Evaluating Self-Hosted LLMs with OpenCode: Performance on RTX 4080

Evaluating Self-Hosted LLMs with OpenCode: Performance on RTX 4080

The adoption of Large Language Models (LLMs) in enterprise environments raises critical questions related to data sovereignty, infrastructural control, and Total Cost of Ownership (TCO). In this context, the ability to run LLMs in a self-hosted manner, rather than relying exclusively on cloud services, is gaining traction. A recent study explored precisely this path, testing the capabilities of various LLMs in a local environment, using the OpenCode platform to assess their readiness and practicality in concrete application scenarios.

This analysis offers valuable insights for CTOs, DevOps leads, and infrastructure architects who are evaluating self-hosted alternatives versus cloud-based solutions for their AI/LLM workloads. Understanding the performance and hardware requirements of these models in an on-premise context is fundamental for making informed decisions that balance efficiency, costs, and security.

Methodology and Models Under Scrutiny

The study tested a selection of LLMs, including Qwen 3.5 (in its 27 billion parameter version), Qwen 3.6, Gemma 4 (26 billion parameters), Nemotron 3, and GLM-4.7 Flash, along with other unspecified models. For each LLM, two distinct tests were performed with OpenCode, designed to simulate tasks of varying complexity: creating an IndexNow CLI in Golang, considered an easy task, and generating a migration map for a website based on a site structure strategy, classified as a complex task.

A crucial aspect of the methodology concerns the execution environment. All tests were conducted on a single NVIDIA RTX 4080 GPU, equipped with 16GB of VRAM. Inference was managed via llama-server, using default memory and layer parameters. The context window employed varied between 25,000 and 50,000 tokens, depending on the specific task and model. The choice of consumer-grade, albeit high-end, hardware highlights the increasing feasibility of deploying LLMs in local environments with accessible hardware resources, a key factor for on-premise adoption strategies.

Results and Implications for On-Premise Deployment

The study's results highlighted notable performance for some of the tested models. In particular, Qwen 3.5 27b proved to be a very capable LLM, well-suited to the hardware used. The new Gemma 4 26b also showed promising results, suggesting significant potential for further exploration. For the two specific tasks, both these models offered performance comparable to that of free cloud-hosted LLMs, such as those available through OpenCode Zen.

The execution speed of the self-hosted LLMs on an RTX 4080 was monitored to provide an indication of performance. Although specific speed details were not provided in this summary, the study suggests that Fine-tuning the models or optimizing llama-server parameters could further improve Inference speed. This aspect is critical for companies looking to maximize Throughput and reduce latency in their on-premise deployments. For those evaluating on-premise deployment, AI-RADAR offers analytical Frameworks on /llm-onpremise to assess the trade-offs between hardware costs, performance, and data sovereignty requirements.

Future Outlook and Final Considerations

The analysis confirms that self-hosted Large Language Models are reaching a level of maturity that makes them concrete alternatives to cloud solutions for specific applications. The ability to run complex models on local hardware, such as an RTX 4080, opens new opportunities for organizations that need to maintain full control over their data and AI operations.

Continuous research and optimization, both at the model level (through Fine-tuning) and at the software infrastructure level (such as llama-server parameters), will be crucial to unlock the full potential of on-premise deployments. These practical studies are essential for decision-makers who must navigate the complex landscape of AI architectures, providing concrete data to balance performance, security, compliance, and TCO in a long-term perspective.