…May 21, 2026 From Build to Benchmark: ONNX Model Serving with Triton Inference Server on AMD GPUs — ROCm Blogs Step-by-step guide to building, deploying, and benchmarking ONNX models with Triton…
…tuning and benchmarking to production serving on AMD Instinct™ GPUs with a consistent workflow. Efficient Inference, Optimized by Default High Throughput Without Per-model Tuning Overhead ROCm pairs quantized model packages with…
…May 21, 2026 From Build to Benchmark: ONNX Model Serving with Triton Inference Server on AMD GPUs — ROCm Blogs Step-by-step guide to building, deploying, and benchmarking ONNX models with Triton…
…Verification Kernel benchmark. Kernel benchmark plus end-to-end serving benchmark. Knowledge base RAG-style knowledge base and reverse knowledge. perf_knowledge: structured kernel optimization knowledge across operators, backends, GPU generations, dtypes…
…Its kernels are optimized for different algorithms and quantization, and it’s fully compatible with PyTorch. Because ROCm is open source, we can examine the kernels directly, take advantage of community contributions…
…ATOM is the primary ROCm-native execution path in this design, coordinating model execution, scheduling, KV cache management, graph execution, quantization-aware kernels, and parallelism strategies such as tensor parallelism, data parallelism…
…The complete reference kernels for all four versions below, along with the benchmark harness, are available here . Version 1: The Starting Point Full kernel: v1_register_staged.py The V1 kernel serves…
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