AMD Instinct MI350: The 35x AI Inference Leap That Could Reshape the Data Center
AMD has unveiled its most powerful AI accelerator yet: the Instinct MI350 series. Announced at AMD's Advancing AI event in December 2025, this next-generation compute platform promises a staggering 35x improvement in AI inference performance over the previous MI300 series—a leap that could finally challenge Nvidia's data center dominance.
MI350 Specifications
| Specification | MI350 | MI300X (Previous) |
|---|---|---|
| Architecture | CDNA-4 | CDNA-3 |
| Process Node | 3nm | 5nm |
| AI Compute (FP4/FP6) | 20 PFLOPS | 2.6 PFLOPS |
| HBM Memory | 288GB HBM3e | 192GB HBM3 |
| Memory Bandwidth | 12 TB/s | 5.3 TB/s |
| TDP | 750W | 700W |
| Inference Improvement | 35x | Baseline |
The 35x inference performance improvement comes primarily from:
- Advanced 3nm manufacturing process
- Native FP4 and FP6 compute support
- Doubled memory bandwidth
- 50% more HBM capacity
CDNA-4 Architecture Deep Dive
The new CDNA-4 architecture introduces several innovations:
CDNA-4 Features:
├── Native FP4/FP6 Compute
│ └── 8x more operations per cycle vs FP8
├── Advanced Matrix Cores
│ └── 4x larger than CDNA-3
├── Infinity Cache 3.0
│ └── 50% more on-chip cache
├── Coherent Memory
│ └── Unified memory across 8 accelerators
└── Optimized for Mixture-of-Experts
└── Efficient sparse activations
Why FP4/FP6 Matters
Lower precision formats enable massive efficiency gains:
| Precision | Bits | Use Case | Performance |
|---|---|---|---|
| FP32 | 32 | Training (legacy) | 1x |
| FP16 | 16 | Training | 2x |
| FP8 | 8 | Inference | 4x |
| FP6 | 6 | Inference | 5.3x |
| FP4 | 4 | Inference | 8x |
Modern inference workloads can maintain accuracy at FP4/FP6, making MI350's native support a major advantage.
AMD Helios: Rack-Scale AI
Alongside MI350, AMD previewed Helios—a complete rack-scale AI solution:
Helios Specifications
- Configuration: 8 MI350 accelerators per node
- Aggregate Memory: 2.3 TB HBM per node
- Interconnect: AMD Infinity Fabric 4.0
- Use Cases: Large-scale training, distributed inference
- Availability: 2026
Helios competes directly with Nvidia's GB200 NVL72 and is designed for:
- Training trillion-parameter models
- High-throughput inference clusters
- AI supercomputer deployments
Competitive Analysis
The AI accelerator market in late 2025 is intensely competitive:
| Accelerator | Vendor | Process | HBM | Inference PFLOPS |
|---|---|---|---|---|
| MI350 | AMD | 3nm | 288GB | 20 (FP4) |
| B200 | Nvidia | 4nm | 192GB | 18 (FP4) |
| Gaudi 3 | Intel | 5nm | 128GB | 8 (FP8) |
| TPU v6 | 5nm | 256GB | 15 (Int8) |
AMD's MI350 leads in memory capacity and raw FP4 performance, though Nvidia maintains advantages in software ecosystem (CUDA) and market presence.
Google TPUs vs. Nvidia GPUs
Interestingly, reports from December 2025 suggest Google TPUs are outperforming Nvidia GPUs in performance-per-dollar for inference workloads. Companies like Midjourney and Meta are reportedly negotiating deals to shift workloads to TPUs:
"For pure inference, TPUs are offering 40-60% better economics than H100s," according to industry analysts.
This opens opportunities for AMD to capture customers seeking alternatives to both Nvidia and Google's walled garden.
China Market: The MI308
For the Chinese market, AMD is preparing the MI308—a compliance-friendly version designed to meet U.S. export restrictions:
- Compute: Reduced to comply with regulations
- Status: Nearing commercial availability
- Customer Interest: Alibaba reportedly considering 40,000-50,000 units
This positions AMD to capture Chinese demand that Nvidia cannot serve due to export controls.
Software Ecosystem: ROCm 7.0
AMD's software stack has historically lagged Nvidia's CUDA. ROCm 7.0 addresses this with:
New in ROCm 7.0:
- PyTorch 2.5 Native Support - First-class integration
- JAX Optimization - Google's ML framework support
- vLLM Acceleration - Optimized LLM inference
- Triton Support - OpenAI's compiler framework
- FlashAttention 3 - Memory-efficient transformers
Early benchmarks show ROCm 7.0 achieving 90-95% of CUDA performance on common LLM inference workloads.
Customer Adoption
Major cloud providers have announced MI350 support:
| Provider | Status | Availability |
|---|---|---|
| Microsoft Azure | Confirmed | H1 2026 |
| Oracle Cloud | Confirmed | Q2 2026 |
| CoreWeave | Confirmed | Q1 2026 |
| Lambda Labs | Confirmed | Q1 2026 |
AWS and Google Cloud have not announced MI350 support, likely due to their preference for custom silicon (Trainium, TPU).
Pricing & Availability
| Product | Expected Price | Availability |
|---|---|---|
| MI350 | $20,000-25,000 | Mid-2025 |
| MI350X (Enhanced) | $30,000-35,000 | Late 2025 |
| Helios Node | Contact AMD | 2026 |
AMD is targeting aggressive pricing to win market share from Nvidia.
Verdict
The Instinct MI350 represents AMD's most credible challenge to Nvidia's data center dominance since the acquisition of ATI. With 35x inference improvements, native low-precision compute, and an improving software stack, AMD is positioned to capture a meaningful slice of the explosive AI accelerator market.
For enterprises frustrated with Nvidia's supply constraints, pricing power, and CUDA lock-in, MI350 offers a compelling alternative—assuming ROCm continues to close the software gap.
AMD Instinct MI350 is expected mid-2025. Pre-orders open to enterprise customers.
