Building an Accelerated Analytics Node: ClickHouse + NVLink-Connected RISC-V CPUs and Nvidia GPUs

Building an accelerated analytics node: ClickHouse + NVLink Fusion RISC-V CPUs + Nvidia GPUs

Hook: If your team is struggling to deliver sub-10ms inference on production analytics workloads or to collapse OLAP and near-real-time AI inference into a single node, this guide shows a practical architecture that pairs ClickHouse with SiFive’s NVLink Fusion-enabled RISC-V CPUs and Nvidia GPUs to cut data-path latency and simplify system design.

Why this matters in 2026

Late-2025 and early-2026 industry moves — ClickHouse’s rapid growth and funding, and SiFive’s announcement that it will integrate Nvidia’s NVLink Fusion into RISC-V platforms — make hybrid storage+AI nodes a realistic, high-payoff engineering project. ClickHouse is increasingly adopted as a high-throughput OLAP backbone, and NVLink Fusion promises to reduce CPU/GPU copy overheads and improve DMA semantics. Combining them produces a compact analytics node that targets:

• low-latency, high-throughput analytics
• co-located feature storage + GPU inference
• simplified data movement (less NIC, less PCIe hops)

High-level architecture

At a glance, the node has three tightly integrated layers:

1. Storage & Serving: ClickHouse runs as the analytics/feature store and OLAP engine.
2. Compute Control Plane: SiFive RISC-V CPU(s) with NVLink Fusion that can address GPU memory and coordinate transfers efficiently.
3. Accelerators: Nvidia data-center GPUs (Blackwell-series and successors) connected by NVLink Fusion for low-latency memory access.

Why NVLink Fusion plus RISC-V changes the game

NVLink Fusion is designed to provide tighter CPU/GPU coupling: fewer copies, lower latency, and more direct DMA/GPUDirect-like semantics. With SiFive integrating NVLink Fusion into its RISC-V IP (announced early 2026), you can expect platforms where the CPU can orchestrate direct transfers into GPU memory without traversing costly PCIe boundaries repeatedly. The result is a simplified pipeline for ClickHouse to feed inference engines with fewer software hops.

Target use cases

Design this node for workloads that need a tight loop between analytics queries and GPU inference:

• Real-time personalization: Query session features from ClickHouse, run GPU model inference, return personalized content in ms.
• Fraud detection: Evaluate feature vectors at transaction-time with low p99 latency.
• Feature store + ranking: Use ClickHouse for historical aggregates and feed batched feature vectors to GPU rankers.
• Embeddings retrieval and rerank: Store vectors in ClickHouse; perform top-k on CPU or GPU and rerank with deep models.

Concrete software stack

Below is a recommended, practical stack you can prototype with in 2026:

• OS: Ubuntu 24.04/26.04 LTS or Rocky Linux 9.x — tuned kernel with hugepages and NUMA awareness
• ClickHouse: Latest stable release (2025–2026), compiled with jemalloc and NUMA support
• GPU Drivers: Nvidia driver matching your data-center GPU (>=520.xx), CUDA 12/13 toolkits
• Nvidia Software: Triton Inference Server (for model serving) and TensorRT for optimized kernels
• GPUDirect / NVLink Fusion libraries: vendor-provided runtimes and NVLink Fusion SDK as available from Nvidia/SiFive
• Network/IO: GPUDirect Storage (GDS) capable NVMe drives for direct NVMe-to-GPU DMA; Mellanox/NVIDIA RDMA NICs if cross-node
• Orchestration: systemd / Nomad / lightweight kubelet for single-node lifecycle; monitor with Prometheus + node_exporter + nvidia-smi exporter

Data flow patterns

Three canonical flows are worth implementing and measuring:

1) Synchronous single-request inference (lowest latency)

1. Client queries an API on the node.
2. Controller process issues a ClickHouse query (select features by key).
3. ClickHouse returns rows into pinned CPU buffers.
4. Using NVLink Fusion capabilities, CPU coordinates a DMA into GPU memory (zero-copy/pinned path).
5. Triton/TensorRT runs inference and returns results to the CPU, which responds to the client.

2) Batched inference (throughput optimized)

1. Aggregate feature requests in a short time window.
2. Use ClickHouse to produce pre-aggregated feature batches (materialized views).
3. Transfer the entire batch into GPU memory and run high-throughput kernels.
4. Return responses or persist results back to ClickHouse.

