hardwarestrategyprocurement

Which Hardware Vendors Win the AI Infrastructure Game? What Similarity Search Teams Should Watch

ffuzzypoint

2026-01-29

10 min read

Which vendors matter for vector search in 2026? Decode Broadcom, NVIDIA, and AMD to choose GPUs, NICs, and memory—and decide procure vs lease.

Hook: Your similarity search SLA depends on a vendor chess match

If your vector search cluster misses SLAs after a spike, it’s often not a software bug — it’s a hardware choice. Memory scarcity and price pressure, GPU supply, and NIC behavior shape latency, recall, and total cost of ownership for production similarity search. In 2026, teams can’t treat GPUs, NICs, and memory as interchangeable: Broadcom, NVIDIA, and AMD are playing different long games that change procurement, leasing, and architecture decisions.

The 2026 context — why vendor dynamics matter right now

Late 2025 and early 2026 brought two clear trends that make vendor strategy a first-class engineering decision:

Memory scarcity and price pressure. As reported at CES 2026 and covered by industry press, AI-driven demand for HBM and high-density DRAM pushed prices up and squeezed supply chains. That affects how much hot memory you can realistically buy for on-prem clusters and increasing cost per GB for low-latency index hosting.
Consolidation in networking and silicon. Broadcom’s market muscle (noted in recent market coverage) and NVIDIA’s continued GPU ecosystem dominance (CUDA, high-HBM GPUs, InfiniBand via Mellanox) mean infrastructure choices increasingly tie you to ecosystems rather than components.
Cloud and leasing proliferation. Specialized GPU colocation and HaaS providers matured their offerings in 2024–2026, letting teams lease advanced GPUs and networking stacks for predictable bursts instead of large CAPEX buys.

What similarity search teams must ask right now

Do I need HBM-heavy GPUs (fast on-device indices) or a large pool of system DRAM and NVMe for CPU-based indices?
Is RDMA/InfiniBand essential for my cross-node queries, or will RoCE/Ethernet suffice?
Is vendor lock-in acceptable for the latency and tooling I need?
Lease for elasticity or buy to minimize per-query cost at scale?

Vendor snapshot: Broadcom, NVIDIA, AMD — what each brings to vector search

NVIDIA — the ecosystem and latency king

NVIDIA remains the default for GPU-heavy similarity search: strong HBM availability on top-tier GPUs, optimized libraries (cuBLAS/cuFFT/NCCL), and broad support across frameworks (FAISS, Triton, ONNX runtimes). NVIDIA’s InfiniBand lineage (Mellanox) continues to give teams low-latency RDMA options for sharded nearest-neighbor searches.

Strengths: Best-in-class GPU software stack, broad marketplace support, mature DPU/SmartNIC integrations (BlueField family), and a huge community for model and index optimizations.
Risks: High price and occasional supply contention on the newest HBM-rich GPUs; tighter vendor lock-in through CUDA-centric toolchains.

AMD — the value and openness alternative

AMD has closed the gap on raw compute and provides a valuable alternative for teams prioritizing cost control and open-stack compatibility. ROCm ecosystem improvements through 2024–2026 mean more libraries now run well on AMD hardware, and vendors are shipping competitive server GPUs with attractive memory-to-cost ratios.

Strengths: Strong price-performance on many workloads, growing ROCm/oneAPI tooling, and less entrenched lock-in.
Risks: A smaller ecosystem for highly optimized similarity search libraries, occasional driver or toolchain rough edges, and fewer turnkey managed offerings in the market.

Broadcom — the hidden infrastructure giant

Broadcom’s influence is felt more in the networking and memory supply chains than in GPUs. By 2026 its market position and recent acquisition-driven expansion gave it outsized control over top-of-rack switching, Ethernet NICs, and enterprise networking software. For many clusters, the NIC and switch choice determines achievable QPS and cross-node latency.

Strengths: Broadcom NICs and switches are ubiquitous in hyperscale racks; strong hardware offloads and telemetry; enterprise support and supply leverage.
Risks: Consolidation can introduce single-vendor dependency and pricing pressure; compatibility quirks with RDMA/RoCE stacks require careful validation.

