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MangoBoost · Enterprise LLM inference

Serve LLMs faster.
On the hardware you already own.

LLMBoost is a drop-in, OpenAI-compatible inference server that delivers up to the throughput of vLLM — with auto-tuning for your exact hardware.

$ pip install llmboost_hub
$ lbh serve
Hottest models, tuned
DeepSeek-V3.2 · Kimi-2.6
and many more, out of the box
up to
Throughput vs vLLM
same model, same GPU
OpenAI API compatible
Drop-in /v1 server
zero client code changes

Two ways to run it

LLMBoost Hub fastest

One CLI that pulls the image, downloads the model, and serves it.

lbh serve deepseek-ai/DeepSeek-V3.2
LLMBoost Hub guide →

Manual full control

Prefer to manage the container yourself? Run the LLMBoost image directly with docker run for full control over flags, mounts, and devices.

Manual guide →

Built for production

Auto-tuned for your box

Parallelism, batching, and memory tuned to your exact GPU. No knob-twiddling.

Scales across GPUs

Serve a model across multiple GPUs on a single node — tensor and data parallel.

Streaming & multimodal

Token-by-token streaming, structured outputs, tool calls, vision models.

AMD Instinct

Tuned for AMD Instinct MI300X / MI325X / MI355X.

Deploy your first model in one command.

Free trial license. No infra changes. Keep your data on your own GPUs.