Mojo + Vulkan is INSANE: Run Local AI on ANY GPU (Goodbye CUDA)

The End of CUDA's Monopoly in AI

Breaking the CUDA Barrier

• For 15 years, running AI on GPUs was synonymous with CUDA, NVIDIA's proprietary software tied to its hardware. This monopoly has now been challenged.

• New technologies like Vulkan and Mojo allow local AI to run on various GPUs from AMD, Intel, Apple, and more without relying on CUDA or incurring NVIDIA's costs.

• The significance of this shift is underscored by Qualcomm's recent acquisition of a company behind Mojo for nearly $4 billion.

Understanding the CUDA Moat

• CUDA serves as a critical interface for communicating with NVIDIA GPUs, enabling parallel processing essential for machine learning frameworks like PyTorch and TensorFlow.

• While CUDA is effective software, it restrictively operates only on NVIDIA hardware, creating a "CUDA moat" that has solidified NVIDIA’s dominance in the AI space.

• This moat limited options for users wanting local AI solutions; they were forced to purchase NVIDIA products despite capable alternatives from AMD and Intel.

Historical Context and Challenges

• Previous attempts to create cross-vendor solutions like OpenCL failed due to poor support and lack of incentive from NVIDIA to improve it.

• The question of why existing graphics cards couldn't be utilized for local AI persisted in forums until new solutions emerged in 2026.

Enter Vulkan: A Game-Changer

Vulkan's Universal Compatibility

• Vulkan is an open standard graphics API designed for compatibility across all GPU vendors (NVIDIA, AMD, Intel).

• It allows models to run efficiently across different hardware setups without needing extensive installation processes or vendor-specific toolkits.

Performance Insights

• Tokens per second measure model output speed; anything above 10 tokens per second is considered fast. One Vulkan build achieved 190 tokens per second using various GPUs.

• Users can download a single binary file (GGUF), which contains the entire model quantized for their GPU memory—no complex installations required.

Benchmarking Against Traditional Solutions

Community Benchmarks

• Community benchmarks reveal that certain AMD cards outperform traditional cloud services when utilizing Vulkan without any reliance on NVIDIA technology.

• On an AMD card (7,900 XTX), Vulkan outperformed Rockm (AMD’s official stack), demonstrating that open-source paths can surpass vendor-specific solutions.

Expanding Capabilities Across Hardware

• Even lower-end Intel ARC cards are capable of running language models effectively through Vulkan integration.

• Apple silicon also performs well under Vulkan via Molten VK translation layer; M3 Ultra achieves impressive token generation rates.

The Rise of Mojo: A New Programming Language

Introduction to Mojo

• Mojo aims to replace CUDA by allowing developers to write code once that compiles down efficiently across multiple GPU architectures (NVIDIA, AMD).

• Developed by Chris Latner (creator of Swift), Mojo combines ease-of-use akin to Python with performance comparable to C++ directly on the GPU.

Practical Applications and Flexibility

• Developers can integrate Mojo into existing Python applications seamlessly without needing extensive rewrites or adaptations.

Real-world Impact and Competitive Edge

Performance Comparisons

• Modular demonstrated that Mojo could match or exceed performance metrics against established tools like VLLM while maintaining compatibility across different hardware platforms.

Open Source Movement

• Over 450k lines of production-ready GPU kernel code have been released as open source under Mojo. This contrasts sharply with proprietary systems like CUDA.

Challenging Established Normatives

Head-to-head Competition

• Modular successfully rewrote one of Nvidia’s elite kernels in Mojo achieving competitive performance levels against Nvidia’s own hand-tuned versions.

Future Implications

• As languages like Mojo demonstrate capabilities equalizing competition against established giants such as Nvidia, it signals a potential shift away from monopolistic practices within the industry.

Industry Shifts Towards Inclusivity

Qualcomm's Strategic Acquisition

• Qualcomm’s acquisition reflects a strategic move towards making AI accessible across diverse hardware ecosystems rather than being locked into specific vendors’ offerings.

Conclusion: An Open Future Ahead

• With new pathways emerging beyond just Nvidia-centric solutions, users owning various types of GPUs can now leverage their existing hardware for serious local AI applications without additional costs or waiting periods.