Most inference engines focus on how fast a model generates tokens. @TandemLLM asks a different question: how fast can local AI actually finish the job? Because faster inference means little if an agent keeps looping, loses context, or struggles to use its tools. That's what we're trying to change. 👇 vLLM, SGLang and TensorFold are powerful inference engines. They focus heavily on running and serving models efficiently. Tandem is taking a more integrated approach. We're building both the inference engine and the environment around it: → Inference: Custom kernels, quantisation, speculative decoding and StairCut to squeeze more performance out of local hardware. → Intelligence: A conductor that understands tasks, selects skills, adjusts reasoning effort and catches agent loops or broken tool calls. → Continuity: Persistent caches and memory so conversations can resume without repeatedly processing everything. → Execution: Sandboxed tools, agent workflows and a dashboard that shows what the system is actually doing. → Flexibility: OpenAI-compatible APIs, with the ability to work alongside other inference backends. The goal isn't to replace everything vLLM, SGLang or TensorFold does. It's to build a complete local AI system where inference, reasoning and execution work together. And we're just getting started. Tandem is now a team of three: me, @0xWhiteMage and @AgentSparko. We're looking to bring more builders on board. Whether you're into CUDA, kernels, quantisation, inference optimisation, agentic systems or local AI infrastructure, we'd love to hear from you. We're building this in the open, and there's plenty left to explore. Interested in building the future of local AI with us? DM me. tandemllm.0xbakeer.com