Vendor Evaluation Checklist: Choosing Chip and Networking Partners for AI Infrastructure

Vendor Evaluation Checklist: Choosing Chip and Networking Partners for AI Infrastructure

Hook: If you run AI infrastructure, you’re facing three converging headaches in 2026: exploding model demands, memory shortages that raise costs, and vendor ecosystems that lock you into software and hardware paths. This checklist helps procurement and engineering teams evaluate Broadcom, NVIDIA, AMD, and Intel across technical, commercial, and operational dimensions so your AI platform stays performant, affordable, and resilient for the next 5+ years.

Quick summary — What you’ll get

• Actionable procurement and technical checklist for chips and networking
• Vendor-specific strengths and risk indicators for Broadcom, NVIDIA, AMD, and Intel
• Scoring matrix, benchmark commands, and integration checks you can run today
• 2026 trends and predictions to align roadmaps for long-term resilience

Top takeaways (read first)

• Prioritize software portability — software lock-in creates long-term cost and migration risk as models evolve.
• Evaluate networking early — interconnects (Ethernet, InfiniBand, RoCE) determine usable scaling more than raw GPU TFLOPS.
• Validate roadmaps and supply — vendor financial health, M&A cadence, and memory supply constraints (CES 2026 evidence) matter.
• Score across technical, commercial, and operational buckets — use a weighted decision matrix to remove bias.

AI hardware and networking landscape in 2026 — short context

Late 2025 and early 2026 reinforced two realities: AI workloads are outgrowing single-system capacity, and memory and specialized silicon remain constrained. At CES 2026 industry reporting showed memory price pressure driven by AI chip demand, which raises procurement and refresh costs for whole fleets. Meanwhile, strategic vendor consolidation continued — Broadcom has become a dominant player across switch and ASIC markets (its market capitalization exceeded $1.6T by late 2025), and NVIDIA retains leadership in accelerators and software ecosystems.

“Memory scarcity and concentrated supply chains mean procurement must evaluate total lifecycle cost, not just SKU price.”

How to use this checklist

This is a combined procurement + technical checklist. Use it in three phases:

1. Discovery — gather vendor documentation, roadmaps, SLAs, and benchmark data.
2. Validation — run the technical tests and contract simulations below.
3. Decision — score vendors in a weighted matrix, negotiate SLAs/terms, and require integration proofs.

Checklist — Strategic & commercial criteria (must-haves)

1. Financial and strategic stability

• Public financial health — revenue trends, net debt, and capital allocation. Example: Broadcom’s >$1.6T market cap signals scale but also aggressive M&A-driven roadmap shifts.
• M&A behavior and product continuity guarantees — require contractual commitments for multi-year firmware/driver support after acquisitions.
• Supply chain assurances — minimum allocation commitments, lead times for bulk orders, and escalation contacts for shortages.

2. Contract terms and pricing models

• Volume discounts and price collars for 12–60 month windows
• Support SLAs: RMA turnaround, spare parts locality, and critical firmware patch timelines
• IP licensing clarity — e.g., CUDA/licensed libraries, ROCm, oneAPI terms; ask for written portability assistance

3. Roadmap transparency

• Ask for a 3–5 year public roadmap and a private roadmap under NDA; align vendor roadmap ticks with your upgrade cycles
• Validate deprecation windows and EOL processes

Checklist — Technical & integration criteria (must-validate)

4. Performance and benchmark validation

Don’t rely on vendor marketing numbers. Require benchmark artifacts from your representative workloads (LLM inference, training steps, data-prep). Key metrics:

• Throughput (tokens/sec), latency (p99), and memory utilization under representative batch sizes
• Interconnect performance across hosts (RDMA latency, bisection bandwidth)
• Power draw at workload peaks and efficiency (perf-per-watt)

Sample benchmark commands to request or run:

# NVIDIA GPU health and utilization
nvidia-smi --query-gpu=index,name,memory.total,memory.used,utilization.gpu,temperature.gpu --format=csv

# RDMA/InfiniBand quick latency test (requires libibverbs)
ibv_rc_pingpong -S -d mlx5_0

# TCP bandwidth test
iperf3 -c  -P 8

5. Memory architecture and bandwidth

• Validate GPU HBM capacity and effective bandwidth for your model sizes
• Ask for memory-overcommit strategies and OOM recovery behavior
• Confirm ECC/RAID/refresh behaviors and impacts on throughput — and consider cache and on-device policies when designing memory tiers.

