AI model training has always required expensive cloud servers or powerful data center hardware. That barrier just fell. QVAC Fabric LLM brings professional AI training to your phone, laptop, or desktop GPU. You can now fine-tune language models on the device in your pocket.
This framework from Tether Data changes how we think about AI development. Instead of uploading data to remote servers, you train models locally. Your data stays private. Your device does the work. No internet required.
QVAC Fabric LLM launched in December 2025 as the first production system enabling modern AI training on smartphone GPUs like Qualcomm Adreno and ARM Mali. It works across every major platform: iOS, Android, Windows, macOS, and Linux.
What Makes QVAC Fabric LLM Different
Traditional AI training tools only work with NVIDIA GPUs and cloud infrastructure. QVAC Fabric LLM breaks this limitation. It runs on AMD, Intel, NVIDIA, Apple Silicon, and mobile chips.
The framework uses LoRA (Low-Rank Adaptation) for efficient training. Instead of retraining an entire AI model, LoRA adds small adapter modules. These adapters learn from your data while the base model stays frozen. This approach dramatically reduces compute and memory requirements while preserving model quality.
QVAC Fabric LLM integrates with llama.cpp, a popular runtime for running AI models on consumer devices. It uses Vulkan and Metal APIs to access GPU power across different hardware vendors. This means one codebase works everywhere.
How Local AI Training Works
Training AI models requires matrix multiplication, the core math operation in neural networks. GPUs excel at this task. But different GPU makers use different software interfaces.
QVAC Fabric LLM executes both training and inference through the Vulkan API, a cross-platform, vendor-agnostic interface for GPU compute. For Apple devices, it uses Metal. This universal approach unlocks hardware that was previously locked out of AI development.
The framework includes a dynamic tiling algorithm for smartphone GPUs. This technique breaks large matrix operations into smaller, memory-safe segments that process sequentially. Each tile is computed, stored temporarily, then assembled into the final result. This makes training possible even with limited phone memory.
Training Performance Across Devices
Performance varies by hardware, but the results prove local training works:
| Hardware | Training Time | Notes |
|---|---|---|
| RTX 4090 Desktop GPU | ~45 minutes | High-end consumer GPU |
| Qualcomm Adreno 830 (Phone) | ~13 hours | First smartphone training |
| Consumer AMD/Intel GPUs | 2-8 hours | Varies by model |
| Apple Silicon MacBook | 1-3 hours | M1/M2/M3 chips |
On a high-end RTX 4090 desktop GPU, a full fine-tuning completes in approximately 45 minutes. On a smartphone with Qualcomm Adreno 830, the same training takes roughly 13 hours.
These times represent complete training cycles, not simple inference. You're actually teaching the AI new skills, not just running it.
Model Quality and Accuracy
Hardware flexibility doesn't sacrifice quality. Models trained using QVAC Fabric LLM were evaluated against industry-standard benchmarks, with performance on par with PyTorch and in some cases marginally better.
Testing included biomedical question-answering tasks. Models achieved 79-94% accuracy across different hardware platforms. Win rates against PyTorch-trained models reached 45-48% in LLM-as-judge evaluations.
The framework maintains consistency across all supported GPUs. A model trained on a phone produces the same quality results as one trained on a desktop GPU. Only the time required differs.
Supported AI Models
QVAC Fabric LLM extends the llama.cpp ecosystem with new capabilities:
- LLama3 (all sizes)
- Qwen3 models
- Gemma3 family
- Any GGUF-format model
These models, previously unsupported for fine-tuning in this environment, can now be fine-tuned through a simple, consistent workflow across all hardware types.
The framework supports quantized models (4-bit, 8-bit) for memory efficiency. You can train larger models on smaller devices by using quantization.
Privacy and Security Benefits
Cloud AI training means uploading your data to external servers. QVAC Fabric LLM keeps everything local.
By enabling training and personalization directly on user-owned devices, the framework keeps data local by default. Sensitive information never leaves your hardware. This matters for:
- Healthcare data (HIPAA compliance)
- Financial records
- Personal communications
- Proprietary business information
- Any regulated industry data
Organizations can fine-tune models on secure hardware without exposing data to cloud providers. This simplifies compliance and reduces risk.
Getting Started
The framework is open source under Apache 2.0 license. Pre-built binaries are available for download.
Basic setup steps:
- Download the binary for your platform from GitHub releases
- Extract the files to a working directory
- Download a base model in GGUF format
- Prepare your training data in JSONL format
- Run the training command
Example training command:
./llama-finetune-lora \
-m models/qwen3-0.6b-q8_0.gguf \
-f train.jsonl \
--assistant-loss-only \
-c 128 -b 128 -ngl 99 \
--num-epochs 2
The framework includes sample datasets for testing. You can start with biomedical question-answering or email style transfer examples.
Training Data Preparation
Your training data needs proper formatting. Use JSONL (JSON Lines) format with instruction-response pairs.
