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Tiny LLM

Tiny LLM is a Rust inference engine that runs MiniCPM5-1B locally on a CPU and provides a terminal interface for viewing inference details.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Tiny LLM — OpenViST project profile in AI Infrastructure & Serving.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it for local CPU inference with MiniCPM5-1B or to study how a compact Rust inference engine implements transformer operations.

Who it is for

  • Developers who want to run MiniCPM5-1B prompts locally without a GPU.
  • Learners exploring transformer operations and a compact Rust inference implementation.
  • People comparing the hand-written implementation with the project's separate Candle-based branch or no-KV-cache branch.

Documented uses

  • Run a prompt against a local MiniCPM5-1B model.
  • Inspect attention heatmaps and candidate logits in a terminal UI.
  • Review latency, throughput, and operation-timing statistics.
  • Study tensor operations, transformer components, SIMD optimizations, and KV-cache code.
Before you choose

Requirements & limits

The documented setup requires Rust Cargo and locally placed MiniCPM5-1B model files. The project states that it is MIT licensed.

  • The documented inference target is MiniCPM5-1B, rather than a listed set of supported models.
  • The main branch uses hand-written algorithms and tensor operations; a separate branch is based on Candle.
  • The project documents CPU-only inference and a minimal KV cache.
  • The repository includes code walkthrough pointers for tensors, algorithms, inference, SIMD, KV cache, TUI, and benchmarking.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up and run a local MiniCPM5-1B prompt

Build and start inference with a local MiniCPM5-1B model directory.

Before you start

  • MiniCPM5-1B model files: config.json, model-00000-of-00001.safetensors, and tokenizer.json
  • Rust Cargo

Steps

  1. Download MiniCPM5-1B and place the required files in a model directory.

  2. From the repository, run cargo run --release -- "" "".

  3. Press q to quit early.

Expected outcome

The project starts inference for the supplied prompt and presents its terminal interface.

Guide limitations

  • The documented model is MiniCPM5-1B.
  • The README describes a local CPU inference flow, not a general-purpose serving endpoint.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Model inference and serving

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
Tiny LLMRun a prompt against a local MiniCPM5-1B model.CLI, Self-hostedThe documented setup requires Rust Cargo and locally placed MiniCPM5-1B model files. The project states that it is MIT licensed.
MorphingdbImport a libtorch model and create predictions from values in a PostgreSQL table.Docker, Linux, Self-hosted, ServerThe README documents a Docker image and a build-from-repository option, followed by connecting to PostgreSQL and enabling the pgdl extension.
Gateway — pyadavSend chat-completion requests to a provider through a common server endpoint.ServerThe repository is public and identifies the project as Apache-2.0 licensed. README installation instructions include binaries, platform package managers and Docker.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
14 October 2026
Original website title
AspadaX/tiny-llm · Official homepage title · retained in its source language
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