liteinfer 0.1.3

A lightweight, hackable LLM inference engine built from scratch.

liteinfer

A lightweight, hackable LLM inference engine built from scratch — designed to make state-of-the-art inference techniques (paged KV cache, prefix caching, tensor parallelism, torch.compile, CUDA graphs, …) easy to read, test, and benchmark.

Goals

1. Fast offline inference — throughput in the same league as vLLM on a single node.
2. Readable codebase — clean, minimal, well-structured. The core engine should fit in your head.
3. Optimization suite — a clear place for each technique (prefix caching, TP, torch.compile, CUDA graphs, …) with isolated, testable implementations.
4. HuggingFace compatibility — load any compatible HF model from the Hub or a local safetensors directory.

Status

v0 — minimal end-to-end greedy/sampled inference on local safetensors. Single-prompt at a time, no continuous batching, no paged cache. See docs/milestones.md for what is in, and docs/roadmap.md for what is queued.

Installation

pip install liteinfer

For development or benchmark comparisons:

git clone https://github.com/ValeGian/liteinfer.git
cd liteinfer
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
# Optional: install vLLM for benchmark comparisons
pip install -e ".[dev,bench]"

Quick start

from liteinfer import LLM, SamplingParams

llm = LLM(model="meta-llama/Llama-3.2-1B-Instruct")
params = SamplingParams(temperature=0.8, max_tokens=128)

outputs = llm.generate(["Explain paged attention in one paragraph."], params)
print(outputs[0].text)

Repository layout

liteinfer/
├── liteinfer/             # Library source
│   ├── llm.py             # User-facing LLM class
│   ├── config.py          # EngineConfig
│   ├── engine/            # Orchestration: scheduler, sequence, model runner
│   ├── models/            # Model loaders + per-architecture implementations
│   ├── layers/            # Reusable building blocks (attention, RMSNorm, …)
│   ├── cache/             # KV cache (paged, prefix-cached, …)
│   └── sampling/          # SamplingParams + Sampler
├── tests/                 # Unit / integration / e2e tests
├── benchmarks/            # vLLM comparison harness
└── pyproject.toml

Each module's __init__.py documents the contract it owns.

Architecture (brief)

User code calls LLM, a thin facade over LLMEngine, which owns:

• Scheduler — picks which sequences run on the next forward pass (continuous batching; later, prefix-cache aware).
• ModelRunner — runs the actual forward pass for the selected batch on the GPU. Tensor parallelism, torch.compile, and CUDA graph capture plug in here.
• KVCache — paged blocks shared across sequences. Prefix caching is a KVCache variant.

Sampling is a separate stage so strategies (greedy, top-p, …) can be swapped without touching the engine.

Testing

liteinfer is test-first: every feature ships with the tests that pin its contract.

pytest                              # full suite — runs sequentially, GPU-safe
pytest -m "not gpu and not slow"    # fast suite (no model downloads, no GPU)
pytest -m gpu                       # GPU tests only — sequential, never use -n auto
pytest -n auto                      # parallel mode — CPU-only tests only
pytest tests/unit/                  # one directory

GPU tests: always run sequentially. The e2e tests (tests/e2e/) load real models onto GPU. Running them in parallel (-n auto) will cause OOM or cross-process interference. tests/e2e/test_vllm_runner.py additionally requires the bench extras (pip install -e ".[dev,bench]").

Test layout:

• tests/unit/ — single-component tests. CPU-only, fast, no model loading.
• tests/integration/ — multiple components wired together (still no HF download).
• tests/e2e/ — load a small real model and verify generation against transformers.

See tests/README.md for conventions.

Performance

Llama-3.2-1B-Instruct · greedy · NVIDIA A40 — see docs/benchmarks.md for full setup and methodology, or open the interactive dashboard for a visual comparison.

Engines: liteinfer (no KV cache, RECOMPUTE) · liteinfer-kvcache (eager KV cache) · liteinfer-b4 (eager KV cache + static batching B=4) · vllm (B=1) · vllm-b4 (B=4).

Throughput — 32 requests submitted at once. E2E includes queue wait time.

Engine	B	req/s	tok/s	E2E p50	E2E p99
liteinfer	1	1.81	75	11193 ms	17662 ms
liteinfer-kvcache	1	1.87	75	9865 ms	17100 ms
liteinfer-b4	4	4.42	182	4096 ms	7234 ms
vllm	1	2.89	180	5785 ms	11023 ms
vllm-b4	4	10.67	650	1656 ms	2963 ms

Latency — sequential, no queue; each request sent only after previous finishes.

Engine	B	TTFT p50	TTFT p99	E2E p50	tok/s
liteinfer	1	13 ms	14 ms	1654 ms	75
liteinfer-kvcache	1	15 ms	16 ms	1490 ms	75
vllm	1	26 ms	27 ms	693 ms	183

Benchmarking against vLLM

Every engine implements the same EngineRunner interface, so comparing liteinfer against vLLM (or future variants of liteinfer itself) is a single command:

python -m benchmarks.compare \
    --model meta-llama/Llama-3.2-1B-Instruct \
    --engines liteinfer vllm \
    --workload throughput \
    --output benchmarks/results/throughput.json

Metrics: requests/sec, output tokens/sec, TTFT (p50/p99), inter-token latency, peak GPU memory. See benchmarks/README.md for adding workloads or new engines.

Roadmap

High-level direction:

• Single-prompt greedy/sampled generation from local safetensors
• Static batching (B > 1)
• Paged KV cache → continuous batching → prefix caching
• torch.compile and CUDA graphs for decode
• Tensor parallelism (single node)
• Speculative decoding

Detailed, fine-grained backlog with scope and parity-test notes lives in docs/roadmap.md. Achieved milestones are tracked separately in docs/milestones.md.

License

Apache-2.0.