AMD Raises MI350 Price by 70% to $25,000, Targeting AI Accelerator Leadership
With the rapid acceleration of AI computing, AMD is stepping out from NVIDIA’s shadow and taking a bold stance in the data center GPU race. The company’s Instinct MI350 series marks a major leap in AI acceleration, backed by a significant price increase that signals AMD’s growing confidence.
AMD has raised the price of the MI350 accelerator from $15,000 to $25,000 — a 70% jump. While steep, the move reflects both surging AI demand and AMD’s belief in the MI350’s competitiveness. Importantly, its pricing still undercuts NVIDIA’s Blackwell B200, which starts around $30,000, positioning AMD as the value-performance challenger.
MI350 Technical Upgrades and Specs #
Built on the CDNA 4 architecture and manufactured on TSMC’s 3nm process, the MI350 family includes the MI350X and MI355X. Both are equipped with:
- 288GB HBM3E high-bandwidth memory
- Memory bandwidth up to 8TB/s (vs. 5.2TB/s on MI300X, higher than Blackwell B200’s 192GB capacity)
- Support for AI models with 50B+ parameters without external storage reliance
Compute Performance #
- Supports FP4, FP6, FP8, and FP16 formats
- MI355X peaks at 20.1 PFLOPS (FP4) and 10.1 PFLOPS (FP8)
- NVIDIA B200 reaches ~9 PFLOPS (FP4)
This performance advantage is powered by AMD’s chiplet modular design:
- 8 Compute Dies (XCDs) + 2 I/O Dies
- 185 billion transistors (up 21% from MI300X)
- 256 Compute Units for improved scalability and power efficiency
Cooling options:
- MI350X supports air cooling
- MI355X requires liquid cooling with 1,400W TDP, designed for dense data centers
Architectural Innovations and Ecosystem Growth #
The CDNA 4 architecture introduces key advances:
- Infinity Fabric interconnect with 5.5TB/s bandwidth
- Lower bus frequency and voltage for better efficiency
In real-world AI benchmarks:
- MI355X is 35× faster than MI300X on Llama 3.1 405B inference
- Matches or outperforms NVIDIA B200/GB200 by up to 3× on DeepSeek R1 and Llama 3.3 70B
This leap comes from matrix engine and sparsity optimizations, not just theoretical FLOPS.
Software Ecosystem #
- Fully supported by ROCm 7, optimized for PyTorch and TensorFlow
- Enhanced for distributed training workloads
- AMD champions open interconnect standards via the Ultra Ethernet and UALink alliances — in stark contrast to NVIDIA’s closed NVLink
- Major adopters like Meta, Microsoft, and OpenAI already use MI300X and are expected to expand into MI350 deployments
Market Dynamics and Strategy #
The AI chip market is projected to hit $500 billion by 2028, with hyperscalers ramping up compute investments. NVIDIA still commands ~90% market share, but CoWoS packaging constraints at TSMC limit supply, opening a window for AMD.
AMD’s roadmap reflects an aggressive push:
- 2024: MI325X launch
- Mid-2025: MI350X / MI355X
- 2026: MI400 series with HBM4 memory and 19.6TB/s bandwidth, designed to challenge NVIDIA Rubin
This pricing move highlights AMD’s confidence:
- MI350 offers 30% lower cost than B200 while providing more memory
- AMD also unveiled its Helios rack-scale architecture, pairing MI350 accelerators with 5th Gen EPYC CPUs to deliver 2.6 Exaflops of FP4 compute for massive clusters
Looking Ahead #
As AI models scale from hundreds of billions to trillion-parameter workloads, memory capacity, efficiency, and thermal management will define the next wave of accelerators.
The MI350’s massive HBM3E pool, liquid cooling options, and open ecosystem give AMD a credible chance to erode NVIDIA’s dominance in cloud, enterprise, and research markets.
Challenges Remain #
- NVIDIA’s entrenched CUDA ecosystem and developer loyalty
- Deployment experience and tooling maturity still favor NVIDIA
- AMD must keep investing in ROCm and expand real-world case studies
Still, the MI350 price hike signals a turning point: AMD isn’t just competing on price anymore — it’s pushing to redefine leadership in AI acceleration with performance, scalability, and ecosystem openness.