Domain-specific LLM fine-tuned on 500K financial instructions. Get instant, accurate answers on investing, economics, accounting, and market analysis.
Trained on diverse financial datasets and optimized for AMD hardware, our model delivers accurate, context-aware financial intelligence.
Powered by vLLM on AMD Instinct MI300X with Flash Attention 2. Sub-100ms responses for real-time financial decision making.
Fine-tuned on 500K financial instruction examples covering investing, monetary policy, accounting, and risk analysis.
Self-host on AMD Developer Cloud or your own infrastructure. Your financial data never leaves your control.
Full training code, datasets, and weights available on Hugging Face. Apache 2.0 licensed. No black boxes.
Handles complex numerical reasoning, financial calculations, comparative analysis, and scenario modeling.
OpenAI-compatible API via vLLM. Drop-in replacement for GPT-4 in your existing financial tools and workflows.
See why AMD Finance LLM is the smart choice for financial AI.
| Feature | AMD Finance LLM | Generic GPT-4 | BloombergGPT | FinBERT |
|---|---|---|---|---|
| Domain-Specific Training | ✓ 500K finance instructions | ✗ General purpose | ✓ Finance focused | ✓ Sentiment only |
| Open Source | ✓ Apache 2.0 | ✗ Closed | ✗ Closed | ✓ Open |
| Self-Hostable | ✓ On-prem / cloud | ✗ API only | ✗ Enterprise only | ✓ Yes |
| AMD Hardware Optimized | ✓ Native ROCm support | ✗ CUDA only | ✗ NVIDIA only | ✗ CPU/GPU generic |
| Fine-Tunable | ✓ Full weights + code | ✗ No | ✗ No | ✓ Limited |
| Inference Cost | ✓ Free self-host | $$$ Per token | $$$ Enterprise | ✓ Free |
| Conversational QA | ✓ Full chat + reasoning | ✓ Yes | ✓ Yes | ✗ Classification only |
Start free, scale as you grow. No hidden fees, no surprises.
See what early users are saying about AMD Finance LLM.
"The domain-specific training really shows. When I ask about bond yield curves or Fed policy, the answers are precise and contextual. Beats generic LLMs for finance work."
"Running this on our AMD MI300X cluster is a game changer. The vLLM integration gives us GPT-4 level throughput at a fraction of the cost. Fully self-hosted too."
"We compared this against BloombergGPT for our risk analysis pipeline. AMD Finance LLM matched accuracy while being open source and customizable. Hugely valuable."
Everything you need to know about AMD Finance LLM.
AMD Finance LLM is specifically fine-tuned on 500,000 financial instruction examples covering investing, monetary policy, accounting, and market analysis. Unlike general-purpose models, it understands financial terminology, regulatory frameworks, and numerical reasoning at a deeper level. Plus it's fully open source and optimized for AMD hardware.
Absolutely. The entire stack — model weights, training code, Docker configuration, and serving scripts — is available on Hugging Face under Apache 2.0 license. You can deploy on AMD Developer Cloud MI300X instances, on-premise ROCm servers, or even consumer AMD GPUs with quantized versions.
For full 7B parameter inference with vLLM, we recommend at least one AMD Instinct MI300X (192GB HBM3) or equivalent. For development/testing, the model can run on a single MI210/MI250 with gradient checkpointing. Quantized versions for 8GB VRAM are coming soon.
While AMD Finance LLM demonstrates strong financial reasoning, it should be used as an analytical assistant alongside human expertise and verified data sources. Never rely solely on AI for investment, trading, or regulatory compliance decisions. We provide the tools — you provide the judgment.
Yes! The training script (train.py) is included in the repository and uses standard TRL SFTTrainer. You can fine-tune on proprietary datasets, add company-specific knowledge, or adapt for regional regulations. The Docker container includes all dependencies for ROCm training.
In the Space settings, enable "Use External API / vLLM" and enter your server URL and API key. The UI supports any OpenAI-compatible endpoint including vLLM, TGI, or cloud providers. Test the connection with the built-in "Test Connection" button before chatting.
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