Local-LLM

Event Core A developer has successfully deployed the Nemotron-3-Super-64B math-tuned model on 48GB VRAM, achieving a 500k context window and 21 tok/s throughput, outperforming full-scale 120B models in complex agentic coding workflows. Bagua Insight ▶ The Triumph of Parameter Efficiency: This proves that domain-specific fine-tuning (math/logic) combined with aggressive KV Cache optimization allows mid-sized models to punch well above their weight, challenging the dominance of massive, unoptimized LLMs. ▶ Democratizing Long-Context: The 500k context threshold is no longer exclusive to cloud-scale infrastructure; it is now accessible on prosumer hardware, enabling local agents to ingest entire codebases. Actionable Advice For Developers: Prioritize domain-specific fine-tuned models for coding tasks rather than chasing raw parameter counts; logic-heavy models often exhibit superior reasoning in agentic loops. For Architects: Invest in KV Cache quantization and hardware-aware attention kernels to maximize context windows within constrained VRAM environments.