Hosting GPU-Accelerated Multi-tenant Analytics with ClickHouse and NVLink-Powered Nodes

Hook: Stop losing milliseconds — and money — at query time

If you run analytics for multiple tenants and serve model-backed features at query time, you already know the pain: slow joins, CPU-bound feature transforms, unpredictable costs, and brittle scaling during traffic spikes. This guide shows a battle-tested architecture — pairing ClickHouse for OLAP with NVLink-connected GPU nodes — to accelerate query-time ML features, tighten SLOs, and simplify multi-tenant operations in 2026.

Executive summary — why this matters in 2026

ClickHouse continues to gain enterprise traction (its 2025 funding round underlined market momentum), while GPU fabrics and NVLink Fusion are lowering friction for tight CPU–GPU data paths. Combining an OLAP engine optimized for fast analytical reads with GPU-persistent feature transforms and inference reduces latency, lowers egress and CPU costs, and enables richer query-time ML features for many tenants. This article gives architecture patterns, operational playbooks, and step-by-step deployment guidance.

Key components of the hybrid architecture

At a high level the design splits responsibilities to get the best of both worlds: ClickHouse for durable, cost-effective analytical storage and GPU clusters for compute-heavy, latency-sensitive transforms and inference. Components:

• ClickHouse cluster (hot/cold tiers) — persistent OLAP storage, pre-aggregations, materialized views for feature precomputation.
• NVLink-connected GPU nodes — tightly-coupled GPUs for feature transforms (cuDF/RAPIDS), vector operations, and model inference (NVIDIA Triton or TorchServe), using NVLink and GPUDirect to minimize copy overhead.
• Data plane — low-latency transport (RDMA/InfiniBand or 100/200GbE + GPUDirect RDMA) and Arrow-based zero-copy interchange between ClickHouse rows and GPU memory.
• Feature orchestration — materialized views and streaming (Kafka / ClickHouse Kafka engine) to feed online feature pipelines into GPU caches.
• Control plane — Kubernetes for worker orchestration, NVIDIA device-plugin and MIG for GPU multiplexing, ClickHouse Keeper/ZooKeeper for coordination.
• Observability & SLOs — Prometheus, Grafana, NVIDIA DCGM, and ClickHouse system tables for tenant-level SLIs and billing tags.

Recent trends you should design for (2025–2026)

Two developments directly impact architecture decisions in 2026:

• ClickHouse's market momentum after its 2025 funding round means faster product evolution, improved integrations, and stronger community support — a safer bet for long-term analytic workloads. See the Bloomberg coverage for context.
• NVLink Fusion and tighter CPU–GPU fabrics (industry moves like SiFive's NVLink Fusion integration announced in 2025) are making cross-socket GPU fabrics and RISC-V integrations possible, which unlocks denser, lower-latency GPU clusters for inference and feature ops.

Both trends favor hybrid architectures where storage and compute are optimized independently and connected through high-speed data paths.

Why ClickHouse + NVLink GPUs? The core benefits

• Latency: NVLink + GPUDirect minimizes copies between NIC, CPU, and GPU, reducing query-time ML latency by eliminating host-GPU shuttles.
• Throughput: GPU-native vector transforms (cuDF / RAPIDS / CUDA kernels) chew through large feature batches much faster than CPUs for dense numeric workloads.
• Cost control: Storing wide historical data in ClickHouse (cheaper per TB than GPU memory) and only promoting hot features to GPU memory reduces overall operational cost.
• Multi-tenancy: Use ClickHouse's user-level isolation and per-tenant quotas with GPU partitioning (MIG) and namespace-aware autoscaling to safely share hardware across customers.

Detailed architecture pattern — hot vs. hot-GPU paths

Design two primary read paths:

1. ClickHouse hot path: fast OLAP reads and simple aggregations served directly from ClickHouse nodes for queries that need only DB-level computation.
2. GPU-accelerated path: for ML-backed features or heavy vector ops, ClickHouse returns a compact projection (IDs, timestamps, numeric vectors) to a GPU worker which performs transformations, joins to model embeddings, or runs inference on a model hosted in GPU memory.

The decision between paths can be dynamic: a query planner or middleware service inspects the query and routes to GPU workers when model features are requested.

Step-by-step deployment playbook

Below is an operational sequence to get a production-ready hybrid cluster running.

