<\/span><\/h2>\nThe P16v Gen 3 maintains the long-lasting, skilled aesthetic widespread to the ThinkPad household however opts for a barely extra sturdy \u201cThunder Black\u201d chassis. In contrast to the carbon fiber and magnesium discovered within the P1, the P16v makes use of aluminum for the highest cowl and a sturdy polycarbonate-ABS mix for the underside. This ends in a barely thicker profile (roughly 1 inch) however ensures the system meets the rigorous MIL-STD-810H sturdiness requirements. At a beginning weight of 4.63 lbs (2.1 kg), it stays moveable for a 16-inch machine, although it’s noticeably heavier than an ultrabook.<\/p>\n
The keyboard stays one of many strongest facets of the ThinkPad design. The 6-row, spill-resistant keyboard offers deep journey and tactile suggestions, now that includes the devoted Copilot key for speedy AI help. The twin-input UltraNav system consists of the traditional purple TrackPoint and a spacious, smooth-surface touchpad. Audio is dealt with by front-facing stereo audio system with Dolby Atmos, offering clear mids for video conferencing, although it lacks the low-end punch required for media manufacturing.<\/p>\n
Connectivity is a standout function, notably for these in networking or industrial environments. In contrast to slimmer workstations that require dongles, the P16v features a devoted Gigabit Ethernet (RJ-45) port. That is complemented by two Thunderbolt 4 ports (supporting Energy Supply 3.1), two USB-A 3.2 Gen 1 ports, and an SD Categorical 7.0 card reader for fast knowledge offloading. This port array ensures that whether or not you might be in a server room or a area workplace, the P16v stays self-sufficient.<\/p>\n
<\/span>Digicam & Safety<\/span><\/h3>\nA 5.0MP IR digital camera allows distant collaboration and Home windows Whats up and features a privateness shutter and Ultrasonic Human Presence Detection. The digital camera makes use of fastened focus and temporal noise discount (TNR) to make sure high-fidelity video even in suboptimal lighting situations. Past the visible {hardware}, the P16v Gen 3 implements a classy, multi-layered safety structure. On the basis is a discrete TPM 2.0 chip, licensed to TCG and FIPS 140-2 requirements, which works in tandem with the touch-style fingerprint reader built-in into the ability button. This reader makes use of match-on-chip know-how to forestall biometric spoofing.<\/p>\n
Firmware-level safety is equally sturdy, that includes a self-healing BIOS and a complete suite of entry controls, together with NVMe, Energy-on, and Supervisor passwords. For bodily safety within the area, the chassis features a Kensington Nano Safety Slot and a bottom-cover tamper-detection system that alerts IT directors when the {hardware} has been accessed. For company fleets, choose fashions additionally assist Intel vPro Enterprise, offering the distant manageability and stability options required for contemporary enterprise environments.<\/p>\n
<\/span>Upgradability & Internals<\/span><\/h3>\nUpkeep is a key differentiator for the P16v, providing a degree of modularity that favors long-term skilled use. Internally, the system options two DDR5 slots (supporting each SODIMM and CSODIMM) that assist a most reminiscence capability of 96GB. The system can use high-speed DDR5-6400 CSODIMM modules, making certain that data-intensive AI and simulation duties are by no means memory-bandwidth restricted.<\/p>\n
