What shipped
Nano-vLLM is a working LLM inference engine in roughly 1,200 lines of Python that implements the core architectural patterns from vLLM—the open-source engine powering many production LLM deployments. Created by a DeepSeek contributor (listed on DeepSeek-V3 and R1 technical reports), it achieves approximately 90% of vLLM's throughput in optimal scenarios while remaining digestible.
The project implements paged attention, prefix caching, tensor parallelism, and CUDA graph compilation. It's not a toy—it's a functioning inference engine with production techniques, just without the thousands of lines handling edge cases and hardware variants.
Why this matters
Every LLM API you call—OpenAI, Claude, regional providers—runs on infrastructure making the same fundamental trade-offs around batching, memory management, and scheduling. Understanding these choices matters when you're:
- Evaluating latency vs throughput requirements for your deployment
- Debugging why certain request patterns perform poorly
- Deciding between managed APIs and self-hosted inference
- Architecting systems that need predictable SLA compliance
The code separates inference into prefill (processing the initial prompt) and decode (generating subsequent tokens). This split drives most performance characteristics. Batching multiple requests amortizes GPU overhead but increases latency for individual requests—there's no free lunch.
The scheduler maintains waiting and running queues, deciding which sequences to process based on resource availability. The block manager handles KV cache allocation using paged memory, eliminating fragmentation. These aren't abstract concepts—they're why your inference costs look the way they do.
The trade-off
Nano-vLLM's educational clarity comes with compromises. It requires separate prefill and decode steps where production vLLM can process both simultaneously. For learning or prototyping unsupported architectures, that's fine. For production APAC deployments handling variable load, mature engines with broader hardware support matter more.
The project's value isn't replacing vLLM—it's understanding what vLLM does, which helps when you're configuring max_num_seqs, tuning gpu_memory_utilization, or explaining to finance why inference costs scale non-linearly.
Worth a look if you're building on LLMs. The code is on GitHub. Part 2 apparently covers attention mechanisms and tensor parallelism internals.