A local language model can fail because its knowledge is out of date, because it declines to answer, because it invents a fact, or because it mishandles a search tool. Those failures may look similar in a chat window, but they are not the same problem—and they need different fixes. In a 13-question home-lab test, XDA author Joe Rice-Jones reported far more refusals or gaps in current knowledge than fabrications. The test still caught one invented Proxmox command, and adding web search helped only when the model and search pipeline worked together.
What the 13-question test actually found
Rice-Jones tested a setup using Lemonade to serve models, Crush as a terminal harness, and a self-hosted answering engine for retrieval. The questions covered recent releases, changing facts, exact versions, stable knowledge, and deliberately invented products or commands. He compared direct local inference with a workflow that could search the web.
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Without search, the model reportedly refused to answer 11 of the 13 questions. It also confidently invented a nonexistent Proxmox VE subcommand, qm autoscribe; the other deliberately fake items were reportedly rejected. That makes the test a useful illustration of distinct failure modes, not a general hallucination rate. Thirteen questions in one setup cannot establish how often local models hallucinate overall.
Rice-Jones also reported that, without search, the model called Proxmox VE 8.2 the current stable release. After he explicitly asked it to search, it returned “9.2,” cited ten sources, and referred to an ISO filename dated May 21, 2026. That example shows how retrieval can change an answer; it is not independently verified release guidance, so do not use it to determine which Proxmox version to install.
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Four outcomes that should not be conflated
- Stale knowledge: the model answers from information learned before a change, or cannot supply a current fact.
- Refusal or uncertainty: the model declines rather than inventing an answer. That may be safer than confident fabrication, but it does not meet a need for current information.
- Hallucination: the model asserts something false, such as the invented
qm autoscribecommand in this test. - Retrieval failure: search is unavailable, the model does not call it, results are not passed back correctly, or the model fails to use them.
A useful evaluation records which of these happened instead of scoring every unsatisfactory reply as “hallucination.”
Why enabling web search may not be enough
A retrieval tool adds a route to newer information; it does not guarantee that a model will take that route or use the results correctly. In Rice-Jones’s setup, search was reportedly used on seven of 13 questions when available, and on 12 of 13 when the prompt explicitly told the model to search. The prompt made tool use more frequent in this small test, but still did not make it universal.
His reported three-way timing comparison also shows the trade-off: 1.3 seconds without search, 4.9 seconds with search available but not requested, and 11.3 seconds when search was requested. These are measurements from one deployment, not expected response times for other hardware, models, or search services.
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What the search layer consisted of
The report describes Vane, formerly known in the story as Perplexica, as a self-hosted web-answering application with SearXNG bundled in the deployment and a Lemonade provider connection. Vane’s official v1.11.0 release record lists Lemonade as a provider and documents a setup wizard and a single-command Docker installation. Its architecture documentation describes a user interface, search endpoint, metasearch backend, and answer citations. Those project records confirm that the features exist; they do not verify the reliability of Rice-Jones’s particular installation.
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In his initial tests, Rice-Jones reported DuckDuckGo returning a CAPTCHA, Brave rate-limiting requests, Mojeek and Yep returning errors, Google and Startpage failing silently, and Bing suspending requests after a handful of queries. He said Vane expanded one question into about three searches. After adding a Brave API key, his deployment reportedly returned 26–55 sources in about 12 seconds. These are dated observations from one host and configuration—not current provider-wide rules, guaranteed limits, or evidence that one engine is generally better. Search providers and their interfaces can change.
The model may still mishandle results
Rice-Jones’s four reported model runs illustrate why “search worked” is not the same as “the answer is reliable.” In his setup, gpt-oss-120b was slow to begin; Qwen3.5 9B used its budget reasoning; Qwen3 Coder 30B printed raw tool-call markup as its final answer; and Qwen3 4B searched but then ignored the results. These are observations from that setup, not a model ranking or controlled comparison.
His terminal workflow had a separate integration problem: community Perplexica MCP servers did not match Vane’s provider UUID and model-key requirements. He wrote a small Python bridge and reported an MCP Python SDK 2.x compatibility issue, which he addressed by pinning below 2.0. The precise package version and current compatibility are not established, so that downgrade should not be treated as general installation advice.
