As AI computing surges, AMD is stepping into the spotlight with renewed confidence. Long operating in the shadow of NVIDIA, AMD is now aiming to assert its presence in the AI accelerator market with the Instinct MI350 series—and it’s doing so with both technical upgrades and a significant price adjustment.
AMD recently raised the price of its MI350 accelerator from $15,000 to $25,000, marking a 70% increase. While this sounds steep, the move reflects strong demand and growing confidence in the product’s capabilities. Even at the new price, it remains more affordable than NVIDIA’s Blackwell B200, which starts around $30,000. AMD is clearly looking to strike a new balance between performance, cost-efficiency, and profitability.
Technical Advancements #
At the heart of the MI350 series is AMD’s CDNA 4 architecture, fabricated on TSMC’s 3nm process. The lineup includes the MI350X and MI355X, both outfitted with 288GB of HBM3E memory delivering up to 8TB/s of bandwidth—a major leap from the MI300X’s 5.2TB/s and ahead of the B200’s 192GB capacity.
This expanded memory capacity enables the MI350 to handle models exceeding 50 billion parameters without needing excessive external memory access, which significantly improves training and inference efficiency by reducing latency.
Performance-wise, the MI350 supports multiple floating-point formats (FP4, FP6, FP8, FP16), with the MI355X peaking at 20.1 PFLOPS in FP4 and 10.1 PFLOPS in FP8. By comparison, the Blackwell B200 achieves around 9 PFLOPS in FP4. AMD accomplishes this through a chiplet-based design, incorporating eight compute dies (XCDs) and two I/O dies, for a total of 185 billion transistors—a 21% increase over the MI300X. The 256 compute units offer improved scalability and better energy efficiency. The MI350X remains air-cooled with modest power requirements, while the MI355X supports liquid cooling at up to 1400W for higher-end deployments.
Architectural Improvements & Ecosystem Maturity #
The CDNA 4 architecture introduces a more efficient Infinity Fabric interconnect, delivering 5.5TB/s bandwidth with lower frequency and voltage, enhancing power efficiency. In real-world AI benchmarks—such as inference for the Llama 3.1 405B model—the MI355X delivers 35x the performance of the MI300X. In tests with models like DeepSeek R1 and Llama 3.3 70B, it matches or outperforms the B200 and GB200 by up to 3x.
This leap in performance isn’t just about raw specs—it stems from AMD’s matrix engine optimizations, advanced sparsity handling, and mature AI workload tuning.
On the software side, AMD is rapidly closing the gap with its ROCm 7 platform, which now supports major frameworks such as PyTorch and TensorFlow, and includes optimizations for distributed training. In addition, AMD’s involvement in open interconnect initiatives like the Ultra Ethernet Consortium and UALink Alliance sets it apart from NVIDIA’s closed NVLink ecosystem—an appealing proposition for hyperscalers like Meta, Microsoft, and OpenAI, all of whom have deployed the MI300X and are expected to expand with the MI350.
Industry Outlook and Strategic Positioning #
The AI chip market is on a trajectory to reach $500 billion by 2028, with data centers investing heavily in high-performance compute. Though NVIDIA still commands a 90% market share, production constraints—such as TSMC’s CoWoS packaging bottlenecks—are creating opportunities for challengers like AMD.
AMD is capitalizing on this window with an aggressive roadmap:
- MI325X in 2024
- MI350 in mid-2025
- MI400 in 2026, featuring HBM4 memory with 19.6TB/s bandwidth, aiming directly at NVIDIA’s Rubin architecture
The MI350’s pricing also reflects market dynamics. With 30% lower cost than the B200 and more onboard memory, it’s well-positioned for organizations seeking cost-effective AI infrastructure. The launch of the Helios rack-scale platform, combining MI350 accelerators with 5th-gen EPYC CPUs, delivers 2.6 Exaflops of FP4 compute, making it ideal for hyperscale deployments.
The Road Ahead #
As model sizes evolve into the trillion-parameter scale, demands on memory capacity and thermal efficiency will only intensify. The MI350’s generous memory and advanced cooling design prepare it well for this future. AMD’s open approach may help it gain ground in cloud AI, research, and enterprise, challenging NVIDIA’s dominance.
That said, AMD still faces challenges—NVIDIA’s CUDA ecosystem is deeply entrenched, and its integration pipeline is battle-tested. To secure a lasting foothold, AMD must continue to refine its software stack and build a compelling portfolio of customer success stories.
The MI350’s price hike signals more than just a business move—it marks AMD’s ambition to lead in the next wave of AI computing. Backed by technical innovation and strategic positioning, AMD is poised to reshape the accelerator market and fuel the industry’s next stage of growth.