benchmarkperformanceAI infrastructure

Benchmark Plan: Comparing RISC-V + NVLink vs x86 GPU Servers for LLM Inference

ttunder

2026-01-30

9 min read

Design a reproducible benchmark to compare RISC‑V NVLink vs x86 GPU servers for LLM inference—throughput, latency, power, and cost‑per‑token.

Hook: Why your procurement and SRE teams should run this benchmark now

Rising cloud costs, unpredictable inference latency, and fragmented hardware stacks are slowing feature velocity and inflating ML ops budgets. In 2026 the conversation shifted: SiFive announced integration with Nvidia's NVLink Fusion, making RISC‑V hosts first‑class citizens for GPU‑accelerated inference. That change creates a practical choice for platform teams—do you standardize on tried‑and‑true x86 GPU servers, or evaluate a lower‑power RISC‑V + NVLink design for LLM inference?

Executive summary — what this benchmark plan delivers

This article gives you a reproducible, production‑grade benchmark suite and methodology to compare throughput, tail and median latency, power efficiency, and cost‑per‑token between SiFive NVLink‑enabled RISC‑V platforms and traditional x86 GPU servers. It includes:

Workload selection and model profiles (small to very large LLMs)
Hardware and software configuration checklist
Measurement methods for latency, throughput, and power (wall and component level)
Cost model to compute amortized cost‑per‑token
Reproducible test scripts, statistical best practices, and tuning guidance

The 2026 context: why this matters now

Two industry shifts make this benchmark timely:

Heterogeneous host choices: SiFive's announcement that it will integrate NVLink Fusion with RISC‑V IP signals that the host CPU is no longer always x86 for GPU‑centric inference designs. (See the 2026 SiFive/Nvidia NVLink coverage in industry press.)
Memory and interconnect evolution: NVLink Fusion, plus broader adoption of CXL and disaggregated memory fabrics, is allowing larger effective context windows and reduced PCIe host‑CPU overhead—shifts that can change per‑token economics.

What to expect from the results

RISC‑V hosts can reduce host power and potentially lower cost when they deliver equivalent IO and driver maturity. x86 platforms still offer deep software ecosystem and AVX‑accelerated preprocessing. Your real decision will depend on model size, quantization level, multi‑GPU sharding strategy, and your power/cost constraints.

Benchmark design principles

Design benchmarks for procurement decisions, not micro‑bench bragging. Focus on:

Representativeness: Use models and sequence lengths that mirror your production load.
Repeatability: Pin threads, fix firmware/driver versions, and publish configs.
Actionability: Produce metrics you can plug into TCO models—tokens/sec, p50/p90/p99 latency, watts/token, cost/token.
Comparability: Use identical quantization, batching, temperature, and prompt sets across platforms.

Benchmark suite: workloads and models

Pick a tiered set of LLMs to span common production cases. For each, run inference across the same tokenization and prompt sets.

Small‑footprint (7B‑13B, single‑GPU): latency‑sensitive assistants, high QPS microservices.
Mid‑range (30B‑70B, multi‑GPU possible): multitask assistants and retrieval‑augmented generation (RAG).
Large (100B+ or sharded 70B across GPUs): long‑context summarization, multi‑document ingest.

For each size, test the following inference modes:

Single‑stream low‑latency: one request at a time, critical for chat frontends.
Multi‑stream high‑throughput: many concurrent streams, microbatching allowed.
Large context window: long sequences (8k–128k tokens) to stress memory and NVLink or host memory access.
Quantized modes: FP16/FP8, INT8 or 4‑bit where supported—test identical quantization across platforms.

Hardware and software configuration checklist

Keep everything measurable and as similar as possible across comparisons.

Hardware: One RISC‑V NVLink‑enabled platform (SiFive SOC + same generation Nvidia GPUs) vs one x86 server with the same GPU family and NVLink connectivity where applicable. Match GPU counts, GPU DRAM, and NVLink topology where possible.
Firmware & drivers: fixed firmware, BIOS/UEFI, kernel, CUDA/nvidia drivers, and any NVLink Fusion runtime versions. Record exact versions.
OS & runtime: same Linux distro and kernel tuning: hugepages, transparent hugepage off/on, IRQ affinity, CPU governor set to performance.
ML stack: identical runtime (e.g., FasterTransformer, Triton, or a single open inference runtime like vLLM or text-generation-inference), identical model build artifacts and tokenizer versions.
Power & cooling: identical rack PUE assumptions, ambient temperature, and airflow configuration. Measure at the PDU for wall power for comparability.

