AMD Ryzen AI Max+ 395 vs. Intel Core Ultra 7 258V: AI Performance Showdown
AMD has released performance data for its Ryzen AI Max+ 395, demonstrating a substantial performance advantage over Intel’s efficiency-focused Lunar Lake CPUs, specifically the Core Ultra 7 258V, in a variety of AI-related benchmarks. A blog post from AMD details the capabilities of the new Zen 5 + RDNA 3.5 based chip, claiming a performance lead of up to 12.2 times in specific AI workloads. This comparison focuses on the relative performance of these two processors in the rapidly evolving field of AI processing.
Benchmark Setup and Methodology
To showcase the capabilities of the Ryzen AI Max+ 395, AMD performed a series of tests, directly comparing it to Intel’s Core Ultra 7 258V, which features Arc 140V graphics. The benchmarks were centered around various large language models (LLMs) and LLM configurations, including popular models like DeepSeek R1 and Llama. These models represent common AI workloads and provide a relevant basis for comparison.
Memory Configuration Considerations:
A key aspect of the testing methodology was ensuring a fair comparison, particularly regarding memory configurations. Model sizes were limited to 16GB. This limitation was put in place to reflect the memory constraints of Lunar Lake-based laptops, which currently have a maximum memory capacity of 32GB. The specific test systems used were:
- Ryzen AI Max+ 395: An Asus ROG Flow Z13 laptop equipped with 64GB of memory.
- Core Ultra 7 258V: An Asus Zenbook S14 laptop equipped with 32GB of memory.
This difference in total system memory, while present, was mitigated by limiting the model size to ensure that neither system was unduly disadvantaged by memory capacity limitations.
DeepSeek R1 Benchmark Results: A Clear Advantage
The DeepSeek R1 benchmarks clearly showed the Ryzen chip’s superior performance. The results, measured in tokens per second (a standard metric for LLM performance), were as follows:
- Distill Qwen 1.5b: The Ryzen AI Max+ 395 was up to 2.1 times faster than the Intel Core Ultra 7 258V.
- Distill Qwen 7b: The AMD chip demonstrated a performance lead of up to 2.2 times.
- Distill Llama 8b: Performance was up to 2.1 times faster on the Ryzen processor.
- Distill Qwen 14b: The Ryzen AI Max+ 395 achieved speeds up to 2.2 times faster.
These results highlight a significant performance advantage for the AMD chip in processing various configurations of the DeepSeek R1 model.
Phi 4 and Llama 3.2 Benchmark Results: Continued Leadership
The Ryzen AI Max+ 395 maintained its performance lead over the Core Ultra 7 258V in tests utilizing Phi 4 and Llama 3.2 models:
- Phi 4 Mini Instruct 3.8b: The AMD chip was up to 2.1 times faster.
- Phi 4 14b: Performance was up to 2.2 times faster on the Ryzen processor.
- Llama 3.2 3b Instruct: The Ryzen AI Max+ 395 achieved speeds up to 2.1 times faster.
These results further solidify the Ryzen AI Max+ 395’s position as a high-performance processor for AI workloads, demonstrating consistent advantages across different LLMs.
Time to First Token (TTFT): A Critical Metric for Responsiveness
AMD also emphasized the “time to the first token” (TTFT) metric, a crucial indicator of responsiveness in AI applications. TTFT measures the delay between a user’s input and the initial response from the AI model. Lower TTFT values indicate a more responsive and interactive user experience. In these benchmarks, the Ryzen AI Max+ 395 demonstrated even more substantial performance leads:
- DeepSeek R1 Distill Qwen 14b: The AMD chip was up to 12.2 times faster.
- Even in scenarios where the Zen 5 chip’s performance advantage was less pronounced (Phi 4 Mini Instruct 3.8b and Llama 3.2 3b Instruct), the AMD chip still maintained a 4x speed advantage over the Core Ultra 7 258V.
This focus on TTFT highlights AMD’s commitment to providing a superior user experience in AI applications, where responsiveness is paramount.
AI Vision Model Benchmarks: Expanding the Performance Gap
The performance advantage of the Ryzen AI Max+ 395 extended to AI vision models, again using the “time to the first token” benchmarking approach. These models are used for tasks such as image recognition and object detection:
- IBM Granite Vision 3.2 2B: The Ryzen AI Max+ 395 was up to 7 times faster than the Core Ultra 7 258V.
