Deploying Google's Gemma 4 LLM on Consumer Hardware
The article describes the author's experience deploying Google's Gemma 4 language model on their home inference server, including building the model from source and running it in production within 2 hours.
Why it matters
This article demonstrates the ability to quickly deploy and run state-of-the-art large language models on consumer-grade hardware, highlighting the performance and scalability of modern AI infrastructure.
Key Points
- 1The author built a custom Docker image to support the Gemma 4 architecture, which was not yet available in released llama.cpp builds
- 2The model was deployed on the author's home Kubernetes cluster, which also handled the build process using Kaniko
- 3The dual-GPU setup and MoE architecture allowed the model to achieve 96 tokens/second on a single request and 170 tokens/second under concurrent load
Details
The author's home inference server is powered by 2 NVIDIA RTX 5060 Ti GPUs, an AMD Ryzen 9 7900X CPU, and 64GB of DDR5 RAM. They used a custom Kubernetes operator called LLMKube to manage the deployment. When the Gemma 4 model was first released, the existing llama.cpp builds did not support the new architecture, so the author built the model from source on the cluster. The custom Docker image targeted CUDA compute capabilities 86 (Ampere) and 120 (Blackwell). The deployed model was 15.6GB in size and used a 32,768 token context. Under load testing, the system achieved 170 tokens/second aggregate throughput with 0% error rate and 2 second P50 latency.
No comments yet
Be the first to comment