2. Is cloud GPU cheaper than buying an RTX 5090?<\/strong><\/p>\n\n\n\nDepends entirely on utilization. Low GPU usage throughout the day means renting is cheaper. Continuous high utilization means buying amortises the cost over time. With RTX 5090 street prices running above the $1,999 MSRP through much of 2025-2026 due to supply constraints, renting became the more practical option for teams that couldn’t source hardware at launch pricing.<\/p>\n\n\n\n
3. Can I run DeepSeek’s flagship model on a single GPU?<\/strong><\/p>\n\n\n\nNo. DeepSeek V3 and R1 at full scale (671B parameters) require multiple 80GB GPUs with tensor parallelism. Two H100 80GB cards is the realistic entry point. The distilled variants at 7B, 14B, and 32B are a different conversation entirely and run on standard consumer hardware without issue.<\/p>\n\n\n\n
4. Do I need more VRAM for fine-tuning than for inference?<\/strong><\/p>\n\n\n\nYes, substantially more. Inference holds only model weights in VRAM. Fine-tuning holds weights, gradient tensors, and optimizer state simultaneously. That combination typically demands two to five times the memory of inference alone. Cloud GPU for fine tuning LLM<\/strong> jobs usually means stepping up at least one hardware tier from what inference alone requires.<\/p>\n\n\n\n5. What’s the cheapest cloud GPU that can run Llama 70B?<\/strong><\/p>\n\n\n\nThe cheapest cloud GPU for LLM inference<\/strong> that handles 70B models is typically an A100 40GB. At Q4, a 70B model sits around 43GB, which is tight but workable. An A100 80GB gives proper headroom. On some platforms, H100 NVL or L40S instances offer better cost-per-performance for sustained inference workloads.<\/p>\n\n\n\n6. Does quantization hurt model quality?<\/strong><\/p>\n\n\n\nAt Q4, mildly. For practical tasks like summarisation, drafting, and extraction, most users don’t notice the gap between Q4_K_M and full precision in everyday use. Complex reasoning chains and long multi-step tasks show the difference more clearly. Q8 sits in between: quality close to full precision, memory requirements meaningfully lower than running uncompressed.<\/p>\n\n\n\n
7. Should I use Ollama or vLLM for a production endpoint?<\/strong><\/p>\n\n\n\nFor production, vLLM. Ollama is excellent for local use and individual development workflows. Under genuine