- Google Gemma 3.4b: The AMD chip demonstrated a performance lead of up to 4.6 times.
- Google Gemma 3 12b: Performance was up to 6 times faster on the Ryzen processor.
These results demonstrate the versatility of the Ryzen AI Max+ 395, showcasing its capabilities not only in language processing but also in vision-based AI tasks.
Architectural Advantages: The Foundation of Superior Performance
The impressive performance figures achieved by AMD’s Ryzen AI Max+ 395 are largely attributed to several key architectural advantages:
- Powerful Integrated Graphics (RDNA 3.5): The integrated graphics chip within the Ryzen AI Max CPU features 40 RDNA 3.5 compute units (CUs). This provides a level of graphics performance that rivals discrete graphics solutions, significantly accelerating AI workloads that rely on GPU processing.
- Higher Core Count: The Ryzen AI Max+ 395 boasts eight more CPU cores than the Core Ultra 7 258V. This contributes to enhanced processing capabilities, particularly in multithreaded tasks and overall system responsiveness.
- Configurable TDP (Thermal Design Power): The Ryzen chip has a significantly higher configurable TDP, rated up to 120W. This allows for greater performance headroom, enabling the chip to sustain higher clock speeds and deliver superior performance, especially in demanding AI workloads.
These architectural advantages combine to provide a significant performance boost, enabling the Ryzen AI Max+ 395 to outperform the Core Ultra 7 258V in a wide range of AI benchmarks.
Power Consumption: A Trade-off for Performance
It’s important to acknowledge that the Ryzen AI Max+ 395 consumes significantly more power than the Core Ultra 7 258V, which has a maximum turbo power of 37W. This difference in power consumption is a direct consequence of the higher performance capabilities of the Ryzen chip. However, despite this difference, both chips are designed for the same market segment: thin-and-light laptop PCs. Therefore, the comparison remains relevant, highlighting the trade-off between performance and power efficiency.
Future Competition: NVIDIA RTX 50-Series
The mobile computing landscape is constantly evolving, and the next major challenge for AMD’s new mobile APUs will likely come from NVIDIA’s RTX 50-series mobile GPUs. While reports suggest potential supply chain issues and delays for the launch of these GPUs in upcoming RTX 50 series gaming laptops, they will undoubtedly represent AMD’s primary competition in terms of raw performance, regardless of form factor differences. NVIDIA has traditionally dominated the high-end mobile graphics market, and the RTX 50-series is expected to continue this trend.
Early Comparisons to Discrete GPUs:
Interestingly, AMD has already made claims about the Ryzen AI Max+ 395’s superior AI performance compared to NVIDIA’s RTX 4090 laptop GPU. This suggests a strong competitive stance even against discrete graphics solutions, indicating that AMD’s integrated solution may be able to compete with, and potentially outperform, dedicated graphics cards in certain AI-focused applications. This is a bold claim, and independent reviews will be crucial to verify its accuracy. It’s a pre-emptive statement, and one that is sure to have those awaiting independent reviews very excited.
Deeper Dive into the Benchmark Results: Understanding the Implications
The benchmark data provided by AMD offers a clear indication of the company’s focus on AI performance. The selection of models and configurations highlights the increasing importance of efficient and responsive AI processing in modern computing tasks.
Large Language Models (LLMs): A Key Focus:
The use of DeepSeek R1 and Llama, two prominent LLMs, demonstrates the ability of the Ryzen AI Max+ 395 to handle complex natural language processing tasks. These models are widely used in various applications, including chatbots, machine translation, and text generation. The “tokens per second” metric is a standard measure of performance in this area, indicating how quickly the processor can generate text or process language-based inputs. Higher tokens per second translate to faster and more efficient processing.
Model Distillation: Efficiency and Performance:
The inclusion of “Distill” versions of the models (e.g., Distill Qwen 1.5b) indicates a focus on model efficiency. Distillation is a technique used to create smaller, faster versions of larger models while retaining much of their accuracy. This is particularly relevant for mobile devices, where power consumption and memory constraints are critical considerations. By demonstrating strong performance with distilled models, AMD highlights the Ryzen AI Max+ 395’s ability to deliver both speed and efficiency.