3) Hybrid async pipeline (best latency/throughput trade-off)

1. Fast path: small latency-sensitive features are fetched and inferred synchronously.
2. Slow path: heavier contextual features are appended and processed asynchronously, updating models/offsets.

Practical setup: step-by-step prototype

Follow these steps to build a working prototype on a single node (or rack):

1. Choose hardware: acquire a SiFive RISC-V development board or OEM server with NVLink Fusion support and at least one Nvidia data-center GPU with NVLink. If you cannot access a Fusion-enabled RISC-V board yet, you can prototype on an x86 server while designing for the Fusion memory model.
2. Install OS + drivers: install a modern Linux kernel, enable hugepages and NUMA balancing, and install the Nvidia driver + CUDA toolkit.
3. Deploy ClickHouse: install the ClickHouse server and tune profiles for low-latency (see config tune section below).
4. Install Triton + model: run Triton with models optimized with TensorRT. Use ensemble models if you have preprocessing steps.
5. Wire data movement: use the NVLink Fusion SDK and GPUDirect libraries to enable pinned memory transfers. If using GPUDirect Storage, place feature stores on NVMe that supports GDS.
6. Implement the controller: a lightweight C++/Rust/Python microservice that issues ClickHouse queries, pins returned buffers, and invokes a DMA into GPU memory for Triton inference.
7. Benchmark, iterate: instrument everything and measure P50/P95/P99 latency and throughput. Tune CPU thread affinities, ClickHouse settings, and batch sizes.

ClickHouse tuning (practical knobs)

Tuning depends on your workload, but these actionable settings are a good starting point for low latency analytics nodes:

• max_memory_usage — bound per-query memory to avoid OOM.
• max_threads — reduce to match NUMA/CPU cores and eliminate noisy parallelism.
• mark_cache_size — allocate enough mark cache for your column files to reduce seeks.
• use_uncompressed_cache — enable if you have spare RAM for decompressed blocks that you'll feed to GPU.
• merge_tree settings — tune partitioning and primary key to favor point lookups when the node is a feature store.

Example: in /etc/clickhouse-server/config.xml tune per-profile settings:

<profiles>
  <low_latency>
    <max_memory_usage>21474836480</max_memory_usage>  <!-- 20 GB -->
    <max_threads>8</max_threads>
  </low_latency>
</profiles>

Controller pseudo-code (Python) using zero-copy semantics

This pseudo-code shows the control loop: query ClickHouse, pin buffer, request DMA into GPU, call Triton. Replace NVLink Fusion API calls with SDK calls when available.

from clickhouse_driver import Client
import tritonclient.grpc as grpcclient

ch = Client('localhost')
triton = grpcclient.InferenceServerClient(url='localhost:8001')

def fetch_and_infer(key):
    rows = ch.execute("SELECT feature1,feature2 FROM features WHERE id=%s", (key,))
    # rows -> numpy array; pin the underlying buffer (pseudo)
    pinned_buf = pin_cpu_buffer(rows)
    # request NVLink Fusion DMA into GPU (pseudo API)
    gpu_ptr = nvlink_fusion_dma(pinned_buf)
    # use triton to perform inference using gpu_ptr or GPU accessible memory
    result = triton.infer(model_name='ranker', inputs=[...], outputs=[...])
    return parse_result(result)

Observability and benchmarking

Measure three classes of metrics:

• Storage/Query: ClickHouse system tables (system.query_log, system.metrics)
• Transfer: NVLink Fusion SDK counters, PCIe/GDS DMA throughput
• Inference: Triton model latency and GPU utilization via nvidia-smi and Nsight

Targets: for synchronous lookups aim for P99 <= 10–30ms depending on model complexity. For batched pipelines target high GPU utilization (>60–80%) while keeping P95 within your SLA.

Design trade-offs and pitfalls

Be mindful of these trade-offs when designing the node:

• Memory coherency vs complexity: NVLink Fusion promises coherent semantics but adds ABI/driver complexity. Plan for driver/SDK updates.
• Single-node limits: a single node simplifies data movement but limits scale. Use this as a low-latency tier paired with wider ClickHouse clusters.
• Model size vs latency: very large models may not fit in single GPU memory; consider quantization or sharding.
• Operational maturity: SiFive + NVLink Fusion hardware/software was announced in early 2026 — expect evolving toolchains and firmware updates. Keep a testing channel with vendor.

