Why Cerebras’ Mind-Boggling LLM Raw Speed Is Still Falling Into Nvidia’s Massive Software Trap

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By Alex Sirois Published

Quick Read

  • CBRS secured a $20B+ OpenAI deal yet guided to negative operating margins, while NVDA's 75% gross margins reveal the platform moat's financial power.

  • Cerebras' wafer-scale chip delivers a 21x speed advantage over Nvidia hardware, but every major LLM framework natively optimizes for CUDA, requiring costly custom engineering.

  • Don't wait: the analyst who called NVIDIA in 2010 just revealed his top 10 AI stocks. See the full list FREE now.

Why Cerebras’ Mind-Boggling LLM Raw Speed Is Still Falling Into Nvidia’s Massive Software Trap

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NVIDIA (NASDAQ: NVDA | NVDA Price Prediction) and Cerebras Systems (NASDAQ: CBRS) just delivered earnings that frame the same question from opposite ends. Nvidia posted another blowout quarter built on its CUDA software stack. Cerebras, fresh off its May IPO, showed jaw-dropping inference speed yet guided full-year operating margins negative. The moat is developer gravity.

One Sells Platforms. The Other Sells Speed.

Nvidia’s Q1 FY27 hit $81.61 billion in revenue, up 85.2% YoY, with Data Center alone reaching $75.25 billion on 92% growth. Networking soared 199% as InfiniBand, NVLink and Spectrum-X locked customers deeper into the stack. Jensen Huang told investors NVIDIA is “the only platform that runs in every cloud, powers every frontier and open source model, and scales everywhere AI is produced”, and the numbers back the claim.

Cerebras’ first report as a public company landed differently. Q1 GAAP revenue reached $193.4 million, up 94% YoY, with cloud services growing 178%. A multi-year, $20 billion-plus OpenAI inference deal covering 750 megawatts anchors near-term growth. Yet management guided full-year operating margins to negative 28% to negative 32%. Speed sells. Scaling it economically is harder.

Software Gravity Beats Wafer-Scale Throughput

Independent benchmarks show Cerebras’ wafer-scale design delivering a 21x speed advantage over Nvidia hardware for latency-sensitive, low-batch inference. The catch is that every major LLM framework and enterprise developer stack is natively optimized for Nvidia architecture out of the box, while Cerebras requires specialized compilation and custom engineering support for anything off the well-trodden path.

Lens NVIDIA Cerebras
Core Bet CUDA full-stack platform Wafer-scale inference speed
Q1 Gross Margin 75.0% non-GAAP 44.6% GAAP
Anchor Customers Meta, OpenAI, Anthropic, Google OpenAI, AWS, G42
Biggest Vulnerability OpenAI’s Jalapeño custom chip Negative operating margins

Nvidia’s $119 billion in supply commitments and $80 billion added to its buyback authorization signal management is doubling down on the platform. Cerebras raised $5.6 billion at IPO and is funneling it into data center capacity for OpenAI’s decode workloads while AWS Trainium 3 handles prefill. That is a focused inference bet riding on one customer’s roadmap.

The Next Test Is Whether Developers Defect

Two catalysts matter into the back half of 2026. For Nvidia, the OpenAI Jalapeño chip, built with Broadcom, is the most credible threat to CUDA stickiness. NVDA shares are already down 8.79% over the past month, even with the stock up 27.01% YoY. For Cerebras, the bar is executing the OpenAI ramp without further margin slippage. Q2 core gross margin guidance of 36% to 38% telegraphs how steep the infrastructure build will be.

Why The Setup Still Favors Nvidia

For AI infrastructure exposure with a self-funding moat, Nvidia remains the cleaner expression of the thesis. The 75% gross margin, $48.55 billion in quarterly free cash flow, and the developer install base are tough to dislodge in a single product cycle. Cerebras has the faster chip and a marquee anchor customer. A forward P/E of 23 on NVDA already prices in some software erosion. If CUDA defections spread beyond OpenAI, my view changes.

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About the Author Alex Sirois →

Alex Sirois is a financial writer with experience spanning both retail and institutional investing. He has written for InvestorPlace and held roles at BNY Mellon and Bernstein, giving him a perspective that bridges Main Street portfolios and Wall Street analysis.

Alex holds an MBA from George Washington University and has built his career across multiple industries, including e-commerce, education, and translation — a breadth of experience that informs how he breaks down complex financial topics for everyday investors. His writing is conversational, actionable, and grounded in long-term, buy-and-hold investing principles.

At 247 Wall St., Alex focuses on delivering analysis that is both accessible and useful, with a clear emphasis on helping readers make more informed decisions with their money.

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