Measurement methodology — latency and throughput

Follow a structured sequence for each test case:

Warm up: 5–15 minutes of steady traffic to prime caches and thermal profiles.
Latency sweep: Run single‑stream requests and record p50/p90/p99. Use fixed prompt set and measure cold vs warm start separately.
Throughput sweep: Increase concurrent streams and/or batch size incrementally until 95% GPU utilization or latency SLO breach. Record tokens/sec and GPU utilization.
Context scaling: Increase sequence length to evaluate memory paging, host fallback, or NVLink traffic increases.
Quantization modes: Repeat for FP16, FP8, INT8 or 4‑bit if supported, keeping accuracy checks to a small representative validation set.

Measurement tools and signals to collect

Application logs: per‑request timestamps, token counts, and model logits for correctness checks. Store and index telemetry efficiently (e.g., follow guidance from large telemetry stores and ClickHouse best practices).
GPU telemetry: nvidia‑smi, DCGM, and nvlink statistics for bandwidth utilization and error counters.
Host telemetry: top, perf, NUMA stats, and scheduler traces.
Network: if multi‑node, measure interconnect latency (RDMA stats) and NVLink/Fusion counters.
Power: PDUs for wall power (preferred), board sensors or IPMI for component breakdown when available—capture board sensors and PMIC telemetry and feed it into your telemetry backend (see ClickHouse patterns).

Power measurement: tokens per joule and watts breakdown

Power measurement is where architecture differences can be decisive. Use the following approach:

Measure idle power (system up, no inference) for baseline.
Measure steady state power during throughput runs (avg and peak watts).
Compute tokens/J = (tokens/sec) / (watts). Also compute watt·hour per 1M tokens for finance teams. If you care about minimizing memory and power for training and inference, see techniques in AI training pipelines that minimize memory footprint.
Attribute power where possible: GPU power vs host power. On x86 use RAPL for CPU; on RISC‑V use board sensors or onboard PMIC telemetry exposed by the vendor and ingest those signals into your telemetry backend.

Example power calculation

Suppose your throughput test yields 100K tokens/sec at 1200W system power. Tokens/J = 100,000 / 1200 = 83.33 tokens/J. If power costs $0.12/kWh, one million tokens cost:

Energy per 1M tokens = 1,000,000 / 83.33 = 12,000 J = 3.333 Wh
Cost per 1M tokens = 3.333 Wh * $0.12 / 1000 = $0.0004

Combine this with amortized HW costs for an accurate cost‑per‑token—details in the next section.

Cost model: computing cost‑per‑token

Use an amortized TCO model that includes hardware, power, datacenter overhead, and software costs. Components:

Hardware amortization: purchase_price / (3 years * 8760 hours)
Power: measured average watts * $/kWh * hours
Cooling & facility: PUE multiplier (e.g., 1.2)
Software & support: yearly license or support costs apportioned per token

Formula (per token):

cost_per_token = (amortized_hw_hourly + power_hourly * PUE + software_hourly) / tokens_per_hour

Where tokens_per_hour = tokens/sec * 3600. Build a spreadsheet where you can plug in measured tokens/sec and measured watts for each platform; for broader economic context see analysis of edge economics and micro‑region hosting.

Statistical rigor and reproducibility

To avoid chasing noise:

Run each test case at least five times; use the median throughput and median latency to report results.
Compute 95% confidence intervals for latency percentiles using bootstrapping.
Log full environment: kernel, driver, firmware, BIOS, nvlink topology, GPU SM versions, and all runtime flags.
Publish anonymized prompt sets and seed values so others can replicate your results. For policy and secure disclosure patterns, refer to guidance like creating secure desktop AI agent policies so you safely share examples.

Advanced diagnostics — what to inspect when results diverge

If RISC‑V shows lower power but also lower throughput, investigate:

NVLink utilization — are links saturated or underutilized?
CPU dispatch overhead — measure syscall rates, thread wakeups, and copy volumes between host and GPU.
Memory fallbacks — is the runtime staging memory on host due to insufficient GPU DRAM?