Architecture decisions: GPUs, memory tiers, and networking for vector search

Picking hardware isn’t binary — it’s a system design exercise. Below are patterns proven in production similarity search estates.

Pattern A — Low-latency, high-recall: GPU-heavy single-node indices

When latency per query matters (<10ms tail), and you can fit the working set on-device, GPUs with ample HBM win. Typical stack: FAISS-GPU + on-GPU IVF-PQ/HNSW, GPU-resident embeddings, and local SSD/NVMe for persistence.

Hardware choices: NVIDIA HBM-rich GPUs (H100/Blackwell-class successors) or AMD equivalents if supported; InfiniBand or low-latency Ethernet for replication/leader election traffic.
Trade-offs: High upfront cost and HBM scarcity risk; lowest tail latency and simplest retrieval path.

Pattern B — Cost-sensitive scale: CPU + DRAM with tiered NVMe

For huge repositories (hundreds of millions to billions of vectors) where per-query latency can tolerate a few tens of milliseconds and you prioritize cost, CPU-optimized HNSW or IVF with PQ on DRAM + NVMe tiering is effective.

Hardware choices: Large pools of host DRAM, NVMe SSD tiers, Broadcom Ethernet for dense top-of-rack switching.
Trade-offs: Higher query latency than in-GPU solutions but much better capacity per dollar; fewer dependencies on scarce HBM.

Pattern C — Hybrid: GPUs for re-ranking, CPU for candidate generation

Many teams get the best of both worlds by serving a coarse candidate set from an efficient CPU index, then running re-ranking or expensive distance computations on GPUs. This reduces GPU count while keeping tail latency reasonable.

Hardware choices: Moderate GPU fleet (for re-rank), high DRAM nodes for primary sharding, and low-latency Broadcom switches to reduce fetch time.
Trade-offs: Requires careful batching and orchestration to avoid GPU hot-spots; complexity in deployment but excellent TCO at scale.

Networking: When to choose InfiniBand (Mellanox/NVIDIA) vs Broadcom Ethernet

Networking choices shape cross-node latency and operational complexity. InfiniBand (RDMA) remains superior for sub-millisecond cross-node NN searches and synchronous sharded retrieval. Broadcom’s Ethernet and RoCE stacks are increasingly capable, especially with advanced NIC offloads, but require rigorous validation.

Pick InfiniBand/RDMA when: your architecture needs cross-node synchronous queries, and you have an NVIDIA-based stack that benefits from proven RDMA tooling.
Pick RoCE/Broadcom Ethernet when: cost constraints exist, or you require interoperability with a broader set of hardware and switch vendors. Expect to invest engineering time in queue management, congestion control, and NIC driver tuning.

Operational tip: Test the exact query pattern (vector size, batch sizes, index type) on your candidate NIC and switch before signing large procurement deals. Microbenchmarks often miss real-world tail behavior.

TCO and procurement vs leasing — a practical decision framework

Deciding whether to buy hardware or lease it is the classic CAPEX vs OPEX problem — but for vector search there are specific levers to model.

Key inputs for your TCO model

Baseline QPS and peak QPS (monthly distribution)
Target tail latency and required recall/precision
Vector dimensionality and cardinality (used to estimate memory/HBM needs)
Software stack: CUDA vs ROCm vs CPU-only — this affects vendor lock-in and driver support costs
Network fabric: cost of switches, NICs, and cabling
Operational costs: power, cooling, rack space, maintenance, staff

Procure when:

Workload is stable and predictable (large steady-state QPS)
You can amortize CAPEX over long refresh cycles and operate at high utilization
Vendor support contracts (warranty, spare parts) are favorable

Lease or use GPU HaaS when:

Workloads are bursty, seasonal, or growth is uncertain
You need the latest HBM-rich GPUs quickly without supply-chain wait
You lack ops bandwidth to manage firmware, NIC tuning, or temperature constraints

Practical hybrid approach

Many teams choose a hybrid: keep a baseline on-prem for predictable traffic and lease spot capacity (cloud or HaaS colocations) for bursts or experiments involving new GPU generations. This reduces risk from HBM scarcity and avoids overbuying NIC/switch capacity for rare peaks.