6. Interconnect and networking

High-performance networking is as strategic as the accelerator. Evaluate:

• Fabric choices: InfiniBand vs RoCE vs Ethernet — test p99 latencies and packet drop under load
• Switch vendor features: RDMA offload, congestion control, segmentation offload, telemetry (sFlow/sFlow-RT), and OAM
• Compatibility matrix: vendor NIC firmware + OS + hypervisor + container runtimes

7. Software ecosystem and portability

• Runtime and libs: CUDA/cuDNN/Triton vs ROCm/MIOpen vs oneAPI. Verify reproducible performance across versions.
• Container support: vendor downstream images and best-practice orchestration (Kubernetes device plugins, DCGM, Prometheus exporters)
• Model tooling: ONNX support, quantization toolchains, compiler stability

8. Observability and operational tooling

• Logging, telemetry, and flamegraphs for GPU kernels, NICs, and switch fabric
• Support for fleet management (firmware rollout, driver updates, alerting)
• API access for capacity planning and billing integration

9. Security and compliance

• Secure boot, signed firmware, and chain-of-trust attestations
• Supply chain provenance — ability to audit silicon origin
• Vulnerability disclosure program and patch cadence

Vendor-specific evaluation notes

Below are concise evaluation points for each major vendor. Use them as prompts for vendor meetings and line items in RFPs.

Broadcom (networking, ASICs, switches)

• Strengths: Market-leading switch silicon, deep OEM ecosystem, strong switch telemetry, and RDMA/InfiniBand products (Mellanox lineage).
• Risks: Broad M&A strategy and software-focused acquisitions (post-2023) mean product focus can shift—require roadmap commitments in contract.
• Checklist items: Validate switch ASIC family compatibility with your NICs, ask for large-customer allocation guarantees, and lock in telemetry/CLI API versions for fleet automation.

NVIDIA (accelerators, ecosystem)

• Strengths: Leader in accelerator performance and most mature AI software stack (CUDA, cuDNN, Triton, NCCL), excellent partner ecosystem for model tooling.
• Risks: Software-driven lock-in and premium pricing; memory scarcity can inflate GPU purchase costs; licensing and driver compatibility must be evaluated.
• Checklist items: Require multi-version driver compatibility matrix, performance baselines for your models, and written commitments for firmware/driver support windows.

AMD (accelerators & CPUs)

• Strengths: Competitive price/perf in GPUs and EPYC CPUs with strong memory-channel architectures; increasing software maturity via ROCm and open-source tooling.
• Risks: Ecosystem integration gaps relative to NVIDIA, and you must validate ROCm compatibility for your frameworks.
• Checklist items: Run ROCm-based training and inference workflows, test driver/firmware updates in staging, and confirm memory-channel/topology advantages for your data sizes.

Intel (CPUs, accelerators, stack bridges)

• Strengths: Broad CPU portfolio (Xeon), investments in accelerators and software portability layers (oneAPI), and enterprise-grade lifecycle services.
• Risks: Accelerator offerings have matured more slowly compared to incumbents; confirm that software stack compatibility meets your needs.
• Checklist items: Validate oneAPI support for your frameworks, test mixed CPU+accelerator workflows, and require clear EOL and driver support timelines.

Operational tests and validation scripts

Below are short, executable checks to include in your validation environment or ask vendors to run and submit results for.

1) End-to-end model inference test

Run a representative model (e.g., your in-house LLM or a 7B open model) with the vendor-supplied runtime and collect:

• tokens/sec, p95/p99 latency, GPU memory utilization
• CPU ready/wait times and NIC interrupts
• power draw and thermal headroom

2) Fabric stress test

# Run within controlled cluster to measure RDMA latencies (InfiniBand/RoCE)
# On two nodes with RDMA setup:
ib_write_bw -F -d mlx5_0  # bandwidth test
ib_write_lat -d mlx5_0  # latency test

# For Ethernet/RoCE check packet loss and congestion behavior
iperf3 -c  -t 60 -P 16

Include the RDMA latency checks (for example ibv_rc_pingpong) and push results into your observability pipeline; see observability patterns for ingestion best practices.

3) Firmware and driver upgrade rehearsal

• Simulate rolling driver/firmware update in staging; measure downtime, driver incompatibilities, and need for kernel reboots. Reference a patch orchestration runbook when designing your rehearsal.
• Validate rollback process and runbook durations.

