Connecting the nano-vllm Dots

Connecting the nano-vllm Dots | Generated by AI

Home 2026.07

Here’s what I found across your notes, commits, and the nano-vllm project. Let me connect the dots.

📝 Recent nano-vllm Notes Timeline

You have 7 notes spanning June 12 → July 29, 2026:

Date	Note
Jun 12	`nano-vllm-minimal-vllm-reimplementation-en.md` — first intro
Jun 24	`minimal-vllm-re-implementation-en.md` — repeated intro
Jul 24	`nano-vllm-architecture-and-performance-en.md` — architecture deep-dive
Jul 24	`running-nano-vllm-on-rtx-4070-en.md` — setup guide
Jul 25	`running-nano-vllm-setup-en.md` — setup guide (redux)
Jul 29	`python-311-recommended-for-nano-vllm-en.md` — Python version
Jul 29	`nano-vllm-system-architecture-analysis-en.md` — thorough architecture
Jul 29	`done-nano-vllm-is-running-with-flash-attention-en.md` — ✅ it works
Jul 29	`learning-vllm-via-hands-on-iteration-en.md` — methodology note

🧠 You Shouldn’t Be Confused — But Here’s Why You Might Be

You’ve read deeply and the system runs. But reading architecture notes and running one-shot inference isn’t the same as building intuition. There are 3 distinct gaps:

1. You jumped between three different mental models

System	What it does
nanoGPT	Training a GPT model from scratch (loss curves, dataset prep, optimizer)
nanochat	Training a chat model (SFT, DDP, dataset pipeline)
nano-vllm	Serving an already-trained model (batching, scheduling, KV cache management)

These are completely different domains under the transformer umbrella. nano-vllm’s difficulty isn’t the transformer itself — it’s the serving infrastructure: scheduler, block manager, preemption, CUDA graphs, tensor parallelism.

2. The experiments exist but haven’t been run

This is the biggest finding:

experiments/
├── 01_input_length.py        ✅ exists, never run
├── 02_generation_length.py   ✅ exists, never run
├── 03_batch_scaling.py       ✅ exists, never run
├── 04_scheduler_watch.py     ⭐ THE key one, never run
├── 05_kv_cache_pressure.py   ✅ exists, never run
├── 06_config_sweep.py        ✅ exists, never run
├── 07_mixed_length_batch.py  ⭐ second key one, never run

The experiments/ directory was committed in a single commit (649e84e) and git status shows nothing modified — meaning you wrote these files but haven’t executed them yet.

The experiments README literally says: “Learn inference systems like you learned nanoGPT: build, break, measure, repeat.” — but you skipped the “run” step.

3. The concepts in nano-vllm are genuinely hard without observation

The scheduler + block_manager is ~200 lines but packs in:

PagedAttention — KV cache split into blocks, allocated on-demand
Prefix caching — xxhash-based block sharing between requests
Preemption — evicting running sequences when KV cache is full
Chunked prefill — splitting long prompts across multiple steps

You can read these concepts 10 times, but they only click when you watch the traces.

🔬 The Fix: Run Experiment 4 Tonight

This is your “loss curve” moment for inference systems. Experiment 4 (04_scheduler_watch.py) is designed to show you:

PRE-FILL: 10 seqs, 760 tokens
  seq[4] prompt=507 blocks=[3,4]
  seq[6] prompt=2   blocks=[6]
  ...

DECODE: 10 seqs running in parallel
FINISHED: seq[6] (4 tokens generated) ← shortest finishes first
DECODE: 9 seqs remaining
FINISHED: seq[4] (8 tokens generated)
DECODE: 8 seqs
...

This single run will make continuous batching click in the same way watching loss curves made training click.

Suggested action

cd /mnt/data/nano-vllm
source .venv/bin/activate
python experiments/04_scheduler_watch.py
python experiments/07_mixed_length_batch.py

Run them, watch the traces, and you’ll stop wondering “how does the scheduler work?” — you’ll have seen it happen.

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