Tag
Llm-serving tools
4 reviewed tools: 4 free or open source.
Order: free and open source first, then hosted products with a real free tier, then paid-led; catalog order inside each group, with quiet or archived repos last. Not a score, and no tool pays for its place.
4 tools
- NVIDIA DynamoApache-2.0 datacenter-scale LLM inference orchestration above vLLM, SGLang and TensorRT-LLM
Free / OSS · Apache-2.0 · 8.3k GitHub stars
VerdictApache-2.0 cluster-level LLM serving when you run vLLM/SGLang/TRT-LLM across many GPUs — free, but overkill below multi-GPU scale and benchmarks are vendor-run.
CatchBuilt for multi-GPU, multi-node serving — the README itself says one model on one GPU only needs the engine (see vLLM, or llama-swap for a local model switcher). Realistically NVIDIA hardware and Kubernetes; the headline speedups are vendor and partner benchmarks on GB200/GB300-class racks, so measure on your own GPUs. Lots of moving parts (frontend, router, workers, planner, discovery backend) and a fast release cadence: pin a version. Vs vLLM Semantic Router (model choice per request) and plain vLLM: Dynamo is the cluster scheduler, not a model router or an engine.
- llama.cppMIT local LLM inference in C/C++ on almost any hardware
Free / OSS · OSS/MIT · 132k GitHub stars
VerdictThe default local LLM engine — free, runs nearly anywhere, but you own the model choice and the tuning.
CatchIt runs GGUF models, so you convert or download quantized weights, and model licences are separate — check the model card before commercial use. Lots of knobs: speed depends on backend, quant and offload settings, so expect to tune. Vs Ollama (friendlier model manager on similar ground): pick llama.cpp when you want the engine and its flags directly. Vs vLLM or SGLang: those are GPU serving engines for many concurrent users; llama.cpp is the one for laptops, edge boxes and single-user servers.
- SGLangApache-2.0 high-throughput LLM and multimodal serving
Free / OSS · Apache-2.0 · 37.3k GitHub stars
VerdictA top-tier open LLM serving engine beside vLLM — free software, the bill is GPUs; benchmark both on your workload.
CatchA GPU serving engine, not a laptop chat app — expect driver, CUDA/ROCm and multi-GPU ops, and you pay for the hardware. Install currently needs `--prerelease=allow`. Vs vLLM (same lane): both are open, OpenAI-compatible serving engines — benchmark your own model and traffic rather than trusting either side's charts. Vs llama.cpp: SGLang is for many concurrent users on accelerators; llama.cpp is for one machine.
- llm-dApache-2.0 Kubernetes stack for distributed LLM inference
Free / OSS · Apache-2.0 · 4.9k GitHub stars
VerdictThe open, Kubernetes-native layer for multi-GPU LLM serving — free, but overkill below cluster scale.
CatchOnly makes sense if you already run Kubernetes and accelerators at scale — for one model on one GPU, a model server alone is enough. The headline gains in the README (for example 3x throughput from prefix-cache-aware routing) come from partner and vendor deployments on high-end GPUs, so measure your own. Still pre-1.0. Vs NVIDIA Dynamo (same layer): llm-d is Kubernetes-native and multi-vendor under the CNCF; Dynamo is NVIDIA-led.
FAQ
- What is the top free pick among Llm-serving tools?
- NVIDIA Dynamo leads the Stack Gems list for Llm-serving tools. Verdict: Apache-2.0 cluster-level LLM serving when you run vLLM/SGLang/TRT-LLM across many GPUs — free, but overkill below multi-GPU scale and benchmarks are vendor-run.
- How many Llm-serving tools does Stack Gems review?
- 4: 4 free or open source. Each review has a verdict and an honest Catch; no tool pays for its spot.
- Which Llm-serving tools can I self-host for free?
- NVIDIA Dynamo, llama.cpp, SGLang and llm-d.
- Which Llm-serving tools have the most GitHub stars?
- llama.cpp (132k), SGLang (37.3k), NVIDIA Dynamo (8.3k), per GitHub on 2026-10-19. Stars measure attention, not fit; read the Catch.
- What is the catch with NVIDIA Dynamo?
- Built for multi-GPU, multi-node serving — the README itself says one model on one GPU only needs the engine (see vLLM, or llama-swap for a local model switcher).