Artificial Analysis 实测 NVIDIA RTX Spark 笔记本本地智能体性能
NVIDIA's RTX Spark N1X puts up to 128 GB of unified memory and a Blackwell GPU in a Windows laptop. In our first AA-AgentPerf-Local results, an RTX Spark laptop
Artificial Analysis 首次公布 AA-AgentPerf-Local 结果，搭载 NVIDIA RTX Spark N1X 的笔记本在 Qwen3.8 27B 上完成默认本地智能体负载（8 个任务、168 个模型轮次）耗时 18.7 分钟，接近 DGX Spark 的 17.3 分钟，比 MacBook Pro（M5 Pro，64 GB）快 2.0 倍，比 AMD Ryzen AI Halo 快 1.8 倍，GeForce RTX 5090 台式机最快为 4.9 分钟。N1X 芯片本质上是 DGX Spark 的 GB10（20 核 Arm CPU、6,144 核 Blackwell GPU），最高 128 GB 内存、带宽 300 GB/s；测试机型为 15 英寸 Surface Laptop Ultra 原型机（128 GB，5,900 美元），插电并设为 Windows 高性能模式，使用 NVIDIA 公布的 llama.cpp 配方。在 Qwen3.6 35B A3B 上耗时 7.6 分钟，在 Ling 3.0 Flash 上耗时 15.3 分钟，后者的 77 GB 权重无法装入 MacBook Pro 或 RTX 5090。
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Artificial Analysis 首次用本地智能体负载实测 RTX Spark 笔记本，给出与 DGX Spark、MacBook Pro 等设备的耗时对比。
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NVIDIA's RTX Spark N1X puts up to 128 GB of unified memory and a Blackwell GPU in a Windows laptop. In our first AA-AgentPerf-Local results, an RTX Spark laptop completes our local agent workload on Qwen3.8 27B in 18.7 minutes
RTX Spark's N1X chip is essentially the GB10 from DGX Spark (20-core Arm CPU, 6,144-core Blackwell GPU), with up to 128 GB of memory at 300 GB/s. We tested a prototype 15-inch Surface Laptop Ultra (128 GB, $5,900), plugged in with Windows set to high-performance mode, using NVIDIA's published llama.cpp recipes where available
Key results:
➤ On Qwen3.8 27B the RTX Spark laptop finishes our default workload (8 recorded agent tasks, 168 model turns) in 18.7 minutes, close to DGX Spark (17.3), 2.0x faster than the MacBook Pro (M5 Pro, 64 GB, 37.6) and 1.8x faster than the AMD Ryzen AI Halo (34.5). The GeForce RTX 5090 desktop is fastest, at 4.9 minutes
➤ On Qwen3.6 35B A3B it takes 7.6 minutes, close to DGX Spark (7.3), and 1.6x faster than the MacBook Pro (11.9)
➤ On Ling 3.0 Flash, the largest current model we test, it takes 15.3 minutes, close to DGX Spark (14.9) and 1.6x faster than the Ryzen AI Halo (25.1). Its 77 GB of weights do not fit on the MacBook Pro or the RTX 5090