Sovereign intelligence at the point of care. Local LLM deployment, RAG pipelines, and next-generation semiconductor architectures — without a single byte leaving your premises.
Run frontier language models directly on clinical workstations. No API calls. No data egress. Full control.
Alibaba's Qwen series offers exceptional multilingual clinical reasoning. Deploy via Ollama or llama.cpp on a single workstation with quantized GGUF weights — ideal for SOAP note generation, differential diagnosis assistance, and protocol lookup.
Fine-tuned on PubMed and medical literature, BioMistral excels at entity extraction, ICD coding assistance, and clinical text classification. Runs comfortably on 8GB VRAM with 4-bit quantization.
Minimum viable and recommended configurations for running clinical language models in a private practice or hospital department.
Retrieval-Augmented Generation architectures that keep your knowledge base on-premise and your inference trustworthy.
A complete reference architecture: Qdrant vector database on-device, sentence-transformers for embeddings, Ollama as the inference server. Ingest FHIR bundles, PDFs, and structured EHR exports into a queryable semantic index.
Clinical documents require specialized chunking — discharge summaries, lab reports, and imaging notes have different semantic structures. Semantic chunking with clinical boundary detection preserves diagnostic context that naive sliding-window approaches destroy.
The next frontier in edge inference. Understanding what 2nm node architectures mean for always-on, low-latency clinical AI.
TSMC's N2 process delivers ~15% speed uplift and ~25–30% power reduction vs. N3E. For always-on clinical monitors and diagnostic wearables, this translates to multi-day battery life without cloud offload.
Apple Silicon's unified memory architecture makes the M3/M4 Max a compelling clinical AI workstation — 38 TOPS Neural Engine, 128GB unified memory, and macOS privacy guarantees. Running 70B models locally is no longer academic.
45 TOPS on-device NPU enables real-time clinical inference on thin-and-light devices. The first viable architecture for portable patient-side clinical AI that doesn't require a network connection.
Hands-free clinical documentation, real-time diagnostic overlays, and Ayurvedic constitution assessments via wearable AI vision.
Integrating Meta's AI glasses with a local LLM sidecar for real-time patient note capture during ward rounds. Voice-to-SOAP note generation with zero cloud transmission — all processing on an edge device in the clinician's pocket.
Computer vision models running on-device to assist with Ayurvedic Prakriti (constitution) assessment — analysing tongue color, skin texture, nail morphology, and facial features mapped against classical Dosha parameters. All inference is local.
Share your local deployment experiences, hardware configs, or questions about on-premise clinical AI.
Running Qwen 14B on an RTX 4090 workstation for radiology report summarisation. Latency is under 2 seconds per report. The GGUF Q4_K_M quantization hits the right trade-off between speed and accuracy for our use case.
The hybrid RAG stack (Qdrant + BM25) mentioned here is exactly what our medical record retrieval needed. Semantic-only search was missing too many keyword-critical lab results.
Smart glasses for Prakriti assessment is something I've been prototyping. The tongue analysis model in particular is showing real promise — will share benchmarks in the Telegram group this week.