1) Capacity planning and hardware choices

• Choose GPU type: prefer GPUs with MIG support (A100+/H100 family) if you need fine-grained multi-tenancy. If you need maximum single-model memory, pick the largest memory SKU.
• Interconnect: target NVLink-backed node couples and a high-bandwidth fabric (InfiniBand HDR or 200GbE with RDMA). Enable GPUDirect RDMA on NICs and kernel drivers.
• Storage tiers: NVMe local for ClickHouse hot cache, S3 for colder ClickHouse storage and model artifacts.

2) ClickHouse schema & ingestion

• Use MergeTree for time-series/feature tables; partition by tenant and by day/month to keep reads efficient.
• Create materialized views to precompute feature aggregates and downsampled metrics.
• Use the ClickHouse Kafka engine or a lightweight CDC stream to keep GPU-side caches warm with up-to-date features.
• Tag rows with tenant_id and cost_center fields for chargeback.

3) Data interchange and zero-copy patterns

Use Arrow as the interchange format between ClickHouse and GPU workers. Arrow buffers are easy to map into GPU memory using libraries like cuDF and RAPIDS; this avoids repeated serialization.

• Export ClickHouse query results as Arrow/IPC over IPC sockets or shared memory when on the same host.
• For cross-host transfers, use RDMA-enabled transport and GPUDirect to write directly into GPU memory.

4) GPU worker stack

• Run GPU tasks in Kubernetes with the NVIDIA device-plugin and use MIG slices for per-tenant isolation when appropriate.
• Host model inference in NVIDIA Triton or a lightweight PyTorch/TensorFlow runtime pinned to GPU memory for warmed models.
• For feature transforms and joins, use RAPIDS/cuDF and cuML for vector ops and approximate nearest neighbors (ANN) on GPU (Faiss-GPU).

5) Routing and query middleware

Implement a routing layer that inspects ClickHouse queries or SQL hints and selects CPU vs GPU path. This layer also handles multi-tenant quotas and schema translations.

6) Autoscaling and cost controls

• Autoscale GPU worker pools based on queue depth, p99 latency, and GPU utilization.
• Use spot/preemptible instances for non-critical batch GPU workloads and dedicated nodes for latency-sensitive inference.
• Enforce per-tenant quotas in ClickHouse and Kubernetes to prevent noisy-neighbor spikes.

Multi-tenant best practices — safe sharing of GPUs and ClickHouse

Multi-tenancy demands strict isolation, observability, and billing. Follow these patterns:

• Tenant partitioning: Partition ClickHouse tables by tenant_id and apply strict resource groups and quotas per tenant.
• GPU isolation: Use MIG or GPU node pools labeled per-tenant. For smaller tenants or unpredictable loads, use shared pools with fair scheduling and throttling.
• Request tagging & cost allocation: Propagate tenant metadata across the request path (ClickHouse -> middleware -> GPU worker) and record it in traces/metrics for accurate billing.
• Policy enforcement: Apply admission control at the middleware to limit concurrent GPU requests and enforce per-tenant SLOs.

Observability and debugging

Track these metrics and set up automated alerts:

• ClickHouse read latency, rows read per query, and merged parts.
• GPU metrics: utilization, temperature, memory usage, MIG occupancy (via NVIDIA DCGM).
• Interconnect: RDMA error rates, throughput, NIC drops.
• Application: end-to-end p50/p99 for query-time feature retrieval + inference.

Correlate traces across ClickHouse queries and GPU inference calls to pinpoint hotspots. Use OpenTelemetry to propagate context through the pipeline.

Security and compliance

• Encrypt in transit (mTLS) between ClickHouse and GPU workers. NVLink is a local fabric and doesn't replace network encryption for cross-host communication.
• Enable access control in ClickHouse (users, roles) and use Kubernetes RBAC for GPU workloads.
• Audit all model and query changes. Maintain immutable model artifacts in an artifact repository and sign them for production deployment.

Common pitfalls and how to avoid them

1. Overloading GPUs with small queries: Batch short queries or use an async inference queue. Small, frequent requests waste GPU cycles.
2. Network bottlenecks: If your NICs or fabric are underprovisioned, you’ll see CPU fallback and massive serialization costs. Size the fabric to match peak throughput and enable GPUDirect RDMA.
3. Poor data layout in ClickHouse: Wide rows and full-table scans kill performance. Use projections, materialized views, and careful partitioning.
4. Neglecting tenant quotas: Noisy tenants can blow up GPU costs; enforce per-tenant controls from day one.

Example: a compact workflow for query-time feature serving

Here’s a minimal flow you can implement within weeks to move from CPU-bound to GPU-accelerated query-time features.