Diverse Model Selection: Phi 4 and Llama 3.2:
The addition of Phi 4 and Llama 3.2 models provides a broader perspective on the chip’s performance across different AI architectures and model sizes. This demonstrates the versatility of the Ryzen AI Max+ 395 and its ability to handle a variety of AI workloads.
Time to First Token (TTFT): The Importance of Responsiveness:
The emphasis on “time to the first token” (TTFT) is particularly noteworthy. TTFT measures the latency between a user’s input and the initial response from the AI model. A lower TTFT translates to a more responsive and interactive user experience, which is crucial for applications like chatbots, real-time translation, and code completion. In these applications, even small delays can significantly impact the user’s perception of the system’s performance. AMD’s focus on TTFT underscores its commitment to providing a smooth and seamless user experience.
AI Vision Models: Beyond Language Processing:
The inclusion of AI vision models (IBM Granite Vision and Google Gemma) demonstrates the versatility of the Ryzen AI Max+ 395. These models are used for tasks such as image recognition, object detection, and video analysis. The strong performance in these benchmarks suggests the chip’s suitability for applications beyond just language processing, expanding its potential use cases in areas like computer vision and image processing.
The Significance of Architectural Advantages: A Detailed Explanation
AMD’s architectural decisions play a crucial role in the observed performance differences between the Ryzen AI Max+ 395 and the Core Ultra 7 258V.
Integrated Graphics (RDNA 3.5): A Game Changer:
The powerful integrated graphics unit is a key differentiator. Unlike traditional integrated graphics solutions, which often struggle with demanding workloads, the RDNA 3.5 architecture provides a significant boost in performance, enabling the Ryzen AI Max+ 395 to handle AI tasks more effectively. The 40 CUs represent a substantial computational capacity, allowing for parallel processing of AI algorithms. This is particularly important for AI workloads, which often involve large matrix operations that can be efficiently handled by GPUs.
Core Count: More Cores, More Power:
The higher core count (eight more cores than the Core Ultra 7 258V) provides a general advantage in multithreaded workloads. While AI processing often relies heavily on the GPU, the CPU still plays a role in managing tasks and handling certain aspects of the computation. A higher core count allows for better multitasking and overall system responsiveness, contributing to a smoother user experience.
Configurable TDP: Unleashing Performance Potential:
The higher TDP allows for greater flexibility in power management. While it does mean higher power consumption, it also enables the chip to operate at higher clock speeds and sustain performance for longer periods, particularly in demanding AI workloads. The ability to configure the TDP up to 120W provides a significant advantage over the more constrained 37W maximum turbo power of the Core Ultra 7 258V. This is a crucial factor in achieving the observed performance leads, allowing the Ryzen AI Max+ 395 to push its capabilities further.
The Evolving Mobile Computing Landscape: A Battle for Supremacy
The competition between AMD and Intel in the mobile space has intensified in recent years, with both companies pushing the boundaries of performance and efficiency. The introduction of Lunar Lake represented Intel’s focus on power efficiency, aiming to deliver long battery life in thin-and-light laptops. AMD’s Ryzen AI Max+ 395, on the other hand, clearly prioritizes performance, particularly in AI workloads, while still targeting the same thin-and-light form factor.
The upcoming battle with NVIDIA’s RTX 50-series mobile GPUs will be a significant test for AMD. While NVIDIA has traditionally dominated the high-end mobile graphics market, AMD’s advancements in integrated graphics and AI processing capabilities position it as a strong contender. The reported supply chain issues facing NVIDIA could potentially give AMD an advantage in terms of availability and market penetration.
The claims of superior AI performance against the RTX 4090 laptop GPU are bold, but if substantiated, they would represent a significant shift in the competitive landscape. It would indicate that AMD’s integrated solution can compete with, and potentially outperform, discrete graphics solutions in certain AI-focused applications. This would be a major achievement and could have significant implications for the future of mobile computing, potentially blurring the lines between integrated and discrete graphics performance.
The emphasis on AI performance is a clear indication of the direction the industry is heading. As AI becomes increasingly integrated into everyday applications, the demand for processors that can handle these workloads efficiently and effectively will continue to grow. AMD’s Ryzen AI Max+ 395 is a strong contender in this evolving market, showcasing impressive performance and setting the stage for a competitive future. The focus on both LLMs and vision models demonstrates a commitment to a broad range of AI applications, positioning AMD well for the future of AI-powered computing.