Example real-world pattern: embedding retrieval + rerank

This pattern is common in search or recommendation:

1. ClickHouse stores pre-computed embeddings (float16 or int8 compressed) and metadata.
2. For a request, ClickHouse performs an approximate nearest neighbor (ANN) candidate selection or range scan, returning ids and vectors.
3. Vectors are moved to GPU via NVLink Fusion/GDS for reranking with a deep cross-attention model in Triton.
4. Final ranked results are returned to the application; ClickHouse is updated asynchronously with signals/metrics.

This collapses storage and inference close to the metal — fewer network hops, predictable latency.

Security and operational concerns

• Secure drivers: GPU/PCIe drivers are privileged; lock down update channels and use signed driver packages.
• Isolation: If running multiple tenants, use cgroup and MIG (if available) for GPU isolation; ClickHouse user profiles limit query resource consumption.
• Data governance: ensure logs and inference outputs comply with retention and privacy rules; ClickHouse supports TTL policies to auto-evict features.

Benchmarks and expected gains

Concrete numbers depend heavily on your models and data layout, but here are conservative, field-based expectations based on early NVLink/GPU coupling trends in 2025–2026:

• CPU-to-GPU copy latency cut by 2x–5x vs PCIe-only paths when NVLink Fusion and GPUDirect are leveraged.
• End-to-end P99 for small models (sub-10M parameters) can drop into the single-digit milliseconds with well-tuned stacks.
• Throughput (queries/sec) increases 3x–10x for batched workloads due to reduced transfer overhead.

Use A/B tests with realistic traffic; synthetic microbenchmarks often overstate gains because they ignore ClickHouse decompression and query CPU time.

Future-proofing and 2026 trends

As of 2026 you should design for change:

• Heterogeneous compute: more RISC-V + accelerator combos will appear. Abstract your DMA and memory APIs behind a small interface layer.
• Model evolution: models are moving toward efficient quantized formats (int8/4-bit) — store compressed features in ClickHouse to reduce transfer sizes.
• ClickHouse ecosystem: ClickHouse continues rapid adoption and investment; expect more extensions and integration points for ML/AI workloads through 2026.

Design for portability: keep the logic of pinning, DMA orchestration, and model invocation isolated so you can swap CPU/GPU vendors or newer NVLink Fusion drivers without changing application logic.

Checklist: building your first accelerated analytics node

1. Obtain or emulate an NVLink-capable node (SiFive/RISC-V or x86 with NVLink) and an Nvidia GPU.
2. Install OS, drivers, ClickHouse, Triton, and NVLink/GDS SDKs.
3. Design ClickHouse schemas optimized for point lookups and small-row feature fetches.
4. Implement and test zero-copy transfer path from ClickHouse result buffers into GPU memory.
5. Deploy a small inference model in Triton; measure P50/P95/P99 and tune batch sizes.
6. Iterate: increase concurrency, enable compression, and add async paths for heavy features.

Actionable takeaways

• Leverage NVLink Fusion to minimize CPU/GPU copy overhead and enable low-latency inference loops.
• Use ClickHouse as a fast feature store/OLAP layer and tune it for small, frequent lookups if you target low latency.
• Isolate transfer logic behind a small SDK so hardware changes are non-disruptive.
• Benchmark rigorously — measure ClickHouse query times, DMA latency, and GPU inference latency separately and together.

Closing: why build this now

With ClickHouse’s rising momentum in the analytics and OLAP market and SiFive’s NVLink Fusion roadmap announced in early 2026, integrating ClickHouse, RISC-V NVLink Fusion CPUs, and Nvidia GPUs is no longer a speculative experiment. It’s a practical way to build a compact analytics node that excels at the tight loop between feature access and GPU inference — precisely the capability modern AI-driven applications need to meet aggressive latency SLAs.

Call to action: Ready to prototype? Start with a minimal node: ClickHouse + single Nvidia GPU + a controller service that pins buffers. If you want a hands-on checklist, sample controller code, and a reference ClickHouse schema tuned for feature lookup and embedding storage, grab the companion GitHub repo and step-by-step guide we published for this article — or reach out and we’ll help you blueprint your accelerated analytics node for production.

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