Soup CLI vs Vizhi
Comprehensive feature analysis, ratings breakdown, platform compatibility, and community review comparison.
Detailed Feature Comparison Matrix
Compare Other Tools| Dimension |  Soup CLI |  Vizhi |
|---|---|---|
| Primary Category | Open Source | Open Source |
| Community Rating | No ratings(0 reviews) | No ratings(0 reviews) |
| Community Upvotes | 104 votes | 68 votes |
| Supported Platforms | Web | Web |
| Tags & Focus | #Developer Tools#GitHub#Artificial Intelligence#Open Source | #Developer Tools#OpenAI Day#GitHub#Artificial Intelligence#Open Source |
| Maker / Company | Independent Developer | Independent Developer |
| Platform Verification | Community Listing | Community Listing |
About Soup CLI
Soup CLI is an innovative, open-source developer tool designed to democratize LLM fine-tuning by enabling developers to train large language models on modest consumer hardware. Specifically, Soup CLI makes it possible to fine-tune an 8B LLM using just a 4 GB laptop GPU.
Because Low-Rank Adaptation (LoRA) keeps the base model frozen—meaning it is read from and never written to—Soup CLI keeps the base model in system RAM and streams it into the GPU one decoder layer at a time. This reduces peak VRAM consumption to just a single layer instead of holding the entire model in memory. Tested on an RTX 3050 Laptop 4 GB GPU, Llama-3.1-8B trains at an impressive 119.6 tokens/sec while maintaining a peak usage of only 3.32 GB.
Featuring a streamlined workflow requiring just one YAML configuration file and a single command, Soup CLI supports multiple training methods including SFT, DPO, GRPO, and KTO, alongside built-in capabilities for evaluation, gating, and model export. Distributed under the Apache-2.0 license, the project prides itself on radical transparency, publishing all performance numbers—including experimental data that didn't make the cut.
Pros of Soup CLI
- Fine-tune an 8B LLM (like Llama-3.1-8B) on a modest 4 GB laptop GPU
- Extremely memory efficient by streaming the frozen base model layer-by-layer from system RAM
- Simple user experience with a single YAML configuration file and one-command execution
- Supports multiple training methods including SFT, DPO, GRPO, and KTO, plus eval, gating, and export
- Open-source (Apache-2.0) with transparent performance data and published metrics
Cons of Soup CLI
- Requires sufficient system RAM to hold the base model while streaming to the GPU
- Performance heavily relies on system memory bandwidth for layer streaming
- Limited to hardware configurations compatible with the CLI's memory-streaming architecture
Frequently Asked Questions
What is Soup CLI?
Soup CLI is an open-source developer tool that allows users to fine-tune large language models (such as an 8B LLM) on a low-end 4 GB laptop GPU by leveraging a layer-by-layer memory streaming approach.
How does Soup CLI reduce VRAM usage?
Because LoRA keeps the base model frozen and read-only, Soup CLI stores the base model in system RAM and streams it into the GPU one decoder layer at a time. This keeps peak VRAM consumption down to just a single layer rather than the whole model.
What training methods and features are supported?
Soup CLI supports SFT (Supervised Fine-Tuning), DPO, GRPO, and KTO. It also includes built-in evaluation, model gating, and model export functionalities, all configured via a single YAML file and executed with one command.
Is Soup CLI open-source?
Yes, Soup CLI is released under the Apache-2.0 open-source license.
About Vizhi
Vizhi (derived from the Tamil word for "eye") acts as a specialized mission control dashboard for monitoring and managing your Codex CLI sessions. Developed by indie developer Ravi Shankar, Vizhi is designed to keep an eye on active execution sessions so developers don't have to constantly tab back and forth into their terminal windows.
The tool provides deep integration with hardware controllers like the Logitech MX Creative Keypad, enabling users to keep track of up to six live sessions simultaneously. Through this physical interface, developers can perform quick action executions, such as one-press session approvals, and issue local voice prompts powered by Whisper.
For developers who do not own a hardware keypad, Vizhi provides a local browser dashboard that offers visibility and control over active sessions. As an open-source developer tool available on GitHub, Vizhi fits seamlessly into terminal-centric AI development workflows.
Pros of Vizhi
- Monitors up to six live Codex CLI sessions simultaneously
- Integrates with Logitech MX Creative Keypad for tactile, one-press approvals
- Supports local Whisper voice prompts for easy command input
- Offers a local browser dashboard for users without a physical keypad
- Open-source developer tool available on GitHub
Cons of Vizhi
- Full physical experience requires a specific device (Logitech MX Creative Keypad)
- Designed specifically around Codex CLI workflows rather than general terminal tasks
Frequently Asked Questions
What is Vizhi?
Vizhi (Tamil for "eye") is an open-source mission control application designed to watch and manage active Codex CLI sessions.
Do I need a Logitech MX Creative Keypad to use Vizhi?
No. While Vizhi offers live session controls and one-press approvals tailored for the Logitech MX Creative Keypad, it also includes a local browser dashboard accessible without any hardware.
How does voice input work in Vizhi?
Vizhi utilizes local Whisper speech recognition to support voice prompts directly within your workflow.
Is Vizhi open source?
Yes, Vizhi is an open-source developer tool created by Ravi Shankar and hosted on GitHub.