Storage potential is equally spectacular, with two M.2 2280 PCIe 4.0 x4 slots that assist as much as 4TB of whole storage. These slots additionally assist RAID 0\/1 configurations, permitting customers to decide on between most efficiency and important knowledge redundancy. Even the construct supplies mirror a steadiness of sturdiness and sustainability; the highest cowl is crafted from 75% recycled aluminum, whereas the PC-ABS backside cowl and inside C-cover make the most of 30% Submit-Client Content material (PCC) plastic. Powering these internals is a considerable 90Wh Li-ion battery that helps Speedy Cost, able to reaching 80% capability in only one hour when used with the supplied 140W USB-C GaN adapter.<\/p>\n
<\/span>Lenovo ThinkPad P16v Gen 3 Efficiency<\/span><\/h2>\nTo guage the ThinkPad P16v Gen 3, we ran a variety of workstation- and AI-focused benchmarks, together with Procyon AI, SPECworkstation 4, SPECviewperf, Blender, LuxMark, and several other system-level checks measuring CPU, GPU, and storage efficiency throughout skilled workloads.<\/p>\n
Our evaluation configuration was geared up with an Intel Core Extremely 9 285H processor that includes 16 cores and a lift pace of as much as 5.38 GHz, paired with an NVIDIA RTX PRO 2000 Blackwell Laptop computer GPU with 8GB of GDDR7 reminiscence. The system was additionally configured with 32GB of system reminiscence and a 1TB Samsung NVMe SSD whereas operating Home windows 11.<\/p>\n
In these benchmarks, we in contrast the ThinkPad P16v Gen 3 to:<\/p>\n
<\/span>Procyon AI Pc Imaginative and prescient<\/span><\/h3>\nThe\u00a0Procyon AI Pc Imaginative and prescient Benchmark<\/a> measures AI inference efficiency throughout CPUs, GPUs, and devoted accelerators utilizing a variety of state-of-the-art neural networks. It evaluates duties akin to picture classification, object detection, segmentation, and super-resolution utilizing fashions together with MobileNet V3, Inception V4, YOLO V3, DeepLab V3, Actual ESRGAN, and ResNet 50. Assessments are run on a number of inference engines, together with NVIDIA TensorRT, Intel OpenVINO, Qualcomm SNPE, Microsoft Home windows ML, and Apple Core ML, offering a broad view of {hardware} and software program effectivity. Outcomes are reported for each float- and integer-optimized fashions, offering a constant, sensible measure of machine imaginative and prescient efficiency for skilled workloads.<\/p>\nThe ThinkPad P16v Gen 3 delivers the strongest AI inference efficiency among the many three methods examined, largely because of its extra highly effective RTX PRO 2000 GPU and higher-end Core Extremely 9 285H processor. Within the Procyon AI Pc Imaginative and prescient benchmark, the system achieved a CPU total rating of 138, matching the P16s Gen 4 whereas outperforming the smaller P14s Gen 6. Particular person mannequin latency outcomes stay persistently quick, with 0.98 ms for MobileNet V3, 9.57 ms for ResNet 50, and 28.26 ms for Inception V4, whereas heavier workloads, akin to Actual-ESRGAN, full noticeably sooner than on the RTX PRO 500 methods.<\/p>\n
The system\u2019s biggest benefit is obvious in GPU inference workloads. Powered by the RTX PRO 2000, the P16v reached a GPU total rating of 490, considerably forward of the P14s and P16s, which each rating round 355 with the RTX PRO 500. This hole widens additional in TensorRT testing, the place the P16v recorded an total rating of 681 and delivered considerably sooner ends in fashions akin to YOLO V3 and DeepLab V3. Because of this, the P16v stands out as probably the most succesful choice for professionals operating heavier AI imaginative and prescient workloads or large-scale inference pipelines on a cellular workstation.<\/p>\n
