The Challenge of Choosing Between Quantized LLMs on Local Hardware

Selecting the most suitable Large Language Model (LLM) for an on-premise deployment, especially on hardware with limited resources, represents a common challenge for CTOs, DevOps leads, and infrastructure architects. A user recently posed an emblematic question, comparing two specific models: Qwen 3.6 35B-A3B with Q4 quantization and Gemma 4 12B with Q8 quantization, both intended to operate on a configuration with 32GB of unified memory. This situation reflects a widespread issue in the industry: how to optimize performance and resource utilization when aiming to maintain data control and operational costs.

The main question revolves around the importance of quantization and how it influences the choice between a larger but more aggressively quantized model (Qwen 35B Q4) and a smaller one with less aggressive quantization (Gemma 12B Q8). Currently, the user reports a throughput of approximately 15 tokens per second with the Qwen model on their setup, a data point that serves as a benchmark for evaluating alternatives like Gemma 4 12B, for which smooth integration is expected, even at BF16 precision.

Quantization: A Critical Factor for LLM Efficiency

Quantization is a fundamental technique for reducing the memory footprint and improving the inference efficiency of LLMs. It involves representing the model's weights and activations with fewer bits (e.g., from FP16 to Q4 or Q8), thereby decreasing VRAM requirements and potentially accelerating computations. However, this reduction in precision can lead to a trade-off in terms of model accuracy and output quality.

In this specific case, the Qwen 3.6 35B-A3B, despite being a 35-billion-parameter model, becomes manageable on 32GB of unified memory thanks to its 4-bit (Q4) quantization. Conversely, Gemma 4 12B, with its 12 billion parameters, can be run with less aggressive quantization (Q8) or even at BF16 precision, indicating greater flexibility and potentially less impact on output quality, given the same available resources. The choice between these options critically depends on balancing the hardware's computational capacity with the application's specific needs in terms of throughput, latency, and model fidelity.

On-Premise Deployment: Data Sovereignty and TCO

The decision to deploy LLMs on local hardware, such as the mentioned 32GB unified memory configuration, is often driven by strategic considerations related to data sovereignty, regulatory compliance (like GDPR), and Total Cost of Ownership (TCO). Companies operating in regulated sectors or handling sensitive data prefer to maintain complete control over their infrastructure, avoiding the risks associated with public cloud services.

In this context, the choice of models and quantization levels becomes a key element for optimizing initial investment (CapEx) and operational costs (OpEx). A smaller, well-optimized model, like Gemma 12B Q8, can offer an economically advantageous alternative, reducing the need for high-end hardware and associated energy consumption, while still maintaining adequate performance for specific workloads. Evaluating these trade-offs is essential for defining a deployment strategy that aligns technical capabilities with business objectives.

Strategic Evaluation and Future Prospects

The discussion between Qwen 35B Q4 and Gemma 12B Q8 highlights the complexity of LLM deployment decisions in on-premise environments. There is no universal solution; the best choice always depends on specific workload requirements, latency tolerance, desired throughput, and, of course, available hardware resources. Testing models directly on one's own codebase and infrastructure is the only way to obtain concrete data on performance and efficiency.

For organizations carefully evaluating self-hosted alternatives versus the cloud for AI/LLM workloads, AI-RADAR offers analytical frameworks on /llm-onpremise to explore these trade-offs in detail. The continuous evolution of quantization techniques and the emergence of new LLMs optimized for edge and on-premise promise to further expand possibilities, making generative AI increasingly accessible and controllable for businesses.