Large Language Models: Why Running Them Out of the Box Is Still an Illusion

The market for open large language models is in a genuine boom. GLM, Kimi, DeepSeek and dozens of other projects are storming the top of the benchmarks, and the number of vendors is growing faster than the industry can catalog them. It might seem that the golden age of AI democratization has arrived: take a model, deploy it, use it. Reality is far less rosy. Almost no major open LLM works out of the box, and even top-end server hardware does not spare you hours of painful debugging.

That is the conclusion reached by an engineer who published a detailed write-up on Habr about running the newest ultra-large models. The task was thoroughly pragmatic: go through the main LLMs, test them and pick a reliable "workhorse" for everyday tasks. The platform was not cheap: servers built on NVIDIA B200 and H200, fresh drivers at version 590.48.01, and vLLM-OpenAI images for inference. Everything looked textbook. But as it turned out, nobody had really written the textbook.

The problem lies neither in the models nor in the hardware, but in the yawning gap between publishing weights and being able to actually use them. Every model needs its own set of hacks: environment-specific settings, configuration patches, sometimes even customized Docker images. The release of vLLM 0.16 simplified things a little, but the author states plainly that the main workarounds are unchanged. The framework learned to handle some edge cases automatically, but the fundamental compatibility problem has not gone away.

It is especially telling that the author had to find a significant share of the solutions on Chinese technical forums. That is no accident. Most of the breakthrough open models of the past year come out of Chinese labs, and the Chinese engineering community is the first to run into the pitfalls of deploying them. Documentation in English, let alone Russian, often lags by weeks or even months. For specialists who do not read Chinese, this creates an extra and very noticeable barrier.

The situation exposes a systemic problem across the whole open LLM ecosystem. Model makers are focused on the benchmark race: who scores higher on MMLU, HumanEval or Arena Elo. Publishing weights on Hugging Face is treated as the finish line, while everything that follows, including deployment, inference optimization and integration into production pipelines, is left to users. As a result, even companies with powerful infrastructure spend a disproportionate number of engineering hours just getting a model to answer requests.

This is felt especially sharply given how fast the landscape changes. New models appear literally every week. If debugging each one takes a day or two of skilled engineering time, the cost of simply comparing five or six candidates becomes significant even for large teams. And after choosing a model, you still have to fine-tune it for specific tasks, set up monitoring and ensure stable operation under load.

There are, however, some positive signs on the horizon. The vLLM project is developing actively and takes on more routine compatibility work with each version. Standardized model formats and unified configurations are appearing. Cloud providers that offer inference as a service lift part of the burden from end users. But the industry is still far from a point where downloading and running an open LLM is as easy as installing an app.

The paradox of the moment is that an "open" model no longer means an "accessible" one. The weights are published and the license allows commercial use, but between downloading the file and having a working service lies a whole field of non-obvious decisions that require deep expertise. Until model makers start treating deployment as seriously as training, engineers will keep piecing together recipes from forums, whether Chinese, English or Russian.