Products / In-process AI

jitLLM

Serve LLMs inside your Java services, on any GPU.

Talk to us about jitLLM jitllm
At a glance● data stays put
1.4×llama.cpp prompt-processing speed
0sidecars or network hops per call
4GPU vendors: NVIDIA, AMD, Intel, Apple
The problem

AI that needs a Python sidecar

Adding an LLM to a Java service usually means a Python sidecar: a second stack to run and secure, and a network hop on every call. Prompts and data leave the process that owns them.

What Beestream does

A drop-in GPU path.

jitLLM serves LLMs inside the JVM. It loads GGUF models and runs them on your GPU in the same process as the rest of your service, so prompts and data stay where they are.

What you get
  • Llama 3, Mistral, Qwen 3, Phi-3 and DeepSeek model families
  • FP16 and Q8 weights with fused dequantize-and-compute kernels
  • Fits LangChain4j, Spring AI and Quarkus applications
  • Models, prompts and data stay on your own hardware
Runs onone jar
NVIDIAAMDIntelApple

Powered by TornadoVM · cuBLAS · cuDNN · cuDF · cuVS

The Beestream platform

Start with one product. Add the rest on the same GPUs.

jitLLM is one part of the Beestream platform. Once it is in, the other products run on the same GPUs, under the same support, with no new stack to adopt.

7.3×Apache Iceberg GPU accelerationfaster Iceberg compaction UDFsSpark RAPIDS GPU accelerationcompiled to GPU kernels 19×JVector GPU accelerationfaster vector index builds 2.4×jitNNfaster than PyTorch, ResNet-50 13×jitCastfaster forecasts
Contact

Tell us what is costing you CPU time. We'll show you the GPU path.

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