TL;DR — I tried to run Qwen2.5-7B-Instruct-1M on a consumer laptop (RTX 3050 Laptop 6GB VRAM) and mapped the literal feasibility frontier. All evidence in JSON, drift-CI enforced. Three honest findings: 4k context = the hard ceiling on Windows transformers + bitsandbytes int4 NF4. 5k, 6k, 8k all OOM at the first attention forward pass. The 4k cell passes only because Windows kernel shared-memory PCIe spillover (WDDM overcommit) lets allocations spill to system RAM at ~10x latency tax — peak measured 10.8GB on a 6GB GPU. WSL2 + vllm cannot even fit the model. vllm 0.7.3 memory profile literal log: "model weights take 5.43GiB; PyTorch activation peak memory takes 1.42GiB; the rest of the memory reserved for KV Cache is -0.94GiB ". 0 GPU cache blocks allocated, 0.00x concurrency at 4200 tokens. Linux nvidia driver does not provide an equivalent shared-mem fallback — vllm sees only physical 6GB and refuses.…