Example format:
{"instruction": "What is the capital of France?", "response": "Paris"}
{"instruction": "Explain photosynthesis", "response": "Photosynthesis is..."}
The framework supports masked-loss training. This means only the response portion contributes to the loss calculation. The model learns to generate appropriate responses without memorizing instructions.
Use Cases and Applications
Healthcare: Train diagnostic assistants on patient data without HIPAA violations. Models learn from local records, never sending data to the cloud.
Enterprise: Fine-tune models on internal documents, emails, and communications. Create specialized AI that understands company-specific terminology and processes.
Personal AI: Build assistants that learn your writing style, preferences, and communication patterns. The AI adapts to you, not the other way around.
Emerging Markets: The framework ensures operational continuity in high-latency geographical areas and emerging markets where reliable internet isn't guaranteed.
Education: Students can experiment with AI training on school devices without expensive infrastructure.
Technical Architecture
The system consists of three main components:
- Inference Runtime: Executes AI models on any GPU
- LoRA Fine-tuning Engine: Trains adapter modules efficiently
- Cross-platform Graphics Layer: Vulkan/Metal interface for universal GPU access
The framework introduces new APIs to llama.cpp without modifying existing code. This ensures compatibility with upstream updates and the broader llama.cpp ecosystem.
Comparison to Cloud Training
| Feature | Cloud Training | QVAC Fabric LLM |
|---|---|---|
| Data Privacy | Data leaves device | Data stays local |
| Internet Required | Yes | No |
| Hardware Cost | Pay per use | One-time device cost |
| Vendor Lock-in | Often locked | Any hardware works |
| Customization | Limited | Full control |
| Training Speed | Fast (expensive GPUs) | Varies by device |
Cloud training offers speed and convenience. Local training offers privacy, control, and independence. The choice depends on your priorities.
Common Challenges and Solutions
Challenge: Smartphone runs hot during training Solution: Reduce batch size or use shorter training sessions. Modern phones throttle to protect components.
Challenge: Out of memory errors Solution: Use more aggressive quantization (4-bit instead of 8-bit) or select a smaller base model. The dynamic tiling algorithm helps, but extreme memory limits still apply.
Challenge: Training takes too long on laptop Solution: Use a smaller model, reduce epochs, or train overnight. Desktop GPUs significantly outperform laptop GPUs.
Challenge: Quality seems worse than expected Solution: Check your training data quality. Use more examples, ensure consistent formatting, and consider increasing learning rate or epochs.
Advanced Configuration Options
Fine-tuning works better with proper hyperparameter selection:
Learning Rate: Start with 1e-5. Increase if training is slow, decrease if unstable.
LoRA Rank (r): Higher values (32, 64) capture more complexity but require more memory. Start with r=8 for phones, r=16 for laptops.
LoRA Alpha: Typically 16 or 32. Affects how much the adapter influences the base model.
Batch Size: Larger batches train faster but need more memory. Start small (4-8) on phones.
Epochs: How many times to process your entire dataset. More epochs may improve quality but risk overfitting.
Limitations and Considerations
QVAC Fabric LLM is powerful but has constraints:
- Training still takes significant time on mobile devices
- Larger models require substantial memory even with LoRA
- Multi-GPU training is experimental
- Quantization introduces small accuracy trade-offs
- Battery drain on phones during training
- Not suitable for training from scratch (only fine-tuning)
The framework targets fine-tuning existing models, not training new ones from random weights. What once required high-end cloud servers or specialized NVIDIA systems can now happen locally on devices people already own, but expectations should match hardware capabilities.
Future Developments
The project continues to evolve. Planned improvements include:
- Optimized kernels for mobile GPUs
- Reduced memory overhead through bindless descriptors
- Advanced compiler optimizations for Adreno
- Support for more model architectures
- Improved training speed across all platforms
The framework is open source, encouraging community contributions and extensions.
Community and Resources
Access the project through these channels:
- GitHub: Source code and documentation
- Hugging Face: Pre-trained adapters and model weights
- Technical Paper: Detailed methodology and benchmarks
- Discord/Forums: Community support and discussions
The technical overview on Hugging Face provides comprehensive benchmarks and implementation details.
Who Benefits Most
Developers: Build AI features without cloud dependencies or API costs.
Researchers: Experiment with new training techniques on accessible hardware.
Privacy-conscious users: Keep sensitive data completely local.
Organizations: Meet compliance requirements while deploying custom AI.
Emerging market users: Access AI training without reliable internet infrastructure.
Conclusion
QVAC Fabric LLM democratizes AI model training. You no longer need data centers or cloud subscriptions to customize language models. Your laptop works. Your phone works. Your existing hardware works.
The framework proves that local AI training isn't just possible—it's practical. Training takes longer on weaker hardware, but it completes successfully. Quality matches cloud-trained models. Privacy stays intact.
AI should not be something controlled only by large cloud platforms. QVAC Fabric LLM delivers on this vision. It gives individuals and organizations the tools to train AI on their own terms, with their own hardware, maintaining full control of their data.
Start with a small model and a simple dataset. Run a few training epochs. See your AI adapt to your needs. The technology is ready. The tools are free. The only requirement is willingness to experiment.