Local LLM vs ChatGPT: the answer quality a company can expect from its own AI server

How good a local LLM is compared with ChatGPT in 2026

For letters, emails, summaries, rewording and translation into the main European languages, an open-weight model on a company server with four or eight NVIDIA RTX PRO 6000 Blackwell Server Edition cards gives answers in the same quality band as the GPT models offered in ChatGPT, as far as the public leaderboard of October 2026 can show it. A server with two cards runs smaller models that write well but know fewer facts and make more mistakes on hard questions. For answers about your own company, quality depends less on the model than on whether the assistant finds the right document and cites it.

By "local LLM" we mean a large language model (the kind of AI behind ChatGPT) whose files, the weights, a company downloads and runs on its own server, so that questions and documents stay in its network. "Open-weight" (often loosely called open source) means the maker publishes these files under a licence that allows it. ChatGPT runs only in its provider's cloud.

This page is for a managing director or head of department who wants a realistic picture before deciding. It compares quality only. Data handling, contracts and cost drivers of ChatGPT Business and Enterprise are compared in our article on ChatGPT Enterprise or an own AI server, and the IT selection of a model by licence and languages in our guide to choosing an open LLM for a company assistant.

What the public leaderboard shows in October 2026

LMArena, whose leaderboard now runs at arena.ai under the title "Text Arena", lets users put one question to two anonymous models and vote for the better answer. From these votes it computes a score per model. On 22 October 2026 the text leaderboard counted 8,946,356 votes across 418 models and describes its rankings as covering "text-to-text tasks across math, coding, creative writing, and other open-ended domains".

LMArena text leaderboard (arena.ai), overall, update of 22 October 2026, read 25 October 2026; DeepSeek entry is the release of 10 September 2026. Models offered in ChatGPT as listed on OpenAI's help page "ChatGPT Enterprise and Edu models and limits", read 25 October 2026. Licences of the open models from their Hugging Face pages, read 25 October 2026.

The rank spread is the range of ranks a model could hold given the uncertainty of its score. Where the spreads of two models overlap, as they do for the best open models and the GPT models in ChatGPT, the leaderboard cannot tell which of them is better. The gap to the very top is clear: the leading proprietary model ranks 1 to 3. gpt-oss-120b, the open model that fits a single card, sits 124 to 133 points below the GPT models in ChatGPT.

Three limits apply. The votes come from anonymous users with their own questions, mostly in English, and contain none of your documents or customers. "Max" and "xHigh" mean the model ran at its highest reasoning setting, which makes answers slower. And the ranking says nothing about the language your staff write in; GLM-5.3-Flash's model page carries only the language tags English and Chinese.

Which models fit which server

The size of the model a company can run is set by the memory on the graphics cards (GPUs) of its server. Our usable-memory rule counts 90 per cent of the 95.6 GiB the driver reports on each 96 GB card, which gives 166.1 GiB for two cards, 332.2 GiB for four and 664.3 GiB for eight. The weights must fit with room left for every conversation in progress.

Weights are the sums of the safetensors files listed on Hugging Face for openai/gpt-oss-120b, Qwen/Qwen3.8-27B-FP8, nvidia/GLM-5.3-Flash-NVFP4 and deepseek-ai/DeepSeek-V4.1-Flash, read 25 October 2026; scores from the leaderboard table above; fit by our usable-memory rule.

gpt-oss-120b has 117 billion parameters, of which 5.1 billion work on each word, and OpenAI places it in use cases that "fit into a single 80GB GPU". Qwen3.8-27B is a dense model of 27 billion parameters. GLM-5.3-Flash has "320B total parameters and just 18B active parameters" by Z.ai's card, and NVIDIA's 4-bit NVFP4 version scores almost the same as the full-precision model in NVIDIA's own table, 0.9211 against 0.9217 on GPQA Diamond. DeepSeek-V4.1-Flash, 510.3 GB of files, fits the 664.3 GiB of eight cards by our rule.

So the quality step from two to four cards is large, and from four to eight cards the leaderboard shows little further gain for these examples. Eight cards pay off for more users at once, for several models side by side, or for a model your own test prefers.

Task by task: where local models are close and where they lag

The table sums up what to expect for everyday company tasks. It follows from the leaderboard, the model cards and how these systems work; your own test confirms it for your documents.

Our summary of the leaderboard of 22 October 2026, the gpt-oss model card of August 2025 and OpenAI's ChatGPT Enterprise help page read 25 October 2026; to be confirmed by your own test.

Writing and summaries. These tasks need language skill more than knowledge, and even the single-card models write fluent, well-structured text. Give the model two or three of your own letters as examples, and it follows your tone. For long contracts or reports, the context length set on the server limits how much text fits into one question.

Translation. OpenAI reports 83.0 for gpt-oss-120b on the German part of its multilingual test MMMLU, at high reasoning, against an average of 81.3 over all languages it tested. That is the developer's own figure. For Polish, Romanian or smaller EU languages, check the model card first and test with native speakers, as our comparison of open LLMs for European languages shows.

