Why AMD’s Stock Is Lagging in the AI Race?

AMD’s stock price has struggled to keep up with NVIDIA’s, primarily due to NVIDIA’s dominance in the AI accelerator market and AMD’s slower-than-expected growth in capturing AI-related revenue. While AMD has made notable strides in hardware innovation and partnerships, several factors contribute to this disparity, alongside challenges in the broader AI competition. Here’s a detailed analysis:

https://360miq.com/tool?code=AMD,NVDA&tf=d&from=2024-02-11&to=

Table of Contents

Key Reasons for AMD’s Stock Underperformance:

NVIDIA’s AI Dominance

NVIDIA controls approximately 90% of the AI training market, thanks to its CUDA ecosystem and Tensor Core-optimized GPUs like the H100 and upcoming Blackwell architecture. In contrast, AMD generated only $5 billion in AI accelerator revenue in 2024, compared to NVIDIA’s $30 billion per quarter in data center revenue.

Investors perceive NVIDIA as the “default” choice for AI workloads, while AMD struggles to convince enterprises to transition from CUDA to its ROCm software stack, which still lags in compatibility and developer adoption.

Growth Rate Disparity

In 2024, NVIDIA’s revenue surged 113%, while AMD’s AI-related growth remained modest (e.g., 25% EPS growth vs. NVIDIA’s 128%). Analysts project NVIDIA’s 2025 revenue growth at 51%, whereas AMD’s AI segment is expected to grow to ~$7 billion—still significantly behind NVIDIA’s scale.

Software Ecosystem Challenges

NVIDIA’s CUDA and AI frameworks (e.g., TensorRT, Omniverse) are deeply entrenched in AI research and enterprise workflows. AMD’s ROCm, despite improvements, lacks seamless integration with popular tools like PyTorch, creating friction for developers.

AMD’s focus on open standards (e.g., FSR, HIP) has not yet offset NVIDIA’s proprietary advantages, though in the long term, these open standards could attract more industry-wide adoption. If major cloud providers and AI developers embrace AMD’s open ecosystem, it could gradually shift market dynamics and reduce dependency on NVIDIA’s closed platforms. in AI training and inference.

Valuation and Investor Sentiment

NVIDIA’s stock trades at a forward P/E of ~30x (2026 earnings), which is considered reasonable given its growth trajectory. AMD, despite a lower forward P/E (~24x), faces skepticism due to slower AI adoption and its heavier reliance on non-AI segments, which have experienced weaker growth and profitability compared to NVIDIA’s core AI-driven revenue streams. (e.g., gaming, embedded) that underperformed in 2024.

Supply Chain and Product Timing

NVIDIA’s Blackwell GPUs, though delayed, are expected to solidify its lead in 2025. AMD’s MI300X and MI350 AI accelerators, while competitive in memory bandwidth, could gain traction through strategic partnerships with cloud providers like Microsoft Azure and AWS. These collaborations may help AMD overcome adoption challenges by expanding its ecosystem and increasing enterprise trust in its AI solutions. (192GB HBM3), face challenges in scaling adoption amid NVIDIA’s entrenched partnerships with hyperscalers like Google and Meta.

Is AMD Losing the AI Competition?

Short-term Challenges

Training Market Gap: NVIDIA’s GPUs remain unmatched for training large language models (LLMs). AMD’s MI300X targets inference workloads, but training still drives the majority of AI spending.
Software Maturity: ROCm’s limitations in framework support and ease of use hinder AMD’s ability to attract AI developers.

Long-term Opportunities

Inference Focus: AMD’s MI300X excels in inference efficiency, a growing market as enterprises deploy AI models. Partnerships with Microsoft Azure and AWS could help AMD carve a niche.
Cost Advantage: AMD’s GPUs are priced 20-30% lower than NVIDIA’s equivalents, appealing to cost-sensitive buyers.
Open Ecosystem: Companies like Meta and Google, wary of NVIDIA’s vendor lock-in, are testing AMD’s hardware for custom AI workloads.

Outlook for AMD in 2025

AMD’s stock could rebound if:

MI350 Adoption Accelerates: The MI350 series, launching in 2025, must prove competitive against NVIDIA’s Blackwell in both performance and total cost of ownership.
Software Improvements: ROCm 6.0 and AI framework optimizations (e.g., PyTorch, JAX) need to narrow the gap with CUDA.
Cloud Partnerships Expand: Increased deployment in AWS, Azure, and Oracle Cloud AI instances would validate AMD’s credibility.

Conclusion

AMD is not “losing” the AI competition outright but faces an uphill battle against NVIDIA’s ecosystem dominance. While NVIDIA remains the clear leader, AMD’s focus on inference, cost efficiency, and open-source collaboration positions it as a viable #2 player. For investors, AMD’s stock offers higher upside potential if execution improves, but risks remain due to NVIDIA’s entrenched advantages.