What is this?
Flare is a local inference server built specifically for NVIDIA SM120/SM121 GPUs, designed for long conversations, coding agents, and repeated tool use. It is a research prototype released as open source to gather early feedback and enable collaboration.
Flare is a local inference server built specifically for NVIDIA SM120/SM121 GPUs, designed for long conversations, coding agents, and repeated tool use. It is a research prototype released as open source to gather early feedback and enable collaboration.Is this a full replacement for something like vLLM?No. vLLM covers a much broader range of models, hardware and deployment patterns; Flare deliberately trades that breadth for a focused serving contract around selected models and local, single-machine workloads.
No. vLLM covers a much broader range of models, hardware and deployment patterns; Flare deliberately trades that breadth for a focused serving contract around selected models and local, single-machine workloads.How do I get it running?Start with model preparation and serving, using the CLI guide for configuration; the core design doc explains the architectural reasoning if you want more depth.
Start with model preparation and serving, using the CLI guide for configuration; the core design doc explains the architectural reasoning if you want more depth.Do I need specific GPU hardware?Yes. It targets NVIDIA SM120/SM121 GPUs found in workstations and compact systems such as DGX Spark; it's not a general multi-vendor inference server.
Yes. It targets NVIDIA SM120/SM121 GPUs found in workstations and compact systems such as DGX Spark; it's not a general multi-vendor inference server.Is this ready for production use?No, the project explicitly calls itself a research prototype made available to collect early feedback and enable technical collaboration, not a production-grade serving system.
No, the project explicitly calls itself a research prototype made available to collect early feedback and enable technical collaboration, not a production-grade serving system.Can a coding agent just read this README to use Flare?Not directly - the README tells coding agents to skip it and read AGENTS.md instead for setup and usage instructions.
Not directly - the README tells coding agents to skip it and read AGENTS.md instead for setup and usage instructions.Why not just use vLLM for local serving?Flare is aimed at making the most of a single workstation's memory, bandwidth, and compute for long, repeated-tool-use sessions, treating conversation state (including recurrent state and KV caches) as something that can be reused, branched, and resumed - a narrower goal than vLLM's broad production coverage.
Flare is aimed at making the most of a single workstation's memory, bandwidth, and compute for long, repeated-tool-use sessions, treating conversation state (including recurrent state and KV caches) as something that can be reused, branched, and resumed - a narrower goal than vLLM's broad production coverage.What are Ignition and Helios?Ignition is the Python/PyTorch build-time stage that compiles models and captures CUDA graphs into a runtime package; Helios is the small Rust runtime that serves that package, handling scheduling, cache storage, and request handling without Python.
Ignition is the Python/PyTorch build-time stage that compiles models and captures CUDA graphs into a runtime package; Helios is the small Rust runtime that serves that package, handling scheduling, cache storage, and request handling without Python.What are similar projects?LMCache, TensorFold, twinspark, pd-bridge, qwen3.8-Flash-DGX, v100-skinny, ds4, InstinctFlash
LMCache, TensorFold, twinspark, pd-bridge, qwen3.8-Flash-DGX, v100-skinny, ds4, InstinctFlashWhich topics is it in?dgx-spark, kv-cache, inference-engine, model-serving, transformers, llm-inference, vllm, coding-agents, gpu-acceleration, local-llm, inference, nvidia
dgx-spark, kv-cache, inference-engine, model-serving, transformers, llm-inference, vllm, coding-agents, gpu-acceleration, local-llm, inference, nvidia