More GPUs or a Smaller Cache? Tensor Parallelism versus KV Compression for Memory-Bound LLM Serving
Read the original on arXiv AI →The paper compares two strategies for handling memory limits in large language model (LLM) serving: tensor parallelism, which distributes weights and KV cache across multiple GPUs, and KV compression, which reduces cache size via quantisation and eviction on a single GPU. Using a cost‑normalised simulator calibrated on A100, A40, and H100 hardware, the authors find that across two models (Llama‑2 7B and 70B) and various GPU configurations, compression consistently outperforms tensor parallelism in cost per million tokens, offering 1.20× to 2.00× savings. The study identifies a model‑size threshold (~36B parameters on an 80 GB card) where compression dominates, while tensor parallelism becomes necessary only for larger models where weights alone exceed a single GPU’s capacity.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.