TL;DR
Short version: no. I dropped a much older GPU (Quadro P2000, 5GB, Pascal, 2016) next to an RTX 3090 (24GB, Ampere) on the same box, ran the same context-length ladder (8K→128K) through Ollama and vLLM on qwen3-coder:30B-A3B, and got zero extra usable context in either engine — and a 74% decode-speed hit for the trouble. Ollama hits the identical Chunk too big wall at ctx=65536 whether the P2000 is there or not. vLLM refuses tensor-parallel across the two cards entirely — not a VRAM problem, a flat compute-capability rejection (Minimum capability: 75. Current capability: 61.) that fails in 40 seconds, before any memory profiling. And the one real, measured effect of adding the P2000 to Ollama: decode speed goes from 76 → 19.5 tok/s at ctx=49152 once the P2000 gets pulled in as an actual compute device.
Full narrative version — the two-stage collapse, the prompt-cache validation bug caught mid-sweep, the CUDA13-silently-drops-Pascal finding — is on Medium.## The setup
ardi (dual Xeon E5-2680 v4, 128GB RAM, openSUSE Leap) has a Quadro P2000 sitting in a second slot next to the RTX 3090 this whole series has run on so far. Same model as phase 1 (qwen3-coder:30B-A3B), same box, four legs: {Ollama, vLLM} × {3090 only, 3090+P2000 tandem}, priced through HomeLab Monitor against real GPU power draw.
