NVIDIA 公布 Vera CPU 智能体性能数据，对比 x86 最高快 2 倍

NVIDIA reported new Vera CPU results on November 5, 2026, saying a 100-megawatt data-center comparison showed 2x faster agent speed than the most energy-efficient x86 CPU and 1.6x more agents per watt than the fastest x86 CPU.

Those figures address different operating constraints. Agent tasks repeatedly move from GPU reasoning to CPU execution for searches, database queries, code writing and testing, and other actions. Operators therefore need both responsive individual agents and enough concurrent capacity to keep a power-limited facility productive. NVIDIA is positioning Vera as a single processor for both requirements, rather than a mix of fast and dense x86 parts.

Perplexity reported that early Vera tests ran Computer-style workflows roughly 1.5x faster than its current production reference and started concurrent sandboxes up to 1.9x faster. The result also served as a backend test for SPACE, its sandbox platform, which uses disposable environments for individual tasks and sessions that can pause, resume or branch longer work. NVIDIA says Perplexity plans to deploy Vera in an upcoming production system.

DeepInfra’s July 21, 2026 tests replayed captured traffic from its production agent at a fixed pace and generated no live model tokens, removing model randomness from the comparison. Vera, AMD Zen 5, Intel Granite Rapids and Intel Sapphire Rapids were confined to identical 20-physical-core, single-NUMA partitions with one thread per core; runs that escaped the core budget were discarded.

DeepInfra measured bare-metal orchestration steps at 29 milliseconds on Vera, against 35 milliseconds on AMD Zen 5, 51 on Granite Rapids and 64 on Sapphire Rapids, making Vera the fastest of the four and 1.2x faster than the best x86 result. In secure microVMs, with a 99th-percentile response limit of six seconds and zero errors required, Vera sustained 256 concurrent agents. The incumbents sustained 160, although Granite Rapids reached 192 on its best run.

The same tests identified a cost that no chip escaped. Secure microVM sandboxing added a 1.5–2x penalty on typical work and roughly 5–13.5x on process spawning across architectures. DeepInfra found that Vera performed best under that stack, but identified the isolation layer itself as the remaining opportunity for hosting untrusted, multi-tenant agents more efficiently.

Daytona reported on October 7, 2026 that Vera prepared 1,000 sandboxes in 10.8 seconds, compared with 32.3 seconds for AMD’s EPYC 9575F and 88.4 seconds for the EPYC 9755. Vera needed 27.4 seconds to prepare 2,000. With 2,000 concurrent sandboxes executing the same coding-agent task, the system completed 12.7 agent jobs per second, against 10.9 on the EPYC 9755 and 8.1 on the EPYC 9575F.

ClickHouse reported on October 21, 2026 that its standard binary, with no Vera-specific tuning, reached the top of the ClickBench hardware leaderboard on a dual-socket Vera machine with 176 physical cores and 352 hardware threads. Its more compute-intensive filtering, aggregation and join tests scaled nearly proportionally through the physical-core count. A simpler substring filter stopped gaining substantially beyond 64 threads, when memory throughput was approaching the system’s bandwidth limit. That contrast shows why core counts alone cannot predict agent capacity: compute-heavy work can use the available parallelism, while memory-bound work stops benefiting from additional threads.

DuckDB reported on October 16, 2026 that Vera ran 1.53–1.61x faster in its power test and 1.37–1.41x faster in its throughput test than an Intel Xeon 6 baseline. DuckDB used one Vera socket with 88 threads and configured the Xeon system with 96 threads. The derived scale-factor-1,000 workload did not satisfy every TPC-H requirement and is not comparable with published TPC-H results.

NVIDIA’s Vera specifications list 88 custom Olympus cores per CPU, with Spatial Multithreading supplying 176 threads, plus up to 1.2 TB/s of LPDDR5X memory bandwidth and up to 1.5 TB of memory. The second-generation Scalable Coherency Fabric connects the cores, cache, memory, I/O and NVLink-C2C on one compute die with 3.4 TB/s of bisectional bandwidth. NVLink-C2C provides up to 1.8 TB/s of coherent bandwidth between Vera CPUs and NVIDIA GPUs.

Vera can operate as a host CPU in Vera Rubin systems or as a standalone processor for agentic AI, reinforcement learning, data processing and analytics. Deployment options include standard single- and dual-socket systems and a dense liquid-cooled rack.

For infrastructure operators, the practical implication is that agent capacity cannot be read from core count or a single speed ratio alone. The records tie Vera’s potential gains to compute, memory and connectivity, but the partner tests also show that sandbox isolation and memory-bound work can determine how much of that potential survives in production.