1. Store raw events and precomputed features in ClickHouse tables partitioned by tenant_id and date.
2. Create a materialized view that computes the most common feature vector projection per tenant and writes compact Arrow blobs into a cache table.
3. On incoming analytic queries, the middleware requests the projection from ClickHouse (Arrow) and streams it directly into a GPU worker using RDMA/GPUDirect.
4. The GPU worker executes cuDF transforms, performs a k-NN lookup with Faiss-GPU for embedding joins, and executes model inference (Triton) if necessary.
5. Return enriched results to the client; record metrics and billing tags.

Performance tuning checklist

• Enable ClickHouse compression codecs appropriate for numeric arrays (LZ4/Delta) to reduce bandwidth.
• Pre-warm GPU model memory and caches for cold-start reductions.
• Use vectorized Arrow transfers and avoid JSON/binary conversions where possible.
• Batch small inference requests to improve GPU throughput while keeping latency SLOs in check.
• Profile end-to-end with representative tenant workloads and iterate on partition sizes and MIG slice counts.

Case study (hypothetical): SaaS analytics provider

A mid-market SaaS analytics vendor migrated hot features to this hybrid model in late 2025. Key outcomes:

• Median query latency for ML-backed features dropped by ~3–5x for p50 workloads (measured after enabling GPUDirect + Arrow zero-copy).
• Monthly cloud spend for CPU compute dropped since heavy vector math moved to fewer, denser GPU nodes.
• Per-tenant billing granularity improved by instrumenting tenant tags across ClickHouse and the GPU worker pool.

Note: results depend on workload composition. Dense numeric workloads benefit most; sparse, high-cardinality joins sometimes still favor CPU-side pre-aggregation.

Advanced strategies and future directions

Looking to the next 12–24 months, expect these trends to shape architectures:

• More DB-level GPU integration: Vendors will add first-class GPU operators to OLAP engines; be ready to adopt hybrid query planners.
• Composable GPU fabrics: NVLink Fusion and RISC-V integrations (e.g., SiFive moves) will enable more compact, energy-efficient edge and on-prem GPU fabrics for localized multi-tenant analytics.
• Unified feature stores: Feature catalogs that span ClickHouse, object store, and GPU caches with lineage and governance baked in will become standard.

"The most practical performance wins come from reducing copies and matching data locality to compute — not from blindly throwing GPUs at the problem."

Checklist before you go to production

1. Have tenant-level SLAs and quotas defined and enforced.
2. Measure and budget for NVLink/GPU fabric costs, including overprovisioning for peaks.
3. Run a chaos/interrupt test for preemptible GPU workflows and verify fallbacks to CPU path.
4. Automate model deployment and signing; avoid manual model swaps on GPU nodes.
5. Provide an emergency knob to route all requests back to ClickHouse-only path if GPUs fail.

Next steps — a practical starter plan (30 / 90 / 180 days)

30 days

• Prototype with a single ClickHouse replica and one NVLink-coupled GPU node.
• Implement Arrow export from ClickHouse and a GPU worker that runs a simple cuDF transform and Triton inference.

90 days

• Scale to multi-node ClickHouse, enable RDMA/GPUDirect, and introduce routing middleware.
• Instrument tenant metrics and implement basic autoscaling for GPU worker pools.

180 days

• Harden multi-tenant isolation with MIG and enforce quotas; roll out production workloads.
• Optimize costs (spot instances, node sizing) and finalize billing/reporting dashboards.

Useful links & references

• Bloomberg coverage of ClickHouse funding (2025) — context on market momentum.
• Forbes coverage of NVLink Fusion / SiFive — industry integration trends (2025).
• NVIDIA documentation: GPUDirect RDMA, MIG, and Triton for inference servers.
• RAPIDS / cuDF / Faiss-GPU — GPU-native analytics and ANN libraries.

Final recommendations

Start with a clear tenant classification: which tenants need query-time ML and which can use precomputed features. Then, instrument everything. The single biggest technical lever is removing unnecessary host-GPU copies: Arrow + GPUDirect + NVLink changes the cost/latency tradeoff enough to make this hybrid architecture a practical production choice in 2026.

Call to action

Ready to prototype? Start with a one-node ClickHouse + NVLink GPU PoC using the 30/90/180 plan above. If you want a tailored architecture review and cost model for your tenant mix and workloads, contact our engineering team at beek.cloud for a focused workshop and reference implementation.

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