Optimizing Large Language Models on Local Hardware

Running Large Language Models (LLM) on on-premise infrastructures presents a complex challenge, particularly concerning the optimization of available hardware resources. The ability to run increasingly larger models on GPUs with limited VRAM is a critical factor for companies aiming for data sovereignty and control over operational costs. In this context, quantization techniques emerge as fundamental tools to reduce the memory footprint of models while improving Inference performance.

A recent test conducted on a consumer hardware setup highlighted the promising capabilities of a specific quantization technique, APEX, applied to the Gemma4 26B A4B model. The results obtained offer interesting insights for CTOs and infrastructure architects evaluating self-hosted deployment strategies for their AI workloads.

Technical Details and APEX Quantization Performance

The test involved the Gemma4 26B A4B model, a considerably sized LLM, subjected to APEX-I-Compact quantization in GGUF format, with a memory footprint of approximately 15GB. The hardware used for the experiment was an AMD RX 9060 XT GPU equipped with 16GB of VRAM, a component typically found in mid-range workstations or servers. The runtime chosen for Inference was llama.cpp, leveraging the Vulkan backend to maximize efficiency.

The results were remarkable: the system achieved a speed of 38 tokens per second (tps) while managing an exceptionally wide context window of 90,000 tokens. A crucial aspect of this test was the absence of perceptible degradation in model quality, a constraint often difficult to maintain with aggressive quantization techniques. For comparison, a previous quantization of the same model (unsloth ud-q5kxl), which required 21.2GB of VRAM, exhibited blocks or "loops" in similar tests with contexts of only 50,000 tokens. This highlights a significant improvement in both VRAM efficiency and stability, as well as extended context handling capability.

Implications for On-Premise Deployments and TCO

These results have direct implications for organizations considering LLM deployment in on-premise or air-gapped environments. The ability to run complex models like Gemma4 26B A4B on hardware with 16GB of VRAM opens new possibilities for using consumer or mid-range GPUs, reducing the initial CapEx compared to purchasing data center-class cards with much higher VRAM. This translates into a potential reduction in the Total Cost of Ownership (TCO) for AI infrastructures.

VRAM optimization is a key factor for scalability and efficiency. Lower memory requirements per model mean the ability to host more instances on a single server or use less expensive hardware. For those evaluating on-premise deployments, AI-RADAR offers analytical frameworks on /llm-onpremise to assess the trade-offs between performance, costs, and data sovereignty requirements. The ability to handle long contexts without quality degradation is also crucial for applications requiring the processing of extensive documents or prolonged conversations, such as legal analysis or corporate knowledge management.

Future Prospects and Trade-offs in Quantization Choice

The success of APEX quantization in this scenario demonstrates how software innovation can unlock new hardware capabilities. However, it is crucial to recognize that the choice of quantization technique is not universal. Each model, hardware architecture, and use case presents specific constraints and trade-offs. Factors such as tolerance to quality degradation, desired latency, and required throughput must be carefully evaluated.

While APEX quantization showed excellent performance in this test, it is always advisable to conduct internal benchmarks with one's own data and workloads to determine the most suitable solution. The ecosystem of LLMs and optimization techniques is rapidly evolving, and staying updated on the latest methodologies is essential to maximize the efficiency and effectiveness of self-hosted AI deployments.