Anwer

Anwer (أنور, "more luminous") is an open-source semantic profiler for LLM inference systems.

"Semantic" here refers to inference-execution semantics — scheduler decisions, KV cache behavior, expert routing, prefill/decode phase boundaries — not text or content semantics. Anwer's core idea is to join that semantic control-plane with the hardware data-plane (kernel timelines, CUPTI / torch.profiler traces) on a shared timeline, so a latency spike can be attributed to what the engine was doing (e.g. "MoE expert dispatch for request X"), not just which kernel ran.

Modern inference stacks — vLLM, SGLang, TensorRT-LLM, NVIDIA Dynamo, and others — each expose their own metrics, logs, and tracing conventions, if they expose deep visibility at all, and existing tools sit at one end or the other: hardware profilers (Nsight, Proton) see kernels with no execution context, and request-level observability tools (Langfuse) see requests with no hardware detail. Anwer aims to sit in between, providing a single, engine-agnostic layer for understanding where time and memory actually go during inference, and why.

Status

Early / pre-alpha. This repository currently contains project scaffolding only. Design docs, architecture notes, and the first working profiler hooks will land in subsequent commits.

Goals

• Engine-agnostic instrumentation (starting with vLLM and SGLang)
• Joining semantic control-plane events (scheduling, KV cache ops, expert dispatch, prefill/decode phase) with hardware data-plane traces (kernel timelines) on a shared timeline
• Roofline-grounded interpretation of bottlenecks — attributions tied to a computable hardware number, not a heuristic guess
• Low-overhead tracing suitable for production inference, not just benchmarks
• A common schema for inference-level metrics (prefill/decode timing, KV cache occupancy, batching efficiency, queueing delay)
• Tooling to visualize and diff profiler runs across engines/configs

Why "Anwer"

Anwer is a family name meaning "more luminous" in Arabic. The idea is straightforward: make what happens inside an inference server visible.

Contributing

Not yet open for external contributions while the core design settles. Watch this space.

License

Apache 2.0 — see LICENSE.