Optimizing On-Premise LLMs: The Challenge of Tool Calling and Quantization

The adoption of Large Language Models (LLMs) in on-premise or self-hosted environments presents unique challenges, particularly concerning efficiency and hardware resource management. For CTOs, DevOps leads, and infrastructure architects, selecting the right quantization techniques and understanding their impact on real-world performance are crucial aspects. A recent study meticulously analyzed the Qwen3.6-35B-A3B model, focusing on its "tool calling" capabilities across various quantization configurations and with varying context windows. This analysis offers valuable insights for those balancing data sovereignty, control, and Total Cost of Ownership (TCO) requirements in AI deployments.

"Tool calling" represents an advanced LLM feature, enabling models to interact with external tools to perform actions or retrieve information, significantly extending their capabilities. Performance in this area is sensitive not only to the model's architecture but also to the efficiency with which memory, especially GPU VRAM, is managed, and the length of the context provided to the model. Understanding how quantization influences these dynamics is fundamental for optimizing AI workloads on local infrastructures.

Testing Methodology and Quantization Comparison

The research utilized a cluster of NVIDIA V100 GPUs, each equipped with 32GB of VRAM, a common hardware setup in many on-premise data centers. To conduct the tests, the llama.cpp library (version 9529) and the tool-eval-bench tool (version 2.0.4), designed to evaluate LLM "tool calling" capabilities, were employed. Eight different GGUF quantizations of the Qwen3.6-35B-A3B model, from two main providers—ByteShape and Unsloth—were compared. These included both IQ (Integer Quantization) and Q (Quantization) variants with sizes ranging from 13.2 GB to 29.3 GB, designed to fit various VRAM configurations.

In addition to model quantization, the study examined the impact of KV cache quantization, testing three configurations: f16 (standard floating-point precision), q8_0, and q4_0. Another critical aspect was the effect of context length: tests were performed with both a short context (approximately 5,000 tokens) and a long context, simulating a context window 50% filled with an additional 122,000 tokens, to assess the model's resilience in more complex scenarios. The entire benchmark campaign involved 144 runs, totaling approximately 300 GPU-hours, highlighting the effort required to obtain meaningful data in this field.

Key Results and Implications for On-Premise Deployment

The results revealed several crucial aspects. Regarding GGUF quantizations, no clear winner emerged between ByteShape and Unsloth in absolute terms. However, the ByteShape GPU-5 (18.0 GB) quantization showed the best overall performance, notably distinguishing itself for its resilience in "tool calling" tasks with long contexts. Conversely, the ByteShape CPU-5 (18.3 GB) quantization was the worst performer. This suggests that the choice of quantization depends not only on size but also on specific optimization for the hardware and workload.

Another significant finding concerns KV cache quantization: f16 and q8_0 configurations yielded practically identical results, making q8_0 a "free lunch" solution that saves memory without sacrificing output quality. The q4_0 quantization, while having a surprisingly small impact, showed a slight drop in performance. The most critical aspect that emerged was the impact of long context: the addition of 122,000 tokens led to an average degradation of almost 10 points in "tool calling" performance across all scenarios, underscoring the difficulty of current models in maintaining accuracy with extended inputs. For those evaluating on-premise deployments, these trade-offs between model size, available VRAM, and the ability to handle long contexts are essential. AI-RADAR offers analytical frameworks on /llm-onpremise to evaluate these compromises and support informed decisions.

Final Considerations and Future Perspectives

The study highlights the complexity in choosing the optimal configuration for LLMs in on-premise environments. There is no universal solution: the "best" quantization or configuration heavily depends on the specific use case, VRAM requirements, and tolerance for performance degradation with extended contexts. The resilience of ByteShape GPU-5 with long context is an interesting data point for applications requiring the management of long conversations or complex documents.

It is important to note that the results of this benchmark, while significant, depend on the specific tasks of "tool-eval-bench" and the evaluation methodology. The variability and inherent noise in these tests suggest that each individual measurement should be interpreted with caution, but aggregate scores still offer a clear direction. For companies seeking to maintain data sovereignty and full control over their AI infrastructures, a detailed understanding of these trade-offs is fundamental for effective and sustainable deployment.