Concrete example: Sizing for a 100M-vector 1536-d index

Run this simple calculation when estimating working set size and memory needs. This uses 4-byte floats for embeddings.

# Simple memory estimate (Python)
vectors = 100_000_000
dims = 1536
bytes_per_float = 4
raw_bytes = vectors * dims * bytes_per_float
print(f"Raw storage: {raw_bytes/1e9:.1f} GB")
# Output: Raw storage: 614.4 GB

Raw device memory ~614 GB (float32). With compression (IVF+PQ), you might store vectors at 8-32 bytes each, making the hot working set much smaller. For GPU-resident indices, you still need headroom for temporary buffers, model activations, and re-ranking — factor 1.2–1.5x.

Recommended hardware for this example

On-prem: 4–8 HBM-rich GPUs per node if you want full GPU residency (account for 48–96 GB HBM per GPU depending on model). Consider NVIDIA for broad FAISS/GPU support.
Hybrid: Keep compressed PQ indices in DRAM (several TB across nodes) and lease HBM GPUs for nightly rebuilds and heavy-duty re-ranking during peak loads.

Operational checklist before you sign any vendor contract

Run a full-stack benchmark: index build, cold query, warm query, tail 99.9% latency.
Validate NIC offloads and RDMA/RoCE behavior under realistic congestion patterns.
Verify driver/firmware lifecycles — who patches and how are hotfixes delivered?
Confirm HBM and DRAM supply lead times and pricing volatility clauses in procurement contracts.
Check software compatibility matrix: CUDA/ROCm versions, FAISS/ANN builds, and orchestration tooling (Kubernetes device plugins, operator support).

Future predictions (2026–2028): what to watch

Several market signals will influence which vendors “win” for vector search:

HBM production scale-up: If HBM capacity expands quickly (Samsung, SK Hynix, Micron capacity), HBM premiums will fall and pure-GPU approaches will become cheaper.
Open tooling wins share: If ROCm/oneAPI ecosystems continue to mature, AMD’s price-performance edge will attract large similarity search deployments looking to reduce OpEx.
Network consolidation and smart NICs: Broadcom and competing DPU vendors will push programmability into the network. Teams that adopt DPUs for telemetry and offload will improve tail latency but risk deeper vendor coupling.
Deeper cloud–on-prem hybrids: More providers will offer colocation + managed stacks tuned for vector search, making leasing a permanent part of most architectures.

Actionable takeaways — a short checklist you can use this week

Estimate your hot working set: Use vector count × dims × element size and model compression savings. If hot set fits on a single GPU node, prioritize HBM GPUs; otherwise design for tiering.
Benchmark on target NICs: Test NICs and switches with your actual workload; don’t rely on vendor numbers.
Model procurement scenarios: Run a 3-year TCO comparing CAPEX (procure) vs OPEX (lease) with realistic utilization curves.
Lock in software compatibility: Validate CUDA/ROCm and FAISS/ANN versions early to avoid rework after purchase.
Consider hybrid operations: Baseline on-prem for steady traffic and lease cutting-edge GPUs for bursts or re-indexing.

Closing: A pragmatic vendor playbook for similarity search teams

In 2026, Broadcom, NVIDIA, and AMD each shape different parts of the vector search stack. NVIDIA offers the easiest path to low tail latency because of its HBM-rich GPUs and mature ecosystem; AMD is the cost-effective alternative if you can tolerate more engineering around toolchains; Broadcom shapes the network reality — your NIC and switch choices will define achievable cross-node patterns.

Procure when your workload is steady and you can negotiate warranty and supply terms; lease when you need elasticity or access to scarce HBM GPUs. Prefer a hybrid approach for most production similarity search systems: it minimizes risk, preserves agility, and lets you exploit vendor strengths without overcommitting to one ecosystem.

Call to action

Want a hands-on checklist tuned to your workload? Download our vector-search hardware sizing workbook or schedule a 30-minute vendor-fit review with our engineering team to map your QPS, recall targets, and budget to a specific procurement vs leasing recommendation.

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Senior editor and content strategist. Writing about technology, design, and the future of digital media. Follow along for deep dives into the industry's moving parts.