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The primary bottleneck in local AI inference has shifted from parameter count to KV cache memory overhead, demonstrating that runtime and quantization innovations can extend the viability of existing hardware far more cost-effectively than constant GPU upgrades. Storing weights in VRAM is only the initial hurdle; pushing context toward 200K+ tokens causes the KV cache to rival or exceed the memory required for the model itself. Combining EXL3 quantization with DFlash2 speculative decoding preserves sufficient memory headroom for fast decode operations without sacrificing context capacity. Achieving around 63 tokens per second at 256K context cements older 24GB GPUs like the RTX 3090 as viable, private platforms for repository-scale ingestion and autonomous agent tasks. As an initial experimental build from the creator, users should anticipate stability quirks, bugs, and configuration tuning before it reaches production maturity.