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ClickHouse Says It Scaled Near-Linearly Across Vera's 176 Physical Cores in In-Memory Benchmarks and Topped the ClickBench Hardware Leaderboard

In a blog post, ClickHouse reports that three CPU-bound micro-benchmarks scaled close to linearly up to 176 threads on an early-access NVIDIA Vera server, with efficiency falling beyond that.

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Overview

ClickHouse published an engineering post on October 21, 2026, describing how its database performed on an early-access server built on NVIDIA’s upcoming Vera CPU. In the ClickHouse blog, author Robert Schulze writes that the company ran ClickBench, its analytics benchmark, on the machine and that “NVIDIA Vera immediately made it to the top of the ClickBench hardware leaderboard by a wide margin.” The post then goes beyond that headline result and measures how well ClickHouse scales as threads are added. All figures below are ClickHouse’s own and have not been independently reproduced.

The Hardware and the Setup

According to the ClickHouse blog, the company used “the standard ClickHouse binary from our website” and did not perform any optimizations or customizations in the database specifically for NVIDIA Vera. The post describes Vera as featuring 88 NVIDIA Olympus cores that are fully Arm-v9.2-compatible. In a two-socket system, that adds up to 176 cores with 352 hardware threads thanks to NVIDIA Spatial Multithreading, and the post gives the maximum memory bandwidth as ranging from 1.0 TB/s to 1.2 TB/s depending on DRAM capacity. NVIDIA’s product page likewise lists 88 custom NVIDIA Olympus cores and up to 1.2 terabytes per second of LPDDR5X memory bandwidth.

The test table held a single column of 62.5 billion random strings, each between 5 and 10 ASCII characters, for a total size of exactly 1 TB. It used ClickHouse’s Memory table engine so that, in the post’s words, disk I/O did not pose an artificial bottleneck. The post says the strings were stored raw without compression.

What the Benchmarks Showed

Schulze built three micro-benchmarks covering filter evaluation, aggregation and joins, and varied the thread count through the max_threads setting.

  • Filter, simple LIKE query: Runtime fell from 50.44 seconds on one thread to 1.23 seconds on 64 threads, a 41.01x speedup, then barely improved at 256 threads (1.11 seconds). The ClickHouse blog attributes this to the query being memory bound, with throughput of 813 GB/s at 64 threads, a figure it says roughly corresponds to the physical memory bandwidth of the system.
  • Filter, regular-expression query: To keep the query CPU-bound, the post switched to a costlier match expression. Runtime fell from 1209.7 seconds on one thread to 7.4 seconds on 176 threads, a 163.47x speedup at 92.88% efficiency. At 352 threads the query took 5.3 seconds, a 228.25x speedup at 64.84% efficiency.
  • Aggregation: Grouping into 255 groups with an added hash calculation took 884.1 seconds on one thread and 5.4 seconds on 176 threads (163.72x, 93.02% efficiency). At 352 threads it took 4.9 seconds, the same as at 256, for a 180.43x speedup.
  • Join: Joining the large table against a 625,000-row dimension table took 1132.6 seconds on one thread and 7.3 seconds on 176 threads (155.15x, 88.15% efficiency), and 6.1 seconds at both 256 and 352 threads.

The post says the CPU-bound execution times “scale almost perfectly with the number of CPU cores,” and, of the join, “In other words, ClickHouse scales perfectly.” It also notes that efficiency begins to decline gradually at 176 cores, the number of physical cores in the machine, because additional software threads are served by logical hardware threads that share physical cores. According to the post, such threads help only moderately, for example a 10-30% speedup, for compute-bound workloads.

SIMD Test and Agentic Workloads

In a separate single-thread test, the post reports that a simple filter over 2.5 billion UInt32 values completed in 0.42 seconds, versus 1.48 seconds when the compiler’s auto-vectorization was disabled, which the post calls 3.5x slower. It says ClickHouse currently uses Vera’s 128-bit NEON instructions and that support for the chip’s SVE2 implementation is being added together with the open-source community in PR #122860.

The post frames the work partly around AI agents. It states that the company increasingly sees agents in ClickHouse Cloud autonomously answering data questions, and describes agentic queries as more “ad-hoc” than dashboard queries, arriving at unpredictable times and often in bursts, with many running in parallel as agents spawn sub-agents. The post closes with a statement from Alexey Milovidov, ClickHouse CTO: “NVIDIA Vera is an incredibly fast and powerful server platform - the fastest we have seen so far.”

What We Don’t Know

  • The post does not publish the ClickBench leaderboard figures themselves in the text reviewed, so the size of the “wide margin” is not quantified there.
  • The micro-benchmarks used synthetic, uncompressed in-memory data. The post says that, assuming sufficient storage bandwidth, the results translate to a regular MergeTree table, but it does not test one.
  • The aggregation test had only 255 groups. The post itself calls this “low cardinality” and says high-cardinality aggregation poses a greater challenge.
  • The post does not compare Vera against another CPU in these scaling tests, and does not report price or power use.

Context

The DuckDB team also published Vera results this month, reporting that a development build of its engine showed “a 46% performance advantage on TPC-H SF1,000 over the x86 baseline (Intel Xeon 6),” according to the DuckDB blog, as previously reported. That test restricted DuckDB to a single socket of a dual-socket system, while ClickHouse’s scaling runs covered up to 352 threads.