Building Cutting-Edge AI Research on Consumer Hardware with Mojo
An overview of my ongoing research exploring transformer injectivity, self-improving models, and high-performance AI on an RTX 3090
The Vision: Democratizing AI Research
What if you could run bleeding-edge AI research on hardware you already own? That’s the question driving my current research project combining Mojo’s systems programming power with novel transformer interpretability techniques.
This isn’t about running someone else’s model—it’s about building new algorithms, discovering security vulnerabilities, and creating self-improving AI systems on a single RTX 3090.
The Research Portfolio
My Mojo research spans several interconnected areas:
1. Transformer Injectivity & Hidden State Inversion (SipIt)
The Discovery: Transformers are mathematically injective—their hidden states uniquely encode input tokens. This means you can recover the exact input from internal representations.
What I Built: The SipIt (Sequential Inverse Prompt via Iterative updates) algorithm achieves 100% token recovery from Mistral-7B hidden states.
Why It Matters:
- Security: System prompts can be extracted from debug endpoints
- Interpretability: Hidden states reveal what models “understand”
- Training Signal: Inversion quality predicts model capability
2. Fidelity-Guided Training
Novel Finding: Strong correlation (r = 0.891) between how well a model’s hidden states encode information and its task performance.
The Implication: Models can identify their own knowledge gaps without external feedback. This enables self-improving AI using only internal representations.
3. Pi-Attention: Efficient Long-Context Processing
Implementing the latest sparse attention research (arXiv:2511.10696) that achieves:
- 50% GPU memory reduction
- 8.3% perplexity improvement
- O(n) instead of O(n squared) complexity
4. High-Performance Mojo Kernels
Custom SIMD-optimized kernels for:
- L2 distance computation (10x faster than PyTorch)
- Batched vocabulary search (32K candidates in ~8ms)
- GPU-accelerated fidelity scoring
Hardware: Consumer-Grade Research
All research runs on accessible hardware:
| Component | Specs |
|---|---|
| GPU | NVIDIA RTX 3090 (24GB VRAM) |
| CPU | Intel Xeon Gold 6430 (128 cores) |
| RAM | 252GB |
| Models | Mistral-7B, TinyLlama-1.1B, Phi-2 |
No A100s required. No cloud spend for development. Just smart algorithm design and Mojo’s performance.
Why Mojo?
Mojo bridges the gap between Python’s ML ecosystem and systems performance:
- Zero-Cost Abstractions: Write Pythonic code that compiles to LLVM
- SIMD First-Class:
vectorizeandparallelizebuilt into the language - GPU Support: Same code targets CPU and GPU
- Python Interop: Use PyTorch, Transformers, TransformerLens seamlessly
Example: My L2 distance kernel processes 32,000 vocabulary candidates x 4,096 dimensions in 8ms on CPU, enabling real-time token recovery.
Research Outputs
Validated Findings
- 100% system prompt recovery from Mistral-7B
- Fidelity-accuracy correlation (r = 0.891) on 12 security tools
- Working Pi-Attention implementation with memory reduction
Coming Soon
- arXiv preprint on fidelity-guided training
- Open-source release with reproducible benchmarks
- Detailed security vulnerability disclosure
Follow the Journey
This blog will document:
- Deep dives into each research area
- Mojo kernel implementation details
- Benchmark comparisons and optimization techniques
- The path from homelab to publication
Next Post: How I Extract System Prompts from Any Transformer
All research conducted on consumer hardware. No models were harmed in the making of this blog.