\n\n\nCPU Outcomes (common time in ms)<\/b><\/span><\/th>\n Lenovo ThinkPad P14s Gen 6 (Intel Extremely 7 265H)(NVIDIA RTX PRO 500)<\/b><\/span><\/th>\n Lenovo ThinkPad P16v Gen 3\u00a0 (Intel Core Extremely 9 285H)(NVIDIA RTX PRO 2000)<\/b><\/span><\/th>\n Lenovo ThinkPad P16s Gen 4 (Intel Core Extremely 7 265H)(NVIDIA RTX PRO 500)<\/b><\/span><\/th>\n<\/tr>\n\nAI Pc Imaginative and prescient Total Rating<\/td>\n 122<\/td>\n 138<\/td>\n 138<\/td>\n<\/tr>\n \nMobileNet V3<\/td>\n 1.11 ms<\/td>\n 0.98 ms<\/td>\n 1.08 ms<\/td>\n<\/tr>\n \nResNet 50<\/td>\n 10.89 ms<\/td>\n 9.57 ms<\/td>\n 9.80 ms<\/td>\n<\/tr>\n \nInception V4<\/td>\n 31.91 ms<\/td>\n 28.26 ms<\/td>\n 28.44 ms<\/td>\n<\/tr>\n \nDeepLab V3<\/td>\n 39.42 ms<\/td>\n 34.59 ms<\/td>\n 35.51 ms<\/td>\n<\/tr>\n \nYOLO V3<\/td>\n 74.45 ms<\/td>\n 68.85 ms<\/td>\n 69.87 ms<\/td>\n<\/tr>\n \nREAL-ESRGAN<\/td>\n 4,161.76 ms<\/td>\n 3,442.98 ms<\/td>\n 3,016.11 ms<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n\nGPU Outcomes (common time in ms)<\/b><\/span><\/th>\n Lenovo ThinkPad P14s Gen 6 (Intel Extremely 7 265H)(NVIDIA RTX PRO 500)<\/b><\/span><\/th>\n Lenovo ThinkPad P16v Gen 3\u00a0 (Intel Core Extremely 9 285H)(NVIDIA RTX PRO 2000)<\/b><\/span><\/th>\n Lenovo ThinkPad P16s Gen 4 (Intel Core Extremely 7 265H)(NVIDIA RTX PRO 500)<\/b><\/span><\/th>\n<\/tr>\n\nAI Pc Imaginative and prescient Total Rating<\/td>\n 355<\/td>\n 490<\/td>\n 354<\/td>\n<\/tr>\n \nMobileNet V3<\/td>\n 1.05 ms<\/td>\n 0.79 ms<\/td>\n 1.03 ms<\/td>\n<\/tr>\n \nResNet 50<\/td>\n 3.77 ms<\/td>\n 2.80 ms<\/td>\n 3.73 ms<\/td>\n<\/tr>\n \nInception V4<\/td>\n 10.13 ms<\/td>\n 8.05 ms<\/td>\n 10.37 ms<\/td>\n<\/tr>\n \nDeepLab V3<\/td>\n 25.40 ms<\/td>\n 19.54 ms<\/td>\n 24.74 ms<\/td>\n<\/tr>\n \nYOLO V3<\/td>\n 19.16 ms<\/td>\n 12.70 ms<\/td>\n 20.15 ms<\/td>\n<\/tr>\n \nREAL-ESRGAN<\/td>\n 399.21 ms<\/td>\n 256.57 ms<\/td>\n 395.42 ms<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n\nTensorRT<\/th>\n \u00a0Lenovo ThinkPad P14s Gen 6 (Intel Extremely 7 265H)(NVIDIA RTX PRO 500)<\/b><\/span><\/th>\n Lenovo ThinkPad P16v Gen 3\u00a0 (Intel Core Extremely 9 285H)(NVIDIA RTX PRO 2000)<\/b><\/span><\/th>\n Lenovo ThinkPad P16s Gen 4 (Intel Core Extremely 7 265H)(NVIDIA RTX PRO 500)<\/b><\/span><\/th>\n<\/tr>\n\nTotal Rating<\/td>\n 438<\/td>\n 681<\/td>\n 484<\/td>\n<\/tr>\n \nMobileNet V3<\/td>\n 0.76 ms<\/td>\n 0.53 ms<\/td>\n 0.64 ms<\/td>\n<\/tr>\n \nResNet 50<\/td>\n 3.33 ms<\/td>\n 2.20 ms<\/td>\n 3.05 ms<\/td>\n<\/tr>\n \nInception V4<\/td>\n 9.53 ms<\/td>\n 7.40 ms<\/td>\n 8.67 ms<\/td>\n<\/tr>\n \nDeepLab V3<\/td>\n 13.34 ms<\/td>\n 7.97 ms<\/td>\n 11.66 ms<\/td>\n<\/tr>\n \nYOLO V3<\/td>\n 15.28 ms<\/td>\n 7.94 ms<\/td>\n 13.76 ms<\/td>\n<\/tr>\n \nREAL-ESRGAN<\/td>\n 446.47 ms<\/td>\n 289.54 ms<\/td>\n 444.42 ms<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/span>UL Procyon: AI Textual content Technology<\/strong><\/span><\/h3>\nThe\u00a0Procyon AI Textual content Technology Benchmark<\/a> streamlines LLM efficiency testing by offering a concise, constant analysis technique. It allows repeated testing throughout a number of LLM fashions whereas minimizing the complexity of huge fashions and the variety of variables. Developed with AI {hardware} leaders, it optimizes using native AI accelerators to ship extra dependable, environment friendly efficiency assessments. The outcomes measured beneath had been examined utilizing TensorRT.