TensorFoundry Research Labs
Pioneering the future of local AI through cutting-edge research and development. Our labs focus on advancing LLM tools, fine-tuning techniques and infrastructure innovations that empower organisations to harness AI on their own terms.
Research Focus Areas
Model Fine-Tuning
Developing advanced techniques for efficient model adaptation and specialisation. We explore parameter-efficient fine-tuning methods, domain adaptation strategies, and optimisation approaches that work within resource-constrained environments.
- LoRA and QLoRA optimisation
- Domain-specific adaptation
- Multi-task learning frameworks
- Efficient training pipelines
Inference Optimisation
Pushing the boundaries of inference performance for local deployments. Our research focuses on quantisation techniques, model compression, and hardware-aware for enterprise GPU infrastructure.
- Advanced quantisation methods
- Speculative decoding
- Batch processing optimisation
- Memory-efficient architectures
Evaluation & Benchmarking
Creating comprehensive evaluation frameworks for local AI systems. We develop benchmarks that measure not just accuracy, but also privacy, security and operational efficiency in self-hosted environments.
- Privacy-preserving metrics
- Performance benchmarking
- Security assessment tools
- Cost-efficiency analysis
Developer Tools
Building next-generation tools that simplify local AI development. From debugging utilities to deployment automation, we create solutions that make enterprise AI infrastructure more accessible and maintainable.
- Model debugging frameworks
- Deployment automation
- Observability platforms
- Integration toolkits
Security & Privacy
Advancing the state of AI security for self-hosted deployments. Our research addresses model security, data privacy and compliance requirements specific to air-gapped and enterprise environments.
- Model security hardening
- Privacy-preserving inference
- Compliance automation
- Threat detection systems
Distributed Systems
Exploring architectures for distributed AI inference across edge and on-premise infrastructure. We research load balancing, fault tolerance, and coordination strategies for multi-node AI deployments.
- Multi-node orchestration
- Fault-tolerant architectures
- Load balancing algorithms
- Edge-cloud coordination
Publications & Open Source
We believe in advancing the field through open collaboration. Our research findings, tools and frameworks are shared with the community to accelerate innovation in local AI infrastructure.
Research Papers
Technical papers and whitepapers on our latest findings in local AI optimisation and deployment strategies.
Open Source Projects
Tools, libraries and frameworks developed by our research team and shared with the community.
Explore on GitHub →Technical Blog
Deep dives into our research process, experimental results and lessons learned from production deployments.
Read Blog Posts →Join Our Research Efforts
We're always looking for talented researchers, engineers and collaborators who share our vision for democratising AI infrastructure. Whether you're interested in contributing to open source projects, collaborating on research, or joining our team, we'd love to hear from you.