Intel’s Sierra Forest CPU Falls Short in Benchmark Against AMD’s Zen 4c CPUs

Intel’s upcoming Sierra Forest CPU has been garnering attention in the tech industry. One of the most interesting aspects of this CPU is its focus on efficiency cores, or E-cores, which are aimed at maximizing core density. However, the first benchmark results for Sierra Forest have recently surfaced online, and it seems that Intel may have a tough battle ahead when it comes to competing against AMD’s chip.

Benchmark results

The appearance of the first benchmark for the Sierra Forest CPU has caused a stir in the tech community, especially as it provides an opportunity to compare its performance to that of AMD’s chips. Unfortunately for Intel, the benchmark results do not paint a positive picture. In fact, it seems that Sierra Forest falls short in this particular benchmark when compared to AMD’s Zen 4c CPUs.

Geekbench scores

Even though the official launch of Sierra Forest is still a few years away, Geekbench scores for an “unknown” Intel processor with 288 cores, speculated to be the Sierra Forest CPU, have emerged. These scores give us some insight into the potential performance of Intel’s upcoming CPU.

Performance Comparison

Based on the benchmark results, it is evident that Intel’s E-core CPU, represented by the “unknown” processor with 288 cores, offers reasonable performance for an efficiency-focused chip. However, when pitted against AMD’s Zen 4c CPUs, Sierra Forest falls noticeably behind.

Multi-core scores

Taking a closer look at the multi-core scores, we find that the 144-core Sierra Forest CPU is roughly on par with Intel’s current flagship Xeon CPUs, which boast 56 cores and 112 threads in a dual-socket configuration. While this may seem like respectable performance, it pales in comparison to AMD’s flagship 128-core, 256-thread CPU, which outperforms Sierra Forest by a significant margin.

E-core limitations

The performance gap between Sierra Forest and AMD’s Zen 4c CPUs can be attributed to the limitations of the E-cores employed by Intel. Unlike AMD’s Zen 4c CPUs, Sierra Forest’s E-cores lack hyper-threading, which hinders their ability to handle multi-core workloads effectively.

Advantages of AMD’s Zen 4 CPU

One of the key advantages of AMD’s Zen 4c CPU lies in its ability to run two threads per core. This gives AMD’s 128-core CPU a substantial advantage, with 256 threads available for multi-core workloads. In contrast, Intel’s Sierra Forest struggles to match this level of thread performance due to its E-cores’ lack of hyper-threading.

Single-core performance

Sierra Forest’s single-core performance is also noteworthy. When compared to Intel’s Xeon chips, which utilize performance-focused P-cores, Sierra Forest’s single-core score is approximately half of what the Xeon chips achieve. This discrepancy showcases the trade-off between efficiency and raw power, and reinforces the specific design focus of Sierra Forest.

In fairness to Intel, it is important to consider that Sierra Forest is primarily designed with maximum efficiency in mind. Consequently, Geekbench scores may not provide a comprehensive assessment of its capabilities, especially in a data center environment where workloads may vary significantly. It is essential to wait for further testing and real-world scenarios before drawing final conclusions about Sierra Forest’s performance.

Intel’s Sierra Forest CPU, featuring efficiency cores (E-cores) for maximum core density, faces a formidable challenge in terms of performance when compared to AMD’s Zen 4c CPUs. The benchmark results, represented by the Geekbench scores, indicate that Sierra Forest falls short in both multi-core and single-core performance. While Sierra Forest offers decent performance for an efficiency-focused chip, it struggles to match the capabilities of AMD’s Zen 4c CPUs’ multi-threading advantage. Nonetheless, it is important to consider that Geekbench scores do not provide a complete picture, and real-world performance may vary. As the launch of Sierra Forest approaches in early 2024, more comprehensive testing will shed light on its true potential in data center applications.

Explore more

A Beginner’s Guide to Data Engineering and DataOps for 2026

While the public often celebrates the triumphs of artificial intelligence and predictive modeling, these high-level insights depend entirely on a hidden, gargantuan plumbing system that keeps data flowing, clean, and accessible. In the current landscape, the realization has settled across the corporate world that a data scientist without a data engineer is like a master chef in a kitchen with

Ethereum Adopts ERC-7730 to Replace Risky Blind Signing

For years, the experience of interacting with decentralized applications on the Ethereum blockchain has been fraught with a precarious and dangerous uncertainty known as blind signing. Every time a user attempted to swap tokens or provide liquidity, their hardware or software wallet would present them with a wall of incomprehensible hexadecimal code, essentially asking them to authorize a financial transaction

Germany Funds KDE to Boost Linux as Windows Alternative

The decision by the German government to allocate a 1.3 million euro grant to the KDE community marks a definitive shift in how European nations view the long-standing dominance of proprietary operating systems like Windows and macOS. This financial injection, facilitated by the Sovereign Tech Fund, serves as a high-stakes investment in the concept of digital sovereignty, aiming to provide

Why Is This $20 Windows 11 Pro and Training Bundle a Steal?

Navigating the complexities of modern computing requires more than just high-end hardware; it demands an operating system that integrates seamlessly with artificial intelligence while providing robust security for sensitive personal and professional data. As of 2026, many users still find themselves tethered to aging software environments that struggle to keep pace with the rapid advancements in cloud computing and data

Notion Launches Developer Platform for AI Agent Management

The modern enterprise currently grapples with an overwhelming explosion of disconnected software tools that fragment critical information and stall meaningful productivity across entire departments. While the shift toward artificial intelligence promised to streamline these disparate workflows, the reality has often resulted in a chaotic landscape where specialized agents lack the necessary context to perform high-stakes tasks autonomously. Organizations frequently find