Use flamegraphs, eBPF, and DCGM traces to drill down. If x86 shows superior tail latency, check kernel scheduler behavior and packet batching in your RPC layer.

Practical tuning knobs to iterate

Before concluding a procurement decision, iterate on these parameters:

Batch size and batching latency window
Number of concurrent model instances per GPU
Quantization and its impact on accuracy vs throughput
CPU affinity and NUMA placement
NVLink topology (peer‑to‑peer vs switch) and PCIe root complex layout

Reproducible example experiment (commands and pseudo‑script)

Below is a condensed reproducible workflow you can adapt into CI or a benchmarking repo. Replace placeholders with concrete toolchain commands.

Provision identical GPUs and set driver versions.
Deploy runtime: git clone tunder/llm-bench (example). Build container with fixed CUDA and runtime.
Prepare model artifacts and tokenizer; record sha256 of weights.
Warm up: run ./bench --mode warmup --duration 600
Latency test: ./bench --mode latency --streams 1 --prompts ./prompts.json --out latency.json
Throughput sweep: for s in 1 2 4 8 16 32; do ./bench --mode throughput --streams $s --duration 600 --out throughput_$s.json; done
Power capture: start PDU sampling, then run throughput. Pair timestamps for alignment.
Collect GPU traces: nvidia-smi dmon, dcgmi stats, nvlink counters.

Interpreting results: what tradeoffs matter

Use the following rubric when you analyze results:

Latency‑critical services: prefer platform with lower p99 at your target QPS even if cost/token is higher.
Throughput‑driven batch jobs: prefer higher tokens/sec and better tokens/J for batch inference.
Context heavy workloads: prefer platform that supports larger effective context windows without heavy swaps to host memory.
Operational simplicity: x86 may win for ecosystem maturity; RISC‑V may win on power and TCO in large fleets as drivers and runtimes mature.

2026 trends and future predictions

Based on late‑2025 and early‑2026 industry signals, expect these developments:

RISC‑V adoption rises in edge and specialized inference racks as vendors integrate NVLink and runtimes adapt. See approaches to deploying offline‑first field apps on free edge nodes and what that implies for fleet economics.
NVLink Fusion will drive tighter host‑GPU coherence, lowering host overhead for large context windows and enabling new disaggregated topologies.
CXL and memory pooling will further blur the lines—platforms will compete based on software maturity and ecosystem tools, not raw TPU/GPU flops alone. Explore how edge personalization and memory pooling are reshaping local platforms in edge personalization case studies.
Quantization and sparsity advances will compress memory footprints, shifting bottlenecks to interconnects and host dispatch pipelines.

"Benchmark decisions in 2026 will be as much about ecosystem maturity and operational cost as about peak FLOPS."

Example decision flow for platform selection

Run the benchmark suite on representative models and traffic.
Compute tokens/sec, p99 latency, tokens/J, and cost_per_token using the provided model.
Map results to your SLOs (latency, budget). Apply sensitivity analysis for energy prices and amortization windows.
Choose the platform that meets SLOs at lowest expected TCO; add a pilot in production traffic to validate long‑tail issues.

Checklist: what to publish with your results

Hardware specs and serials, driver and firmware versions
Model checkpoints and tokenizer SHAs
Full test scripts and CLI flags
Raw telemetry — latency histograms, GPU traces, PDU logs (ingest with best practices from ClickHouse telemetry guides)
Cost model spreadsheet with assumptions

Closing: actionable next steps

Run this benchmark plan on a small pilot fleet. Start with a representative 7B and 70B workload, measure tokens/J and cost_per_token, and iterate on batching and quantization. Publish the full report and feed it into procurement and SRE decision gates.

Need help operationalizing this? We at tunder.cloud can run the suite on your behalf (or provide the full reproducible repo and CI pipelines). Book a technical pilot to validate RISC‑V NVLink platforms vs your x86 fleet and get a tailored TCO analysis within two weeks.

Call to action

Download the reproducible benchmark repo, request a free pilot, or contact our benchmarking team to run a custom comparison with your models and pricing. Make procurement decisions backed by data—don’t gamble on brochures.

tunder

Contributor

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.