Current events and research. A model knows only what was in its training data. OpenAI states a knowledge cutoff of June 2024 for gpt-oss. ChatGPT Enterprise lists search, deep research and data analysis among its tools, so it can look things up and compute. A local server has none of these unless your IT connects a search service, and every search query then leaves your network.

Coding. Larger models help more with code; our guide to a private coding assistant covers that case.

We supply servers with two, four or eight RTX PRO 6000 Server Edition cards and set up the models on them; send us three typical tasks of your staff, and our team suggests the model and server size for that quality level.

Local LLM hallucinations in plain words, and what helps

A language model hallucinates when it writes a confident, fluent answer that is wrong. It does this because it produces the most likely text, and a missing fact does not stop it from producing a plausible one.

OpenAI measured this for its own open models. On SimpleQA, "a diverse dataset of four thousand fact-seeking questions with short answers", gpt-oss-120b answered 16.8 per cent correctly and had a hallucination rate of 78.2 per cent, run without web access. OpenAI's o4-mini reached 23.4 and 75.0 per cent on the same test. OpenAI's explanation: "smaller models have less world knowledge than larger frontier models and tend to hallucinate more". The same card notes that browsing for information tends to reduce hallucination.

For a company this leads to a simple rule: do not let the model answer company questions from memory. Three measures reduce wrong answers:

1. Answers from documents. The assistant searches your documents first and puts the passages it found into the question, a method called retrieval-augmented generation (RAG). Our article on RAG on company data explains why the search step decides the answer.
2. Citations. Every answer names the document and its date, so the reader can check it in seconds.
3. Permission to say "not found". The assistant is set up to reply that it found nothing when no passage matches, and staff review answers that go to customers or into contracts.

These measures help with ChatGPT as much as with a local model. On your own server they work on documents that never leave your network.

A quality test with your own questions

A leaderboard narrows the choice to two or three models. Your own test decides, and it does not need an IT department to design it.

1. Collect 50 to 100 real questions from the departments that will use the assistant, in the languages they write, with the correct answer and the document it comes from.
2. Add typical writing tasks: a reply to a complaint, a summary of a meeting note, a translation of a product sheet.
3. Run the same questions on two or three candidate models with the same documents and settings, and, where your data allows it, on ChatGPT for comparison.
4. Have the people who know the answers grade them without knowing which model wrote which answer.
5. Count wrong answers and answers without a correct citation separately from style, because a wrong fact costs more than a clumsy sentence.

Our guides to what a private AI pilot should measure and to LLM evaluation before model upgrades give the measurement side for the IT team. Keep the question set: new open models appear several times a year, and the same test shows whether a newer one is better for you.

Our Private AI/ML service runs such a test as a pilot on one process with clear metrics; describe the process and the languages, and we propose the candidates and the server for the pilot.

Hardware for each quality level

This section collects the hardware in one place. The server sizes follow the reference configurations on our AI servers page.

Users are conversations of 32,768 tokens in progress at the same moment by our users-per-card rule (0.9 × 95.6 GiB − 3 GiB per card − weights): gpt-oss-120b (19 per card) from our company-size guide and GLM-5.3-Flash from our GLM-5.3-Flash guide, both with a 16-bit cache; Qwen3.8-27B in FP8 (47 per card) with an FP8 cache, from our guide to Qwen3.8-27B on the RTX PRO 4500 Server Edition; power by our estimate (cards at 600 W, two processors, plus 15 per cent).

"Users at once" counts conversations being answered at the same moment, which is far fewer than the staff who use the assistant during a day. Our guide to a private ChatGPT server by company size turns headcount into that number. With its example values, one card running gpt-oss-120b covers the busy-hour peak of a few hundred employees.

The two-card server is the entry configuration: one card runs the main model, the second is split by NVIDIA's MIG feature into up to four separate 24 GB parts for the document search models. The four-card server runs a model in the quality band of the GPT models in ChatGPT. All three belong in a server room or a data centre for noise and heat, as our guide to a GPU server in an office explains. A 230 V 16 A feed carries 2.94 kW at 80 per cent, enough for two cards. Without a server room, our Private AI/ML service runs the models on dedicated hardware in a Tier-3 data centre in Lithuania, inside the EU.

The RTX PRO 6000 Max-Q is the workstation version of the card, with 300 W; for a server in a rack that runs continuously we recommend the Server Edition.

What we supply

You can buy this system from us: our team sizes the configuration to your workload and budget and replies within one business day. We build the servers to order with two, four or eight RTX PRO 6000 Server Edition cards, assembled and tested before delivery, delivered anywhere in the EU on one EU contract and invoice, with manufacturer warranty on every component handled through us; quantities and lead time are confirmed in writing with the quote. Our Private AI/ML service, with our engineering partner Vixen.UNO, deploys the models, the search on your documents with citations, and the pilot that compares candidates on your own questions.

FAQ