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How to diagnose an answer that looks wrong or a run that hangs
A timeout alone does not tell you which part failed. Treat the workflow as stages: model inference, tool-call handling, the self-hosted search application, and requests to external search providers. Check the stage where progress stopped before changing the model or search engine.
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- Check whether the model tried to search. Compare a question that should need current information with and without an explicit instruction to search. If search is available but never invoked, investigate tool selection and prompting; do not assume that installing a search service forces its use.
- Check whether a search request completed. Look for a returned error, CAPTCHA, rate limit, or silent failure in the search layer. A query that was not answered upstream cannot provide usable evidence to the model.
- Check whether results reached the model in the expected format. Raw tool-call markup in a final answer or a model that ignores returned results points to a model/tool-handling or integration problem, rather than simply stale training data.
- Check inference progress and output length. Rice-Jones reported one reasoning response that exceeded 15,000 generated tokens without an output cap and timed out. A long response can look like a stalled search even when inference is still generating. Inspect the configured output limit and the amount of reasoning being produced.
- Check the serving backend and runtime configuration. The report attributed a substantial difference in its own setup to Lemonade loading a model with Vulkan rather than ROCm. That points to backend configuration as something to verify, not to a universal preference for one backend.
- Separate environment issues from application errors. The report also names terminal environment state as a plausible source of confusing behavior. If a run fails inconsistently, compare the same request in a clean, known configuration before concluding that the model or search provider is at fault.
Provider APIs, container tags, and SDK compatibility are version-sensitive. Check the current documentation for the specific versions in use before changing an integration.
What the reported speed and VRAM figures can—and cannot—tell you
Rice-Jones reported 10.7 tokens per second and 0.2GB of VRAM in one Lemonade/Vulkan run, compared with 33 tokens per second and 6.7GB in a ROCm run. The report does not establish the hardware, measurement method, or repeatability needed to turn those figures into a general benchmark. They illustrate that a serving configuration can change both throughput and reported VRAM use; they do not identify a GPU to buy or show what another machine will achieve.
When comparing local assistants, measure the whole task rather than just generation speed. Include whether the model retrieves when required, whether the returned sources are relevant and inspectable, how long the full search-and-answer cycle takes, whether output limits permit completion, and whether the client, bridge, provider, and model work together.
“Local” inference does not make web queries local
A model can run on your own machine while a web-search request goes to an outside search provider. In a retrieval workflow, the query sent for searching is therefore a possible outbound data flow. That does not establish that the entire conversation or unrelated files were transmitted. Before using sensitive prompts, check what the search integration sends, which provider receives it, and whether the application includes any additional context in the request.
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Self-hosting the answering interface or metasearch component does not, by itself, keep every search request inside your network: the configured upstream search providers matter too. Local inference and private retrieval are separate properties to assess.
A practical way to evaluate your own setup
Use a small, repeatable test set that separates changing facts from stable knowledge and includes a few intentionally false names or commands. For each prompt, record the answer, whether retrieval was actually invoked, whether results arrived, whether the response used them, and the end-to-end time. Inspect citations rather than treating their presence as proof that a claim is correct.
| What you observe | Likely issue to investigate | Useful next check |
|---|---|---|
| An old answer to a fact that has changed | Stale model knowledge, or retrieval was not used | Ask for a search explicitly and confirm that a search request and usable results appear. |
| A refusal despite an available search tool | The model may not recognize that retrieval is needed, or the tool path may be unavailable | Check whether it attempted the tool call and whether the search service returned a response. |
| A plausible but nonexistent command or product | Fabrication | Verify exact commands and version claims against primary documentation before acting on them. |
| Search results appear, but the answer ignores them | Model behavior or tool-result handling | Check the handoff format and whether the model can consume the returned content. |
| A long pause or timeout | Could be upstream rejection, slow inference, excessive output, environment state, or integration trouble | Find the last stage that completed; a timeout by itself does not identify the cause. |
For a fair comparison between workflows, keep the prompts and questions constant, and compare freshness, actual retrieval use, source quality, completion time, output limits, compatibility, and the data sent to outside search providers. The point is not to prove that local models are more or less reliable than another category; it is to learn which part of your own setup is responsible for a failure.
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