Krasis LLM Runtime – run large LLM models on a single GPU

Introducing Krasis LLM Runtime: Empowering Large Language Model Deployment on a Single GPU

The rapid advancement of large language models (LLMs) has revolutionized how we interact with AI, but the significant computational requirements often demand high-end, multi-GPU setups or data center resources. Recognizing this challenge, Krasis LLM Runtime offers an innovative solution that enables users to run sizeable LLMs on a single consumer-grade GPU, making advanced AI accessible to a broader community.

What is Krasis LLM Runtime?

Krasis is a purpose-built inference engine designed to facilitate the deployment of large-scale language models—models that traditionally require extensive GPU VRAM—on a single consumer GPU. Unlike conventional approaches that split model layers between GPU and CPU, Krasis leverages a streaming mechanism for expert weights, optimizing memory usage and computational flow. This approach ensures that even models exceeding the VRAM capacity can be run efficiently, opening new avenues for research, development, and deployment.

How Does Krasis Work?

At its core, Krasis streams key weights directly through the GPU, employing sophisticated optimization strategies tailored for different phases of model inference:

Prefill Phase: Streamed weights are processed as the model prepares to generate output, with optimization techniques ensuring rapid loading.
Decode Phase: During actual text generation, weights are streamed in real-time, maintaining responsiveness and speed.

This methodology allows models such as the Qwen3-235B (approximately 438GB in BF16 precision) to operate at Q4 quantization levels on a single NVIDIA RTX 5090 or even a 5080 GPU, delivering practical inference speeds. System RAM usage remains modest—roughly equivalent to the size of the quantized model—making it feasible to run on standard high-end consumer hardware.

Performance Metrics

Below are some benchmark speeds achievable with Krasis on a single NVIDIA RTX 5090 (PCIe 4.0) at Q4 quantization:

Similarly, on an NVIDIA RTX 5080 (PCIe 4.0) at Q4, the Qwen3-Coder-Next model achieves:

Features and Capabilities

Quantization Support: Krasis automatically converts models from BF16 safetensors to optimize performance.
Memory Optimization: Supports BF16 and AWQ attention mechanisms, significantly reducing VRAM consumption.
OpenAI-Compatible API: Offers an API interface compatible with popular IDEs and applications for seamless integration.
Cross-Platform Compatibility: Runs effectively on Linux and Windows via Windows Subsystem for Linux (WSL), with only a slight performance trade-off.
Focus on Major Models: Currently optimized for Qwen MoE models with plans to support Nemotron models in the near future.
Open Source & Free: Available for download and use at no cost, encouraging experimentation and innovation.

Why Krasis?

Developed by an experienced engineer with over 20 years of building high-performance distributed systems, Krasis stems from a desire to democratize access to premier open-weight models. Instead of relying on costly data center infrastructure or expensive GPUs, Krasis empowers individual developers and researchers to run state-of-the-art models locally, reducing barriers to AI experimentation.

Get Started Today

Krasis is a significant step toward making large language models more accessible and practical for everyday use. Whether you’re a researcher, developer, or hobbyist, this runtime can elevate your AI projects without the need for dedicated data center hardware.

Learn more, contribute, or try Krasis by visiting the official GitHub repository: https://github.com/brontoguana/krasis

Harness the power of large language models locally—simplify deployment, reduce costs, and accelerate innovation with Krasis LLM Runtime.