<\/p>\nAI text-generation workloads present a transparent separation among the many three methods. The ThinkPad P16v Gen 3 leads decisively due to its RTX PRO 2000 GPU and higher-tier Core Extremely 9 processor. Within the Phi mannequin check, the P16v reached an total rating of 1,952, considerably forward of the P14s Gen 6 (1,307) and P16s Gen 4 (1,339). It additionally delivered the quickest response instances, producing tokens at 67.8 tokens per second with a time-to-first-token of 0.456 seconds, noticeably sooner than the roughly 0.67-second response instances on the RTX PRO 500 methods.<\/p>\n
The identical development continues within the Mistral mannequin benchmark, the place the P16v scored 1,745, in contrast with 1,137 on the P14s and 1,113 on the P16s. Token technology speeds once more spotlight the GPU benefit, with the P16v producing over 50 tokens per second, whereas the opposite two methods stay within the mid-30 vary. The P16v was additionally the one system able to operating the bigger Llama3 check configuration, reinforcing its place as probably the most succesful platform for native LLM experimentation and AI-assisted growth workflows.<\/p>\n
\n\n\nUL Procyon: AI Textual content Technology<\/b><\/span><\/th>\n \u00a0Lenovo ThinkPad P14s Gen 6 (Intel Extremely 7 265H)(NVIDIA RTX PRO 500)<\/b><\/span><\/th>\n Lenovo ThinkPad P16v Gen 3\u00a0 (Intel Core Extremely 9 285H)(NVIDIA RTX PRO 2000)<\/b><\/span><\/th>\n Lenovo ThinkPad P16s Gen 4 (Intel Core Extremely 7 265H)(NVIDIA RTX PRO 500)<\/b><\/span><\/th>\n<\/tr>\n\nPhi<\/b><\/span><\/td>\n<\/tr>\n\nPhi Total Rating<\/td>\n 1,307<\/td>\n 1,952<\/td>\n 1,339<\/td>\n<\/tr>\n \nPhi Output Time To First Token<\/td>\n 0.685 s<\/td>\n 0.456 s<\/td>\n 0.672 s<\/td>\n<\/tr>\n \nPhi Output Tokens Per Second<\/td>\n 45.671 tokens\/s<\/td>\n 67.803 tokens\/s<\/td>\n 47.070 tokens\/s<\/td>\n<\/tr>\n \nPhi Total Period<\/td>\n 64.004 s<\/td>\n 43.235 s<\/td>\n 62.210 s<\/td>\n<\/tr>\n \nMistral<\/b><\/span><\/td>\n<\/tr>\n\nMistral Total Rating<\/td>\n 1,137<\/td>\n 1,745<\/td>\n 1,113<\/td>\n<\/tr>\n \nMistral Output Time To First Token<\/td>\n 1.196 s<\/td>\n 0.751 s<\/td>\n 1.292 s<\/td>\n<\/tr>\n \nMistral Output Tokens Per Second<\/td>\n 33.960 tokens\/s<\/td>\n 50.213 tokens\/s<\/td>\n 34.884 tokens\/s<\/td>\n<\/tr>\n \nMistral Total Period<\/td>\n 87.809 s<\/td>\n 59.136 s<\/td>\n 86.295 s<\/td>\n<\/tr>\n \nLlama3<\/b><\/span><\/td>\n<\/tr>\n\nLlama3 Total Rating<\/td>\n \u2013<\/td>\n 1,552<\/td>\n \u2013<\/td>\n<\/tr>\n \nLlama3 Output Time To First Token<\/td>\n \u2013<\/td>\n 0.732 s<\/td>\n \u2013<\/td>\n<\/tr>\n \nLlama3 Output Tokens Per Second<\/td>\n \u2013<\/td>\n 43.409 tokens\/s<\/td>\n \u2013<\/td>\n<\/tr>\n \nLlama3 Total Period<\/td>\n \u2013<\/td>\n 67.892 s<\/td>\n \u2013<\/td>\n<\/tr>\n \nLlama2<\/b><\/span><\/td>\n<\/tr>\n\nLlama2 Total Rating<\/td>\n \u2013<\/td>\n \u2013<\/td>\n \u2013<\/td>\n<\/tr>\n \nLlama2 Output Time To First Token<\/td>\n \u2013<\/td>\n \u2013<\/td>\n \u2013<\/td>\n<\/tr>\n \nLlama2 Output Tokens Per Second<\/td>\n \u2013<\/td>\n \u2013<\/td>\n \u2013<\/td>\n<\/tr>\n \nLlama2 Total Period<\/td>\n \u2013<\/td>\n \u2013<\/td>\n \u2013<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/span>UL Procyon:\u00a0<\/strong>AI Picture Technology<\/strong><\/span><\/h3>\nThe\u00a0Procyon AI Picture Technology Benchmark<\/a> offers a constant and correct technique for measuring AI inference efficiency throughout a variety of {hardware}, from low-power NPUs to high-end GPUs. It consists of three checks: Secure Diffusion XL (FP16) for high-end GPUs, Secure Diffusion 1.5 (FP16) for reasonably highly effective GPUs, and Secure Diffusion 1.5 (INT8) for low-power gadgets. The benchmark makes use of the optimum inference engine for every system, making certain honest and comparable outcomes.<\/p>\nIn AI picture technology workloads, GPU efficiency turns into much more essential. The P16v Gen 3 once more leads by a considerable margin, producing a Secure Diffusion 1.5 (FP16) rating of 1,084, in comparison with 697 on the P14s and 708 on the P16s. In sensible phrases, the P16v generated pictures at 5.77 seconds per picture, whereas the RTX PRO 500 methods required roughly 9 seconds per picture.<\/p>\n
The INT8 optimized check reveals an analogous sample. The P16v achieved a rating of 13,402, considerably outperforming the P14s (8,440) and P16s (8,840). Moreover, the P16v was the one machine able to operating the heavier Secure Diffusion XL workload on this configuration, finishing renders at roughly 44 seconds per picture. These outcomes make the P16v the strongest choice for AI picture technology, whereas the RTX PRO 500 methods stay higher suited to lighter generative workloads.<\/p>\n
\n\n\nUL Procyon: AI Picture Technology<\/b><\/span><\/th>\n \u00a0Lenovo ThinkPad P14s Gen 6 (Intel Extremely 7 265H)(NVIDIA RTX PRO 500)<\/b><\/span><\/th>\n Lenovo ThinkPad P16v Gen 3\u00a0 (Intel Core Extremely 9 285H)(NVIDIA RTX PRO 2000)<\/b><\/span><\/th>\n Lenovo ThinkPad P16s Gen 4 (Intel Core Extremely 7 265H)(NVIDIA RTX PRO 500)<\/b><\/span><\/th>\n<\/tr>\n\nSecure Diffusion 1.5 (FP16)<\/b><\/span><\/td>\n<\/tr>\n\nSecure Diffusion 1.5 (FP16) \u2013 Total Rating<\/td>\n 697<\/td>\n 1,084<\/td>\n 708<\/td>\n<\/tr>\n \nSecure Diffusion 1.5 (FP16) \u2013 Total Time<\/td>\n 143.447 s<\/td>\n 92.235 s<\/td>\n 141.136 s<\/td>\n<\/tr>\n \nSecure Diffusion 1.5 (FP16) \u2013 Picture Technology Velocity<\/td>\n 8.965 s\/picture<\/td>\n 5.765 s\/picture<\/td>\n 8.821 s\/picture<\/td>\n<\/tr>\n \nSecure Diffusion 1.5 (INT8)<\/b><\/span><\/td>\n<\/tr>\n\nSecure Diffusion 1.5 (INT8) \u2013 Total Rating<\/td>\n 8,440<\/td>\n 13,402<\/td>\n 8,840<\/td>\n<\/tr>\n \nSecure Diffusion 1.5 (INT8) \u2013 Total Time<\/td>\n 29.619 s<\/td>\n 18.653 s<\/td>\n 28.278 s<\/td>\n<\/tr>\n \nSecure Diffusion 1.5 (INT8) \u2013 Picture Technology Velocity<\/td>\n 3.702 s\/picture<\/td>\n 2.332 s\/picture<\/td>\n 3.535 s\/picture<\/td>\n<\/tr>\n \nSecure Diffusion XL (FP16)<\/b><\/span><\/td>\n<\/tr>\n\nSecure Diffusion XL (FP16) \u2013 Total Rating<\/td>\n \u2013<\/td>\n 847<\/td>\n \u2013<\/td>\n<\/tr>\n \nSecure Diffusion XL (FP16) \u2013 Total Time<\/td>\n \u2013<\/td>\n 707.623 s<\/td>\n \u2013<\/td>\n<\/tr>\n \nSecure Diffusion XL (FP16) \u2013 Picture Technology Velocity<\/td>\n \u2013<\/td>\n 44.226 s\/picture<\/td>\n \u2013<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/span>SPECworkstation 4<\/strong><\/span><\/h3>\nThe SPECworkstation 4.0 benchmark is a complete instrument for evaluating all key facets of workstation efficiency.