# Crucible LLM :: Bench your LLM and server. Is it fast enough… for everyone? > Source: > Published: 2026-10-01 19:19:04+00:00 I built **Crucible LLM** — a terminal-based benchmarking suite that stress-tests any OpenAI-compatible inference server and measures what a *real user* actually experiences. One static Rust binary. No Python. No venv. No dependencies. Most benchmarks shout “xx.x tokens/sec!” and stop. But when 32 users hit that server at once, each one gets 2 tokens/sec. A number that looks fast in the aggregate can feel painfully slow per user. Crucible measures **that gap** — the difference between a server’s peak throughput and what each individual connection actually gets as well as how competent the LLM is at tasks. `/v1` endpoint and go. *Live Monitor screenshot — real-time throughput graph with auto-scaling y-axis, engine-transition markers, and live event log.* Build from source (requires a stable Rust toolchain): ``` git clone https://github.com/MadGoatHaz/crucible-llm cd crucible-llm cargo build --release ./target/release/crucible-llm ``` Or grab a pre-compiled static binary from the [Releases](https://github.com/MadGoatHaz/crucible-llm/releases) page. On first run, the TUI walks you through entering your server URL and picking a model. A few examples of the kind of insight a run produces: A server that serves 64 users at 2.6 t/s each is not “fast.” It is slow for everyone. Crucible reports the difference. Crucible is **free and open source** (GPL-3.0). If you run inference servers, benchmark models, or just want to know what your hardware can actually do, I’d love your feedback, issues, and PRs. **[github.com/MadGoatHaz/crucible-llm](https://github.com/MadGoatHaz/crucible-llm)** — star the repo, open an issue, or fork and contribute. *Technical: Rust · GPL-3.0 · ~14 MB static binary · zero runtime dependencies (SQLite compiled in) · GPU telemetry feature-gated (NVIDIA NVML built-in)*