MedGemma, Llama 70B demand workstation budgets

The hot take is simple: if your target is 70B local inference for serious research work, you are no longer shopping for a normal laptop, you are shopping for a workstation budget. MedGemma 27B is the more realistic entry point; Llama 70B is where “local” gets expensive fast.

• –Community replies converge on roughly 24GB VRAM as the bare minimum for getting started with large local models, but 70B-class inference at practical quantizations is closer to 48GB once weights, context, and KV cache are counted.
• –Mac gets recommended for quiet operation, efficiency, and unified memory, with a 64GB-plus Mac Studio or MacBook Pro framed as the realistic floor for 70B experimentation rather than an ideal setup.
• –Windows with NVIDIA still wins on raw throughput, broader tooling support, and upgradeability, especially if you are comparing Apple silicon against cards like the RTX 4090, dual 3090s, or 48GB workstation GPUs.
• –Google positions MedGemma as a medical variant in the Gemma family, but it is not clinical-grade, which matters if the end goal is medical research papers rather than casual summarization.
• –Meta’s Llama 3.1 70B remains a heavyweight open model with a 128K context window, but the Reddit consensus is that newcomers should often start smaller before paying the 70B hardware tax.