\u00a0<\/span>It provides a real-world measure of CPU, graphics, accelerator, and disk efficiency, making certain professionals have the info to make knowledgeable choices about their {hardware} investments.\u00a0<\/span>The benchmark features a devoted set of checks specializing in AI and ML workloads, together with knowledge science duties and ONNX runtime-based inference checks, reflecting the rising significance of AI\/ML in workstation environments.\u00a0<\/span>It encompasses seven {industry} verticals and 4 {hardware} subsystems, offering an in depth, related measure of as we speak\u2019s workstation efficiency.<\/span><\/p>\nSPECworkstation outcomes display how every system performs throughout industry-specific workloads. The P16v performs strongly in compute-heavy areas akin to AI & Machine Studying (1.63) and Productiveness & Improvement (1.09), barely outperforming each the P14s and P16s. Nonetheless, the P16s often pulls forward in areas like Life Sciences (1.68) and Vitality (1.28), possible benefiting from an analogous CPU structure paired with barely totally different tuning.<\/p>\n
The P14s usually trails the bigger methods in most classes, although its outcomes stay aggressive contemplating its smaller chassis and extra moveable design. Total, the outcomes counsel the P16v provides probably the most balanced workstation efficiency throughout skilled workloads, whereas the P16s stays shut behind in CPU-driven scientific and engineering duties.<\/p>\n
\n\n\nSPECworkstation 4.0.0 (increased is healthier)<\/b><\/span><\/th>\n \u00a0Lenovo ThinkPad P14s Gen 6 (Intel Extremely 7 265H)(NVIDIA RTX PRO 500)<\/b><\/span><\/th>\n Lenovo ThinkPad P16v Gen 3\u00a0 (Intel Core Extremely 9 285H)(NVIDIA RTX PRO 2000)<\/b><\/span><\/th>\n Lenovo ThinkPad P16s Gen 4 (Intel Core Extremely 7 265H)(NVIDIA RTX PRO 500)<\/b><\/span><\/th>\n<\/tr>\n\nTrade Verticals<\/b><\/span><\/td>\n<\/tr>\n\nAI & Machine Studying<\/td>\n 1.45<\/td>\n 1.63<\/td>\n 1.60<\/td>\n<\/tr>\n \nVitality<\/td>\n 1.13<\/td>\n 1.12<\/td>\n 1.28<\/td>\n<\/tr>\n \nMonetary Providers<\/td>\n 0.95<\/td>\n 0.98<\/td>\n 1.101<\/td>\n<\/tr>\n \nLife Sciences<\/td>\n 1.51<\/td>\n 1.62<\/td>\n 1.68<\/td>\n<\/tr>\n \nMedia & Leisure<\/td>\n 1.53<\/td>\n 1.60<\/td>\n 1.60<\/td>\n<\/tr>\n \nProduct Design<\/td>\n 1.16<\/td>\n 1.27<\/td>\n 1.32<\/td>\n<\/tr>\n \nProductiveness & Improvement<\/td>\n 0.87<\/td>\n 1.09<\/td>\n 1.07<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/span>SPECviewperf 15<\/span><\/h3>\nSPECviewperf 15 is the industry-standard benchmark for evaluating 3D graphics efficiency throughout OpenGL, DirectX, and Vulkan APIs. It introduces new workloads, together with blender-01 (Blender 3.6), unreal_engine-01 (Unreal Engine 5.4, DirectX 12), and enscape-01 (Enscape 4.0, Vulkan ray tracing), together with up to date traces for 3ds Max, CATIA, Creo, Maya, and SolidWorks. With its redesigned GUI, fashionable utility assist, and superior rendering workloads, SPECviewperf 15 offers constant, real-world insights into skilled graphics efficiency.<\/p>\n
