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If Ollama seems to have forgotten the beginning of a long prompt, first separate three different values: what the model can support, what Ollama is configured or able to allocate, and the num_ctx value a frontend may send with each request. They are related, but they are not interchangeable. This diagnostic guide shows how to inspect those layers and identify a likely configuration mismatch without claiming to know which exact tokens were discarded.
What “context length” means—and why the values differ
Ollama defines context length as “the maximum number of tokens that the model has access to in memory.” The model’s advertised capability is not proof that a particular run has that much context available. Ollama’s defaults and runtime allocation, plus any request-level override from a client, determine what is usable for a given interaction.
Ollama’s current context-length documentation, checked in 2026, lists these VRAM-based defaults:
| Available VRAM | Ollama documented default context |
|---|---|
| Below 24 GiB | 4k |
| 24–48 GiB | 32k |
| 48 GiB or more | 256k |
These are documented defaults, not a guarantee of the context for every model, Ollama version, or request. Ollama’s FAQ also states a 4096-token default. Because these pages frame the default differently, do not treat either figure as a universal value: record the version and inspect the settings and runtime for the installation you’re diagnosing. See Ollama’s context-length documentation and Ollama’s FAQ.
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How to scan the configuration and runtime
Use the same route that produced the unexpectedly short interaction. A setting in one interface may not describe requests sent by another. Record the model and Ollama version, then check each layer that you can access.
- Identify the route. Note whether the request comes from the Ollama app, CLI, API, or a frontend such as Open WebUI. Record the model and Ollama version.
- Inspect the server setting. Check whether
OLLAMA_CONTEXT_LENGTHis set in the environment used by the running Ollama service. The location differs by operating system and launch method: the macOS app, a Linux systemd service, and a Windows process do not necessarily inherit the same environment. Follow the relevant instructions in Ollama’s FAQ. - Check for request-level settings. Look for
num_ctxin the request options or the frontend’s model preset and chat advanced parameters. CLI users can inspect or set it with/set parameter num_ctx. The API acceptsoptions.num_ctx. An explicit request value can override the server default. - Inspect the running allocation. Run
ollama pswhile the model is loaded. Compare itsCONTEXTvalue with the intended setting, and inspectPROCESSORto see whether processing is allocated to CPU, GPU, or both. Ollama documents this command as the runtime check; see the context-length guide. - Write down what was observable. Report the server default, any request override you found, and the runtime context shown by
ollama ps. If the frontend or request payload is hidden from the scanner, say that the request-level value could not be inspected.
Does num_ctx override OLLAMA_CONTEXT_LENGTH?
It can. The server environment variable sets a server-side context length, but a client may include num_ctx with each request. Open WebUI documents that a value in its model preset or chat advanced parameters is sent on every request and overrides OLLAMA_CONTEXT_LENGTH. Therefore, a server configured for a larger context does not by itself rule out a smaller frontend setting.
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Open WebUI also documents a 2048-token prefill when its num_ctx control is toggled, which can cap context below the value a user intended. Check the actual preset and chat parameters rather than assuming the control is unset. This behavior is specific to Open WebUI’s documented interface, not a rule for every Ollama client. See Open WebUI’s Ollama provider guide.
What the scanner can—and cannot—conclude
If a visible request override is lower than the server setting, or the runtime CONTEXT is lower than intended, the evidence supports a likely context-configuration issue. A scanner should name the mismatch and the layer it observed, not claim that it identified the exact point where a prompt was cut off.
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“Silent truncation” is documented for a specific case: Open WebUI says an undersized context silently truncates the prompt. That does not establish that every Ollama client behaves the same way, discards the same portion, or omits an error. If the configuration and runtime values look right, the available evidence here does not distinguish context limits from application-side trimming, prompt formatting, tokenization, or model-specific limits; investigate those separately rather than treating them as confirmed causes.
Should you raise the context length?
Not automatically. Larger contexts use more memory, and concurrent requests increase the requirement. Ollama documents memory scaling with OLLAMA_NUM_PARALLEL * OLLAMA_CONTEXT_LENGTH; a higher context setting can therefore affect whether a machine can handle its concurrency target. Check available CPU and GPU memory and the PROCESSOR allocation in ollama ps before increasing the value. Ollama suggests at least 64000 tokens for tasks such as web search, agents, and coding tools, but that is a product recommendation—not a guarantee that every model or machine can support it. See Ollama’s context-length documentation and its FAQ.
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