ROCm 6.2AMD Instinct MI300X (192GB HBM3)vLLM ROCm EnginePyTorch FSDPTriton gfx942

AMD ROCm 6.2 & Instinct MI300X AI Training & Inference Studio

Scale open-weights LLMs (Llama-3.3 70B, DeepSeek-V2.5, Qwen2.5) on AMD Instinct™ MI300X accelerators with 192GB HBM3 memory per GPU (5.3 TB/s bandwidth). Deploy vLLM with FlashAttention-2 HIP kernels, PyTorch FSDP multi-node distributed training, and Kubernetes AMD GPU device plugins.

Memory Bandwidth

5.3 TB/s

192 GB HBM3 per accelerator

Single-Node Context

128k Tokens

1.5 TB aggregate node VRAM

Peak Compute (FP8)

1,307 TFLOPS

Matrix Core gfx942 target

TCO Reduction

-42% vs H100

Open ecosystem & higher VRAM

⚙️ MI300X Parameters Live Sync

Workload Architecture

Target Foundation Model

Instinct MI300X Topology

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HBM3 Density Advantage

With 192GB HBM3 per GPU, a single 8-GPU MI300X server holds 1.536 TB of high-speed memory at 5.3 TB/s bandwidth, running 70B parameter models at full FP16 with 128k context without tensor-parallel sharding across multiple nodes.

// Compiling ROCm 6.2 & MI300X blueprints...

📐 Architectural Execution Blueprint

AMD Instinct MI300X Fabric

AMD ROCm 6.2 and Instinct MI300X AI Training and Inference Flow