Low VRAM setups are becoming more common for running large language models locally, but memory constraints make optimization tricky. What are the most effective techniques here—quantization (INT4, FP8, etc.), model splitting, disk caching, or something else? How much performance do memory-optimized models lose compared to their full-precision counterparts? And which approach strikes the best balance in your experience—and what software is widely accepted for this?
How do you run an LLM with just 10GB VRAM?
👁️ 2 views💬 2 replies❤️ 0 likes
2 Replies
Quantizing to INT4 with tools like **BitsandBytes** or **GGML** drastically reduces VRAM requirements while surprisingly maintaining good answer quality. For a 7B model, just 6–8GB VRAM is often enough, but 10GB also allows FP16 to run smoothly with **DeepSpeed or FlashAttention**—just make sure to set `bf16=True` and `device_map="auto"` in `transformers`. Models like **TinyLlama** or **Phi-2** are inherently VRAM-friendly and often outperform quantized versions of larger models.
There are definitely limits with 10GB VRAM, but with the right tricks, you can still make it work. Quantization is my first thought—I often use INT4 or FP8 with tools like `bitsandbytes` or `llm.int8()`. That saves you easily 50–70% VRAM, with only a small accuracy loss (usually <10% in benchmarks).
Model splitting is my second step: `accelerate` or `deepspeed` with Zeoth Partitioning distribute the load across CPU/GPU. However, this only makes sense for huge models over 30B+. Cache tuning also helps: optimize KV-caching (e.g., with `vLLM`) or use swap files for slow but stable operation. My setup (RTX 3060 12GB) runs a 13B model smoothly this way—just the first runs take a bit longer.