AMD Instinct MI400 (CDNA Next)
Overview
AMD Instinct MI400 is the next-generation flagship GPU after the MI350, featuring the CDNA Next architecture, shipping in 2026. It features 432GB HBM4 memory, 19.6 TB/s bandwidth, 40 PFLOPS FP4 compute (dense), and a TDP of approximately 1,000 W.
MI400 is the core of the AMD Helios rack — 72 MI400 GPUs + 36 EPYC Venice CPUs + Pensando Vulcano NICs, achieving 260 TB/s scale-up interconnect via Ultra Accelerator Link (UALoF). It is AMD's flagship rack solution competing with NVIDIA NVL72.
Core Specifications (per GPU)
| Item | Specification |
|---|
| Architecture | CDNA Next |
| Process Node | TSMC 3nm / 2nm (estimated) |
| Transistor Count | ~200 billion (estimated) |
| Memory | 432 GB HBM4 |
| Memory Bandwidth | 19.6 TB/s |
| FP4 Matrix | 40 PFLOPS (dense) |
| FP8 Matrix | 20 PFLOPS (dense) |
| FP16/BF16 Matrix | 10 PFLOPS |
| FP32 | 250 TFLOPS (estimated) |
| TDP | ~1,000 W (liquid cooling required) |
| PCIe | Gen 6 |
| DC Network | Pensando Vulcano 800G NIC (estimated) |
| Launch | 2026 |
📌 Data convention: AMD uses dense compute as the official standard; contemporary NVIDIA products (Rubin R200) use sparse compute — not directly comparable. All MI400 compute figures in this table are dense.
MI400 vs MI350 Generational Upgrade
| Metric | MI350 (CDNA 4) | MI400 (CDNA Next) | Improvement |
|---|
| Architecture | CDNA 4 | CDNA Next | New generation |
| Process Node | TSMC 3nm | TSMC 3/2nm | More advanced |
| Memory | 288 GB HBM3e | 432 GB HBM4 | 1.5× |
| Memory Bandwidth | 8 TB/s | 19.6 TB/s | 2.45× |
| FP4 (dense) | 20 PFLOPS | 40 PFLOPS | 2× |
| FP8 (dense) | 10 PFLOPS | 20 PFLOPS | 2× |
| TDP | ~1,000 W | ~1,000 W | Unchanged |
| PCIe | Gen 5 | Gen 6 | 2× |
| Launch | Q4 2025 | 2026 | — |
AMD Helios Rack (72-GPU Super Node)
| Item | Configuration |
|---|
| GPU Count | 72 MI400 |
| CPU Count | 36 EPYC Venice (256 cores each) |
| Total HBM | 31.1 TB HBM4 (432GB × 72) |
| Scale-up Interconnect | Ultra Accelerator Link 260 TB/s |
| Scale-out Network | Pensando Vulcano 800G |
| FP4 Compute (rack) | 2.88 EFLOPS (dense) |
| FP8 Compute (rack) | 1.44 EFLOPS (dense) |
| TDP (rack) | ~80 kW |
| Cooling | Liquid cooling required |
Ultra Accelerator Link (UALoF / UALink) = an open-standard scale-up interconnect co-driven by AMD + Broadcom + Intel, aiming to replace the single-vendor NVLink ecosystem. Helios is among the first 260 TB/s-class UALoF racks.
MI400 vs Rubin R200 (Contemporary Comparison)
| Metric | MI400 (CDNA Next) | Rubin R200 |
|---|
| Memory | 432 GB HBM4 | 288 GB HBM4 |
| Memory Bandwidth | 19.6 TB/s | 22 TB/s |
| FP4 Compute | 40 PFLOPS (dense) | 50 PFLOPS (sparse) |
| FP4 dense equivalent | 40 PF | ~25 PF |
| NVLink/UALoF | 260 TB/s (rack) | 3.5 TB/s/GPU |
| CPU | EPYC Venice | Vera ARM 88-core |
| DC Network | Pensando 800G | ConnectX-9 14.4 Tbps |
| Ecosystem | ROCm 7/8 | CUDA 13 |
| Standardization | UALoF open | NVLink proprietary |
AMD advantages: Open ecosystem, large memory, standardized scale-up; NVIDIA advantages: Mature software ecosystem, DC networking, per-GPU NVLink speed.
Recommended Deployment Configurations
| Scenario | Recommended Configuration |
|---|
| 700B+ model training | Helios rack (72 GPUs, single rack can run 700B models) |
| 1T+ mega-model training | Multi-rack + UALoF cross-rack interconnect |
| Ultra-low-latency inference | MI400 + FP4 + vLLM/AMD-SGLang |
| Scientific computing | MI400 + ROCm 7/8 + OpenMP |
| Multimodal generation | MI400 (432GB fully reserved) |
ROCm Software Ecosystem
- ROCm 7.x (2025 GA): PyTorch / JAX / Triton fully optimized
- ROCm 8.x (2026): CDNA Next launch, full FP4 / FP8 support
- vLLM 0.7+ (AMD-SGLang optimized version)
- AMD Composable Kernel (CK): Analogous to CUDA Cores, open source
- MIGraphX / ONNX-Runtime: Inference engines
- Infinity Hub: AMD official reference implementations
Use Cases
- ✅ Large-scale LLM training (700B+ models, Helios 72-GPU node)
- ✅ Open ecosystem preference (UALoF open interconnect, ROCm open source)
- ✅ Ultra-low-latency inference (FP4 + large memory)
- ✅ Scientific computing (FP64 advantage + large memory)
- ❌ Legacy NVIDIA ecosystem lock-in (CUDA-only)
- ❌ Edge deployment (power/physical footprint unacceptable)