceeds the available window, the model cannot consider all of it in one interaction.

Ollama’s FAQ explains that its loader evaluates a model’s VRAM needs against memory available at load time. If the model fits on one GPU, Ollama loads it there; if it does not fit on a single GPU, it may distribute it across available GPUs. A CPU/GPU split can therefore be a useful clue when investigating memory pressure, but it is not by itself a complete performance diagnosis.

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How to find a useful context length

  1. Start from the task. For short questions or small inputs, try a modest context rather than assuming the largest available window is best. For long documents or extended conversations, preserve enough room for the material you need the model to consider.
  2. Check the run’s diagnostics. Observe the processor split and tokens-per-second output for the same task. These figures describe that run; they are not directly comparable to a different model, prompt, or machine.
  3. Reduce context in steps. Repeat the same task at each setting, noting speed and GPU/CPU placement. Check that the model still has enough input window and that the answers remain adequate for your use.
  4. Choose the smallest setting that fits reliably. A short prompt may work well with less context, while a long document may require a larger window. The right balance is the lowest setting that still handles the complete task well.
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What if the model still strains memory?

Ollama’s FAQ also describes Flash Attention, which can significantly reduce memory use as context grows, and KV-cache quantization options with different memory requirements and possible quality effects. Availability and behavior depend on Ollama release, hardware, model, and task, so consult the current Ollama FAQ for supported options before changing them, then check the resulting output quality.

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If the model and context you need still do not fit after tuning, additional GPU VRAM may help with capacity. More VRAM alone does not promise higher generation speed, and the available evidence does not identify one GPU as best for every local-AI workload.

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