@tornadovm.org
Official #TornadoVM - JIT Compiler & Runtime system for running JVM applications on heterogeneous hardware.
@stratika.bsky.socialReposted by @tornadovm.orgYesterday my colleagues @maryxek.bsky.social and @mikepapadim.bsky.social showed live demos about using NVIDIA cuFFT and cuBLAS with @tornadovm.org for signal filtering and @jitllm.io!
NVIDIA performance is a jar away from Java.
@kotselidis.netReposted by @tornadovm.orgWith the latest #TornadoVM version you can accelerate end-to-end Apache #Flink pipelines on the GPUs with native #LLM inference. No Python, no external APIs, all in one deployable jar!
@tornadovm.orgOptimized GPU libraries (e.g. cuBLAS, cuDNN, etc.) by NVIDIA, now one jar away for your Java - without caring about JNI.
We also show demos with Apache Flink and @jitllm.io.
Watch our talk in @devoxx.com 👇
youtu.be/6SPnBmQrYEk?...
@jitllm.ioReposted by @tornadovm.orgjitLLM is part of the @langchain4j.dev 1.21.0 release! 🚀
Read more here: docs.langchain4j.dev/integrations...
@kotselidis.netReposted by @tornadovm.orgOur latest addition to the TornadoVM family. Blazing fast AI inference at native speed!
@jitllm.ioReposted by @tornadovm.orgCan Java compete with llama.cpp/ollama? YES! With @jitllm.io!
jitLLM uses NVIDIA CUDA features and libraries (cuBLAS, cuDNN) and reaches 0.90× to 1.12× of llama.cpp's throughput!
jitLLM is empowered by @tornadovm.org and will be soon in @langchain4j.dev and @quarkus.io.
@tornadovm.orgNext week we will be in @devoxx.com and speak about
@tornadovm.org, @jitllm.io and how we accelerate Java on GPUs, using #NVIDIA native libraries!
Come and talk to us, to learn more!
@tornadovm.orgTornadoVM 7.0.0 is out!
New TileContext API: Write #GPU kernels from #Java in tiles, not threads, via #NVIDIA #cuTile model.
Now, cuTile methods can be mixed with cuBLAS functions in one TaskGraph, optimizing data transfers.
@tornadovm.orgJava 27 arrived yesterday! And we are compatible since Day-0!
#opensource #Java #TornadoVM #GPU
@stratika.bsky.socialReposted by @tornadovm.orgNew #TornadoVM release, and it's JVMCI-free.
📌No more separate jdk25 branch, no back-syncing every PR from develop - one build for JDK 21–27.
📌Native code also migrated from JNI to Panama's FFM API - 7.5k lines of C++ gone.
Kudos to all contributors! 👏
#opensource #Java #GPU #AI
@kotselidis.netReposted by @tornadovm.orgTornadoVM is now modernized and forwards compatible with any JDK > 21 while receiving significant performance boosts! Can't wait to see those benefits translating to our AI inference engine in our next release!
@tornadovm.orgTornadoVM 6.0.0 is here - JVMCI-free and JNI-free.
✅ Compatible with any JDK 21-27 distribution.
✅ Cold start down 37%.
✅ 4.1× faster GEMM with Tensor-Core MMA.
✅ ~7,400 lines of C/C++ glue deleted.
📖 New blog: www.tornadovm.org/blogs/tornad...