On-Premise LLMs: When VRAM Isn't Enough and the Model "Spills" into RAM

The interest in deploying Large Language Models (LLMs) in self-hosted and on-premise environments is steadily growing, driven by the need for data sovereignty, cost control, and customization. However, this choice comes with significant challenges, particularly regarding hardware requirements. One of the most common obstacles arises when the available video memory (VRAM) on the GPU is insufficient to hold the entire model, forcing the system to "spill" (offload) part of the data into system memory (RAM). While this phenomenon allows larger models to run on less powerful hardware, it introduces critical performance bottlenecks.

A user recently shared their experience attempting to run an unsloth gemma4 26b Q5_K_XL model, quantized and approximately 21GB in size, on a home setup. Their configuration includes an AMD RX6600XT GPU, a Ryzen 7 5700X CPU, and 32GB of DDR4 3200MHz RAM, on a headless Ubuntu 26.04 system. With the model clearly exceeding the GPU's VRAM, a significant portion is handled by system RAM. The user reported performance of around 20 tokens per second during decode and 235 tokens per second during prefill, using the llama.cpp framework with specific parameters.

Technical Details: The "Spill" Mechanism and CPU/GPU Split

When an LLM is too large for a GPU's VRAM, the execution framework, such as llama.cpp, employs strategies to distribute the workload. The common practice is to load only the model layers or weights actively used for computation at any given moment into the GPU's VRAM, while the rest remains in system memory. This continuous data transfer process between RAM and VRAM, known as "spilling," occurs via the PCIe bus. The speed of this bus and the system RAM's bandwidth therefore become critical factors for overall performance.

The user's specific question revolves around this mechanism: does the CPU actively execute portions of the model residing in RAM, or does RAM primarily serve as an "extension" of VRAM, with the CPU merely orchestrating data transfer to the GPU? If it were the former, CPU and RAM overclocking could improve performance. If it were the latter, PCIe bus speed and RAM latency would be the true limiting factors. The reported performance of 20 tokens/second for decode, while functional for a personal agent, highlights the trade-off between the ability to run a large model and processing speed on hardware not optimized for intensive AI workloads.

Context and Implications for On-Premise Deployment

The situation described by the user is emblematic of the challenges faced by CTOs, DevOps leads, and infrastructure architects when evaluating LLM deployment in on-premise environments. Choosing consumer-grade hardware, while economically advantageous in terms of initial CapEx, can lead to a higher TCO due to reduced performance and the need for complex optimizations. VRAM availability is often the primary constraint for large LLM inference. Models like Gemma 26B, even when quantized (Q5_K_XL), demand significant resources.

To mitigate these issues, companies can explore various strategies. Aggressive quantization can reduce model footprint, but often at the expense of precision. Investing in high-VRAM GPUs (e.g., NVIDIA A100 or H100) is an option for critical workloads, but comes with high upfront costs. Frameworks like llama.cpp offer flexibility in distributing the workload across different resources, but a deep understanding of offloading mechanisms and workload partitioning is crucial for effective optimization. For those evaluating on-premise deployments, AI-RADAR offers analytical frameworks on /llm-onpremise to assess trade-offs between hardware, performance, and costs, considering aspects such as data sovereignty and compliance.

Optimization and Control Perspectives

Understanding whether the CPU actively participates in computation or primarily acts as a buffer for the GPU is crucial for directing optimization efforts. If the CPU indeed executes portions of the model, then optimizing the processor and system memory becomes relevant. If, on the other hand, RAM is an extension of VRAM, the priority shifts to increasing PCIe bandwidth and reducing memory latency. The user has already optimized the prompt for KV cache reuse, an important factor in improving decode performance, which is often the bottleneck in interactive applications.

The use case of a personal agent for project management and smart home automation highlights the potential of LLMs on a small scale, but also the limitations of consumer hardware. On-premise deployment decisions require a detailed analysis of hardware specifications, expected performance, and operational costs. Direct control over the infrastructure offers advantages in terms of security and customization but demands deep technical knowledge to overcome the challenges associated with efficient computational resource management.