Live truecolor terminal dashboard for a locally running LLM: tok/s, TTFT, GPUs, context, MTP acceptance, layer and real expert routing (llama.cpp)
Star History
Momentum
+10
STARS · LAST 30 DAYS
1
PER DAY
#624
MOST-STARRED Rust
| Window | 7 days | 30 days | 90 days |
|---|---|---|---|
| Stars gained | +7 | +10 | +90 |
| Per day | 1 | 1 | 1 |
| Forks gained | +1 | +3 | +10 |
llm-visuals gained 10 stars in the last 30 days, about 1 a day, and now has 115. It is about 1 year old and has averaged roughly 115 stars a year. It ranks #624 among Rust repositories and #6,449 across all languages on GitHubRepo.
Trending Record
5
DAYS ON TRENDING
#1104
BEST RANK
Sep 24, 2026
FIRST APPEARANCE
Active
STATUS TODAY
llm-visuals has maintained a continuous presence across global trending indexes, peaking at #1104. Below is the 30-day activity profile:
💡 Overview
llm-visuals is an open-source project written in Rust: Live truecolor terminal dashboard for a locally running LLM: tok/s, TTFT, GPUs, context, MTP acceptance, layer and real expert routing (llama.cpp).
Engineered for speed, consistency, and developer ease, it solves common hurdles in gpu-monitoring, llama-cpp, llm. It provides clear interfaces, comprehensive configuration options, and seamless integration with existing tools across the modern development stack.
⚡ Key Features
Optimized execution pipeline written in Rust for predictable speed.
Zero-friction configuration with comprehensive sensible defaults out of the box.
Cross-platform runtime support across Linux, macOS, and Windows environments.
Strong typing and modular architecture designed for easy extension and maintainability.
Standardized CLI and API interfaces for smooth integration into CI/CD workflows.
Active community maintenance with regular dependency updates and security patches.
📥 Installation
$ cargo install llm-visuals⚙ System Requirements
Platforms
- • macOS
- • Linux
- • Windows
Runtime & Dependencies
Rust toolchain (rustc / cargo >= 1.70)
Architecture
x86_64, ARM64 (Apple Silicon & Graviton)
🧠 How It Works
llm-visuals coordinates its core functionality through a modular Rust pipeline. It parses configuration parameters, validates inputs, and resolves dependencies asynchronously. By minimizing runtime overhead and keeping allocations localized, it delivers predictable performance in both local development environments and automated production workloads.
🎯 Production Use Cases
Autonomous AI Agents
Orchestrate intelligent workflows and tool-calling routines with llm-visuals.
Model Inference & Prompting
Integrate fast, local or cloud-hosted generative AI models directly into production code.
Context Memory & RAG
Augment language models with dynamic vector retrieval and structured project memory.
Developer Productivity
Automate repetitive engineering tasks, code generation, and test creation using AI agents.
🚀 Getting Started
Install llm-visuals using your package manager: `cargo install llm-visuals`
Initialize your project workspace or configuration file for llm-visuals.
Import llm-visuals into your codebase or invoke it directly from your terminal.
Execute your test suite or run `llm-visuals --help` to verify successful setup.
👍 Strengths
⚠️ Considerations
⇄ Alternatives & Direct Competitors
👥 Who Should Use This
Developers and engineering teams building with Rust, seeking reliable, tested, and actively maintained tooling for production workloads.
🏆 Nearby in the Rankings
DingoOz/llm-visuals is currently ranked #6,449 by stars across every repository tracked on GitHubRepo. These are adjacent projects:
| Rank | Repository | Language | Stars | Action |
|---|---|---|---|---|
| #6,426 | LoZazaMastro/Playhub | C# | ★ 116 | Compare ↗ |
| #6,426 | antonyshakirov/hop | Swift | ★ 116 | Compare ↗ |
| #6,426 | dboudreau00/Conformiti | Python | ★ 116 | Compare ↗ |
| #6,426 | cvxv666/fomo-robinhood-radar | Python | ★ 116 | Compare ↗ |
| #6,426 | daseinlabs/open-jev | Python | ★ 116 | Compare ↗ |
| #6,449 | DingoOz/llm-visuals This Project | Rust | ★ 115 | |
| #6,449 | mojombo/egitd | Erlang | ★ 115 | Compare ↗ |
| #6,449 | mhartl/find_mass_assignment | Ruby | ★ 115 | Compare ↗ |
| #6,449 | brown/protobuf | C++ | ★ 115 | Compare ↗ |
| #6,449 | mnaberez/supervisor_twiddler | Python | ★ 115 | Compare ↗ |
| #6,449 | alexrabarts/big_sitemap | Ruby | ★ 115 | Compare ↗ |
Frequently Asked Questions
What does llm-visuals do? +
Live truecolor terminal dashboard for a locally running LLM: tok/s, TTFT, GPUs, context, MTP acceptance, layer and real expert routing (llama.cpp)
What language is llm-visuals written in? +
The primary language is Rust. Topics include: gpu-monitoring, llama-cpp, llm, mixture-of-experts, ratatui.
Is llm-visuals actively maintained? +
Yes, the last recorded push was on Sep 26, 2026 with 0 open issues being tracked.
How many stars does llm-visuals have? +
llm-visuals has 115 stars and 15 forks on GitHub.
How does llm-visuals rank among GitHub repositories? +
With 115 stars, DingoOz/llm-visuals is ranked #6,449 globally across all repositories tracked on GitHubRepo and #624 among Rust projects.
What license is llm-visuals distributed under? +
The repository reports a MIT license. Always verify the repository LICENSE file for legal terms.
