Autonomous agents like Cursor Composer, Claude Code, and LangGraph operate fundamentally differently from single-turn conversational chatbots: they accumulate massive context histories across rapid tool calls and multi-turn execution logs.
As context expands toward 64k and 128k tokens, the Key-Value (KV) cache frequently overtakes the static model weight footprint in GPU VRAM, causing out-of-memory faults on standard consumer GPUs.
Our benchmarks on NovaPod's pre-configured vLLM and SGLang templates demonstrate that running a 32B model like Qwen 2.5 with a 64k active context window requires approximately 42 GB of VRAM in FP16, or 28 GB with FP8 KV cache quantization.
This makes 48GB cards (such as the NVIDIA L40S, RTX A6000, and RTX 6000 Ada) and 32GB cards (NVIDIA RTX 5090) the undisputed sweet spots for developers running agent sandboxes on NovaPod, delivering sub-15s startup and maximum throughput per dollar.