SPECviewperf 15 outcomes display how every system handles skilled 3D graphics workloads throughout OpenGL, DirectX, and Vulkan APIs. The P16v performs strongly throughout the board, main in compute-intensive rendering duties akin to creo-04 (133.91), maya-07 (122.09), and medical-04 (105.90), whereas additionally topping the sphere in newer workloads like blender-01 (47.62) and unreal_engine-01 (57.02). The P14s trails in most classes, although it stays fairly aggressive given its moveable kind issue and shared GPU tier with the P16s. The P16s, regardless of carrying the identical RTX PRO 500 because the P14s, didn’t submit outcomes for catia-07 or enscape-01, limiting direct comparability in these workloads. Total, the P16v provides probably the most constant and highest graphics efficiency throughout all examined viewsets, pushed primarily by its RTX PRO 2000 GPU, whereas the P14s and P16s occupy an analogous efficiency tier notably behind it.<\/p>\n
\n\n\nSPECviewperf (increased is healthier)<\/b><\/span><\/th>\n \u00a0Lenovo ThinkPad P14s Gen 6 (Intel Extremely 7 265H)(NVIDIA RTX PRO 500)<\/b><\/span><\/th>\n Lenovo ThinkPad P16v Gen 3\u00a0 (Intel Core Extremely 9 285H)(NVIDIA RTX PRO 2000)<\/b><\/span><\/th>\n Lenovo ThinkPad P16s Gen 4 (Intel Core Extremely 7 265H)(NVIDIA RTX PRO 500)<\/b><\/span><\/th>\n<\/tr>\n\n3dsmax-08<\/td>\n 18.26<\/td>\n 30.54<\/td>\n 8.98<\/td>\n<\/tr>\n \nblender-01<\/td>\n 30.19<\/td>\n 47.62<\/td>\n 13.83<\/td>\n<\/tr>\n \ncatia-07<\/td>\n 32.65<\/td>\n 49.64<\/td>\n \u2013<\/td>\n<\/tr>\n \ncreo-04<\/td>\n 87.59<\/td>\n 133.91<\/td>\n 62.24<\/td>\n<\/tr>\n \nenergy-04<\/td>\n 34.95<\/td>\n 57.13<\/td>\n 11.90<\/td>\n<\/tr>\n \nenscape-01<\/td>\n 19.64<\/td>\n 27.82<\/td>\n \u2013<\/td>\n<\/tr>\n \nmaya-07<\/td>\n 86.42<\/td>\n 122.09<\/td>\n 37.79<\/td>\n<\/tr>\n \nmedical-04<\/td>\n 74.06<\/td>\n 105.90<\/td>\n 26.81<\/td>\n<\/tr>\n \nsolidworks-08<\/td>\n 39.26<\/td>\n 65.69<\/td>\n 35.65<\/td>\n<\/tr>\n \nunreal_engine-01<\/td>\n 40.99<\/td>\n 57.02<\/td>\n 31.12<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/span>LuxMark<\/span><\/h3>\nLuxMark is a GPU benchmark that makes use of LuxRender, an open-source ray-tracing renderer, to judge a system\u2019s efficiency with extremely detailed 3D scenes. This benchmark is related for assessing the graphical rendering capabilities of servers and workstations, particularly for visible results and architectural visualization functions, the place correct mild simulation is essential.<\/p>\n
Ray-tracing efficiency additional reinforces the P16v\u2019s GPU benefit. Within the Hallbench scene, the P16v scores 14,583, in comparison with 9,344 on the P14s and 10,449 on the P16s. The Meals scene follows the identical sample, with the P16v reaching 5,536, up from 3,543 and three,892, respectively.<\/p>\n
Whereas each RTX PRO 500 methods stay succesful for reasonable rendering workloads, the RTX PRO 2000 delivers a noticeable efficiency uplift in ray-traced rendering. For professionals working in visualization or lighting simulation, the distinction can translate immediately into sooner render previews and shorter iteration cycles.<\/p>\n
\n\n\nLuxMark (increased is healthier)<\/b><\/span><\/th>\n \u00a0Lenovo ThinkPad P14s Gen 6 (Intel Extremely 7 265H)(NVIDIA RTX PRO 500)<\/b><\/span><\/th>\n Lenovo ThinkPad P16v Gen 3\u00a0 (Intel Core Extremely 9 285H)(NVIDIA RTX PRO 2000)<\/b><\/span><\/th>\n Lenovo ThinkPad P16s Gen 4 (Intel Core Extremely 7 265H)(NVIDIA RTX PRO 500)<\/b><\/span><\/th>\n<\/tr>\n\nHallbench<\/td>\n 9,344<\/td>\n 14,583<\/td>\n 10,449<\/td>\n<\/tr>\n \nMeals<\/td>\n 3,543<\/td>\n 5,536<\/td>\n 3,892<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/span>7-Zip Compression<\/strong><\/span><\/h3>\n
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