Building an LLM Inference Server from Scratch: Deep Dive into vLLM's Core Mechanisms

mini-llm-serve is a minimal LLM inference server implementation maintained by YunhaoDou (GitHub link: https://github.com/YunhaoDou/mini-llm-serve, updated on 2026-06-10). It aims to help developers deeply understand vLLM's two core mechanisms—KV cache reuse and continuous batching—by building from scratch. The project uses concise code to demonstrate the complete workflow of an inference server, lowering the barrier to learning LLM system design.

With the rapid development of LLMs, efficient inference has become a core challenge in deployment. As a leading inference engine, vLLM achieves high throughput through technologies like PagedAttention, but its codebase is large and complex, with a steep learning curve. mini-llm-serve was created to implement core functions with the most concise code, allowing developers to clearly see the principles behind design decisions.

The project implements two key technologies:

1. KV Cache Reuse: In autoregressive generation, traditional caching leads to high memory overhead and latency due to frequent copying and moving. mini-llm-serve supports reusing caches for identical prefixes across requests, reducing VRAM usage and improving first-token response speed.
2. Continuous Batching: Traditional static batching has low GPU utilization (waiting for the slowest request). mini-llm-serve dynamically adds/removes requests, maintaining high GPU utilization and increasing throughput severalfold.

Memory Management Strategy

Uses a paging mechanism, dividing KV cache into fixed blocks. Through page table mapping to physical storage, it minimizes memory fragmentation, enables dynamic expansion, and supports sharing (with copy-on-write to ensure isolation).

Scheduler Design

The core is continuous batching. After each iteration, the queue is evaluated. Strategies include priority sorting, preemption mechanism (high-priority requests can pause low-priority ones and swap their KV cache to CPU), and dynamic calculation of maximum requests based on memory budget.

Inference Engine Integration

Modular design compatible with mainstream frameworks, supporting rapid experimentation with attention implementations, comparison of quantization schemes, and integration of custom optimized operators.

Educational Value

• High code readability with clear core logic and no over-encapsulation
• Covers the complete workflow of an inference server (from request access to token generation)
• Concise code facilitates debugging and performance profiling

Engineering Insights

• Helps optimize configuration parameters of mature frameworks (e.g., vLLM, TensorRT-LLM)
• Assists in troubleshooting (VRAM overflow, latency anomalies)
• Provides references for custom development (e.g., custom scheduling strategies)

mini-llm-serve is suitable for the following scenarios:

1. Educational research: Example for university LLM system design teaching
2. Prototype verification: Quickly validate new scheduling algorithms or memory management strategies
3. Edge deployment: Custom lightweight inference services for resource-constrained environments
4. Performance benchmarking: Serve as a baseline for fair comparison with other frameworks

mini-llm-serve reveals the core principles of modern LLM inference engines through a minimal implementation, proving that reasonable architectural design can achieve significant inference efficiency. It is an excellent starting point for developers who want to dive deep into the underlying layers of LLMs. It is recommended that readers try modifying the scheduling strategy or memory allocation algorithm while reading the code to deepen their understanding. With the development of multimodal and long-context technologies, there is vast room for optimization in inference systems, and the project's design ideas will continue to play a role.