Large language models: why out-of-the-box deployment remains an illusion

The open large language model market is experiencing a genuine boom. GLM, Kimi, DeepSeek, and dozens of other projects are storming the top positions on benchmarks, and the number of producers is growing faster than the industry can catalog them. It seemed like the golden age of AI democratization had arrived — take a model, deploy it, use it. But reality turns out to be far less rosy: practically no major open LLM works out of the box, and even top-tier server hardware won't save you from hours of painful debugging.

This is the conclusion reached by an engineer who published a detailed breakdown of their experience deploying fresh mega-large models on Habr. The task was straightforwardly pragmatic — test the main LLMs, evaluate them, and select a reliable "workhorse" for everyday tasks. The platform wasn't cheap: servers based on NVIDIA B200 and H200, fresh driver version 590.48.01, vLLM-OpenAI images for inference. Everything seemed to follow the textbook. But it turned out no one had actually written the textbook.

The problem doesn't lie in the models themselves or the hardware, but in the gaping chasm between publishing weights and the actual ability to use them. Each model requires its own set of "workarounds" — specific environment configurations, configuration patches, sometimes even Docker image customization. The vLLM version 0.16 release simplified things somewhat, but the author explicitly points out: the main workarounds remain unchanged. The framework learned to handle some edge cases automatically, but the fundamental compatibility problem persists.

Particularly telling is the fact that a significant portion of solutions the author had to search for on Chinese technical forums. This is no coincidence. Most breakthrough open models of the past year come from Chinese laboratories, and the Chinese engineering community is the first to encounter the pitfalls when deploying them. English documentation, let alone Russian, often lags by weeks or even months. For specialists who don't read Chinese, this creates an additional and quite tangible barrier.

The situation exposes a systemic problem in the entire open LLM ecosystem. Model producers are focused on the benchmark race — who gets more points on MMLU, HumanEval, or Arena Elo. Publishing weights on Hugging Face is seen as the final point, and everything that happens afterward — deployment, inference optimization, integration into production pipelines — remains the responsibility of users. As a result, even companies with robust infrastructure spend a disproportionately large number of engineering hours just to get the model to respond to requests.

This is particularly acute given how rapidly the landscape is changing. New models appear literally every week. If debugging each one takes a day or two of qualified engineering time, the cost of simply comparing five or six candidates becomes noticeable even for large teams. And after selecting a model, you still need to fine-tune it for specific tasks, set up monitoring, and ensure stable operation under load.

On the horizon, however, there are positive signals. The vLLM project is actively developing and with each version takes on increasingly more routine compatibility work. Standardized model formats and unified configurations are emerging. Cloud providers offering inference as a service alleviate some of the pain for end users. But the industry is still far from a situation where downloading and running an open LLM would be as simple as installing an application.

The paradox of the current moment is that "openness" of a model no longer means "accessibility." Weights are published, the license allows commercial use, but between downloading the file and having a working service lies an entire field of non-obvious solutions requiring deep expertise. Until model producers start treating deployment as seriously as training, engineers will continue to collect recipes from forums — whether Chinese, English, or Russian.