Decision framework — scoring matrix (example)

Use a weighted scoring matrix to quantify choices. Example weights (tweak for your priorities):

• Technical fit: 35%
• Network & scaling: 20%
• Commercial terms & supply: 15%
• Roadmap & longevity: 15%
• Operational support & security: 15%

Sample scoring table formula you can paste into a spreadsheet:

# Columns: Vendor, Technical (0-10), Network (0-10), Commercial (0-10), Roadmap (0-10), Ops (0-10)
# Weights (in percent): 35,20,15,15,15
# Weighted score formula for row B2..F2:
= (B2*0.35 + C2*0.20 + D2*0.15 + E2*0.15 + F2*0.15)

For guidance on how to convert benchmark artifacts into structured scoring and dashboards, see our analytics playbook for data-informed departments.

Case study (short)

A global SaaS provider in late 2025 needed to scale inference across three regions with predictable cost. They ran vendor pilots with NVIDIA and AMD accelerators and Broadcom switches. Key outcomes:

• Broadcom switches delivered predictable RDMA behavior and telemetry that reduced packet drops by 80% after tuning congestion control.
• NVIDIA delivered highest raw throughput for sparse transformers, but AMD offered 18% lower total cost-of-ownership for certain dense workloads.
• By requiring driver/firmware SLAs and a 36-month allocation agreement, procurement reduced refresh-related surprises during 2025 memory squeezes.

2026 trends & future predictions — plan for them

• Composable and disaggregated infrastructure will accelerate: network-attached accelerators and memory pooling will move from R&D to production; verify vendor support for composability APIs and operational patterns like those in micro-edge VPS playbooks.
• Software portability frameworks mature: expect better oneAPI/ONNX/runtime parity by late 2026 — maintain multi-backend CI to avoid lock-in and lean on cloud-native orchestration patterns.
• Supply volatility persists: memory price pressure reported at CES 2026 means you should negotiate price collars and multi-quarter allocation guarantees.
• Networking will be the differentiator: smart NICs, telemetry, and congestion-aware fabrics will determine how you scale LLMs across thousands of accelerators.

Red flags to watch for in vendor responses

• Vague roadmap answers or reluctance to provide EOL timelines under NDA.
• No ability to sign allocation or price-stability clauses.
• Unwillingness to provide performance artifacts run with your representative workloads.
• Limited telemetry or closed APIs that block fleet automation — insist on right-to-audit telemetry and open ingestion hooks.

Practical negotiation asks to include in RFP

• Three-year driver/firmware support commitment with a quarterly patch SLA
• Allocation guarantee for peak ordering windows and an emergency supply lane
• Right-to-audit telemetry and access to low-level switch/NIC metrics for 3rd-party tooling
• Credits or price protection if memory-driven price increases exceed a contract threshold

Final checklist — what to sign off before purchase

1. Benchmarks validated on your workloads and reproducible in staging
2. Signed SLA covering driver/firmware/parts and allocation guarantees
3. Interoperability tests (NIC ↔ switch ↔ OS ↔ hypervisor/container runtime) passed
4. Security attestations and supply chain provenance reviewed
5. Weighted decision matrix score documented and approved by procurement and engineering

Closing recommendations

In 2026 procurement must act like engineering: require reproducible technical artifacts, insist on roadmap visibility, and negotiate contractual protections for supply and software lifecycle. Evaluate Broadcom for networking and fabric stability, NVIDIA for top-tier accelerator performance and ecosystem, AMD when you need cost-effective density and open stacks, and Intel for CPU+accelerator balance and enterprise lifecycle services. Mix-and-match vendors purposefully — the right combination often outperforms a single-vendor lock-in.

Actionable next steps (do this this week)

1. Run the three validation commands in your staging cluster and collect results into the scoring spreadsheet above — instrument results with the observability patterns described in observability for edge AI agents.
2. Send a short RFP addendum asking each vendor to confirm: firmware support windows, allocation guarantees, and performance artifacts for your top-3 models.
3. Set a procurement-engineering review to apply the weighted scoring matrix and pick a primary + fallback vendor strategy. Use the analytics playbook to operationalize scoring.

Call to action

Need a ready-to-run RFP template and scoring spreadsheet customized for your workloads? Contact our infrastructure strategy team to get a vendor-proof RFP and a 2-day pilot plan that validates Broadcom, NVIDIA, AMD, and Intel options in your environment.

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