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Narwhal
Narwhal is an adaptive disaggregated inference framework that hot-swaps prefill and decode roles on GPU fleets.
Narwhal is an adaptive, disaggregated inference framework that automatically hot-swaps prefill and decode roles as demand changes without reloading model weights. It scales from a single GPU to multi-node deployments and serves completion and chat requests, either streamed or buffered, with latency-aware admission. The system uses a role controller that scores role splits using measured engine profiles, offered demand, and resident work to optimize service-level objectives. It features built-in fault tolerance, exporting metrics to Prometheus and Grafana, along with operator tooling for validation and diagnostics. Narwhal is designed for infrastructure operators and developers managing large language model inference fleets on Linux.
Narwhal at a glance
Key features
- Hot-swaps prefill and decode roles across GPU fleets
- Serves streamed or buffered completion and chat requests
- Fails over to a warm-standby router automatically
- Profiles engines and runs ordered benchmark points
- Exports router and engine metrics to Prometheus and Grafana
- Validates fleet files offline and collects diagnostic bundles
Use it for
- Serving chat and completion requests with disaggregated inference
- Managing dynamic GPU role splits under changing demand
- Monitoring inference fleet metrics and performance profiles
Alternatives to Narwhal
- GPUStack — Unifies AI model serving and GPU compute management across heterogeneous hardware.
- Reflex — It is optimized for cold-start latency and single-tenant decisions.
- fal — Access 1,000+ generative models and high-performance serverless GPUs.
- Infernix — Serves massive Qwen models quickly on a single consumer GPU with advanced caching.
- LMDeploy — Accelerates inference with high-performance engines and quantization tools.
- ncnn — No runtime dependencies and supports CPU and Vulkan GPU backends.
- AirLLM — Runs massive 70B+ parameter models on small 4GB consumer GPUs.
- TensorRT — Speeds up inference by 36X compared to CPU-only platforms.
- llm-d — Deploys production-grade LLM serving on existing hardware clusters.
- Strata — Runs large AI models locally on consumer gaming PCs without server reliance.
- Lumabri — Runs large AI models across multiple computers by sharing memory and layer workloads.
- Pulsar — It is the fastest inference engine for Qwen models on Apple Silicon.
All Narwhal alternatives