AMD Instinct MI350 vs NVIDIA: AI & HPC Compute Redefined
At Hot Chips 2025, AMD officially announced the Instinct MI350 GPU accelerators, a new flagship based on the CDNA 4 architecture.
With 185 billion transistors, 288GB of HBM3e memory, and up to 10 PFLOPS of compute performance, the MI350 is AMD’s most ambitious move yet against NVIDIA’s dominance in AI accelerators.
Architecture: CDNA 4 + 3D Packaging #
The AMD Instinct MI350 leverages 3D multi-chip module (MCM) packaging, manufactured using TSMC’s N3P + N6 nodes, and integrated with CoWoS-S interconnect technology for extreme bandwidth efficiency.
Each GPU includes:
- 8 Compute Dies (XCDs)
- 2 I/O Dies (IODs) for Infinity Fabric and HBM3e controllers
This modular design increases compute density and enables faster data movement for AI training and inference workloads.
Memory and Bandwidth: 288GB HBM3e + 8TB/s #
Memory is a core highlight of the MI350:
- 288GB HBM3e – industry-leading GPU memory capacity
- 8 TB/s bandwidth – 33% increase over MI300
- 36GB per stack, 12-Hi design
- 256MB Infinity Cache to cut latency
This memory configuration makes MI350 ideal for large language model (LLM) training, long-context AI inference, and next-gen HPC simulations.
Compute Performance: 10 PFLOPS GPU #
The MI350 is optimized for multiple precision workloads:
- 2.5 PFLOPS FP16 / BF16 matrix
- 5 PFLOPS FP8
- 10 PFLOPS MXFP6 / MXFP4 formats
- 78.6 TFLOPS FP64 double precision
In live benchmarks, the MI355X variant delivered a 35× inference throughput increase on Llama 3.1 405B compared to MI300, proving its AI acceleration advantage.
Interconnect and Deployment #
With 4th-gen Infinity Fabric, MI350 delivers:
- 1075 GB/s aggregate per-card bandwidth
- Up to 8 GPU interconnect with ~20% faster communication
Deployment options:
- MI350X (air-cooled) – 1000W TDP, 10U chassis
- MI355X (liquid-cooled) – 1400W TDP, 5U dense deployment
In a standard rack, MI350 scales to:
- 80 PFLOPS FP8 compute
- 2.25TB pooled HBM3e memory
AMD Instinct MI350 vs NVIDIA GB200 vs MI300 #
To highlight the generational leap and competitive positioning, here’s a direct comparison:
| Accelerator | Process | Memory | Bandwidth | FP8 Perf | FP64 Perf | TDP | Key Advantage |
|---|---|---|---|---|---|---|---|
| AMD MI300 | TSMC 5nm | 192GB HBM3 | 6 TB/s | 1 PFLOPS | 39 TFLOPS | 750W | First CDNA 3 chiplet GPU |
| AMD MI350 | TSMC N3P+N6 | 288GB HBM3e | 8 TB/s | 5 PFLOPS | 78.6 TFLOPS | 1000–1400W | 10 PFLOPS MXFP, 35× AI inference |
| NVIDIA GB200 | TSMC 4N | 192GB HBM3e | ~6.5 TB/s | ~5 PFLOPS | ~39 TFLOPS | 1000–1200W | Strong CUDA ecosystem |
The MI350 offers 1.6× more memory and 2× FP64 performance compared to NVIDIA’s GB200, making it especially competitive in AI inference and scientific computing.
Availability and Roadmap #
The AMD Instinct MI350 will ship to hyperscale datacenters in Q3 2025.
AMD also confirmed that the Instinct MI400 series is in development, targeting a 2026 launch, reinforcing AMD’s strategy of annual AI accelerator refresh cycles.
Conclusion: AMD Challenges NVIDIA with Instinct MI350 #
The AMD Instinct MI350 GPU represents a massive leap in AI and HPC acceleration. With 185B transistors, 288GB HBM3e, 10 PFLOPS performance, and scalable Infinity Fabric, it positions AMD as a serious challenger to NVIDIA’s AI supremacy.
For workloads like large-scale AI model training, generative AI inference, and scientific simulations, the MI350 sets a new benchmark in compute density and memory scalability.
As AI adoption accelerates globally, the launch of the MI350 signals a new era in the AI datacenter performance race.