OpenBioLLM Llama 3 70B

OpenBioLLM-Llama-3-70B is a biomedical domain-specialized fine-tune of Llama 3 70B. Trained on biomedical literature, clinical notes, and medical Q&A. Run at Q4_K_M via Ollama (ollama pull openbiollm:70b) or llama.cpp with -ngl 999 -fa -c 4096. Q4_K_M file size ~40 GB on disk. Minimum VRAM: 48 GB — RTX A6000 (48GB) at Q4_K_M for 4K context. RTX 4090 24GB: Q3_K_M with KV offload. Recommended: A100 80GB at AWQ-INT4. Throughput: ~15-25 tok/s on A6000 at Q4_K_M. Standard Llama 3 architecture — compatible with all Llama inference stacks. Biomedical specialization means the model is significantly better at medical terminology, drug names, clinical reasoning, and literature summarization than base Llama 3 70B. But general knowledge outside biomedicine may be degraded due to catastrophic forgetting from domain fine-tuning. Use for: medical Q&A, clinical note summarization, biomedical research assistance, drug interaction checking. Not for: general chat, coding, creative writing. License: verify on huggingface.co/arcee-ai/OpenBioLLM-Llama3-70B.

Minimum: RTX 3090 24GB at Q3_K_M (4K). Recommended: RTX A6000 48GB at Q4_K_M (8K). Optimal: A100 80GB at AWQ-INT4. VRAM math: identical to base Llama 3 70B — 70B dense at Q4_K_M ≈ 40 GB. KV cache at 8K: ~10 GB. Total ~50 GB. A6000 48GB: borderline at 8K — trim to 4K. RTX 4090 24GB + KV offload for Q3_K_M. Dual RTX 4090 48 GB: Q4 at 8K. Mac Studio M4 Max 64GB: Q4_K_M at 5-10 tok/s. Cloud: A100 80GB at $5-10/hr. AWQ-INT4 enables 32K context. Biomedicine-specific prompts are typically shorter (2-4K tokens) than general chat — less context pressure.

1. Catastrophic forgetting. Domain fine-tuning on biomedical data degrades general knowledge. The model will hallucinate more on non-biomedical topics than base Llama 3 70B. 2. Medical accuracy liability. OpenBioLLM is a research model — not FDA-approved, not clinically validated. Medical outputs may be incorrect, outdated, or dangerous. Never use for clinical decision-making without human review. 3. Terminology precision at low quants. Medical terminology is precise — Q3 quantization may confuse drug names, dosages, or anatomical terms. Use Q4_K_M minimum for medical use. 4. Training data recency. Biomedical knowledge has a cutoff date from the fine-tuning data. New drugs, treatments, and guidelines published after the cutoff won't be known. Supplement with RAG on current literature.

Ollama for quick-start (if OpenBioLLM tag exists). llama.cpp for local use. vLLM for serving. Standard Llama architecture — any Llama-compatible stack works. For RAG: pair with a biomedical vector database (PubMed embeddings) for current literature grounding.

Mistake: Using OpenBioLLM for general medical advice as a production clinical tool. Fix: This is a research model. Always verify outputs against current medical guidelines. Never deploy for clinical decision-making without physician review. Mistake: Expecting OpenBioLLM to know about drugs released after its training cutoff. Fix: The model's knowledge is frozen at training time. Use RAG with current PubMed/clinical databases for recent information. Mistake: Using Q3 quantization for biomedical tasks. Fix: Q3 degrades terminology precision. Use Q4_K_M minimum. Q8 or FP16 if precision is critical. Mistake: Comparing OpenBioLLM to general-purpose models on non-medical benchmarks. Fix: OpenBioLLM is domain-specialized. It will underperform on general benchmarks compared to same-sized general models. Test only on biomedical tasks.