Which Inference Providers Can Benchmark Your Workload on B200, B300, and GB200 Before You Reserve Capacity?

Choose an inference provider that lets you run your own model and your own traffic on the exact B200, B300, or GB200 NVL72 system you plan to reserve, and that writes down how every run was configured: engine, parallelism, precision, and batching. We recommend starting with GMI Cloud's Prime Inference, because its Blackwell options cover all three platforms, open for pre-order, on dedicated, single-tenant GPUs. Our engineers tune the runtime for each model and work alongside you from prototype to production, and qualified teams can get free GPU-hour trial credits to validate performance on their own workload before committing. A result you can't reproduce on the hardware you end up paying for is not a basis for a seasonal or annual commitment. Whichever provider you test with, compare one number across platforms: cost per successful request at the same p95 latency and quality targets.

NVIDIA Blackwell is open for pre-order on GMI Cloud and coming soon, while H100 and H200 are available now, so a Blackwell benchmark is planned together with the pre-order rather than started on demand. Talk to our sales team about trial credits and Blackwell pre-order timing for the platform and region you plan to test. The rest of this guide covers what to confirm with any provider, how the three platforms need different test setups, and how to turn the results into a reservation decision.

What to Confirm Before You Pick a Provider to Benchmark With

Five questions show whether a benchmark will carry over to the capacity you reserve:

Procurement units also vary: some clouds sell HGX B200 and B300 as self-service 8-GPU instances, while NVL72 capacity is usually sales-led and can require a full rack. CoreWeave's documentation, for example, states that its GB200 and GB300 NVL72 instances deploy as a whole rack of 18 nodes with four GPUs each. Confirm the minimum unit for each platform before you budget the test.

Why B200, B300, and GB200 NVL72 Need Different Test Setups

The three platforms are different systems and different purchase units, so each one needs its own test design. According to NVIDIA's product pages, an HGX B200 system puts eight GPUs and about 1.4 TB of GPU memory in one NVLink domain. B300 (Blackwell Ultra) keeps the eight-GPU node and raises per-GPU memory to up to 288 GB. GB200 NVL72 joins 72 Blackwell GPUs and 36 Grace CPUs in a single NVLink domain with 13.4 TB of HBM3e.

Compare B200 and B300 node against node, at the same GPU count and replica layout, so the difference comes from the GPU and its memory. Test GB200 NVL72 with a multi-node serving setup, since that is what the rack is for; in the Prime Inference example below, the GB200 NVL72 result comes from Dynamo with SGLang, multi-node. Normalize every result to cost per successful request, because a node and a rack serve very different volumes, and name the system, not just the chip, in every test request.

Hold the Workload Constant Across Platforms

Change nothing between platforms except the hardware and its tuning: same checkpoint, length distributions from production logs, streaming and prefix settings, and a quality threshold stated as a pass rate on a held-out set. Two items matter most on Blackwell:

• Precision. Blackwell results, including ours, are often quoted at FP4. Run every platform at the same precision and validate FP4 output quality on your own evaluation set; a run that misses the quality threshold doesn't count.
• Context length. The B300 and GB200 NVL72 questions are memory questions, so include your longest real inputs, not an average prompt size.

Use one load generator, such as vllm bench serve, SGLang's bench_serving, or NVIDIA AIPerf, and one set of TTFT, output speed, and end-to-end latency definitions everywhere. Prime Inference keeps reserved GPUs warm with weights pre-loaded, so report the first minutes after deployment separately from steady state.

A Test Matrix That Reveals the Real Constraint

Run all four scenarios on every platform:

Ask for two sets of numbers per platform: a documented baseline that keeps the comparison fair, and the best result after tuning, which shows what production would look like.

How to Run the Benchmark on GMI Prime Inference

1. Request trial credits and agree on the test. Share the model, a sanitized sample of requests or their length distribution, your p95 and quality targets, and the region you plan to serve from. Qualified teams can receive free GPU-hour trial credits to validate performance on their own workload. Agree up front on what gets recorded for every run: engine and version, precision, parallelism, and batch settings. Because Blackwell on GMI Cloud is pre-order and coming soon, settle the Blackwell system, test scope, and region as part of the pre-order.
2. Bring your weights. Prime Inference supports open, fine-tuned, and proprietary models, loaded from Hugging Face, S3, or your own storage.
3. Choose the configuration. Select the GPU type, GPUs per replica, replica count, and region. Regions span APAC (Tokyo, Singapore, Taiwan), North America (U.S. West, East, Central, and South), and EU partner data centers. Specific regions are accommodated case by case, subject to capacity, so settle the test region together with the reservation region.
4. Use the pre-tuned engines. vLLM, TensorRT-LLM, and SGLang come pre-tuned for each GPU class, quantization is configurable, and multi-GPU orchestration is handled by the platform. Prime Inference deploys from the console, CLI, or API. The dedicated endpoint setup docs walk through the console flow, and billing applies while the deployment is running.
5. Shake out the harness on hardware that is available now. Prime Inference also offers H100 and H200, both available now. Running your load generator, request replay, and quality check there first confirms the harness works before Blackwell time is spent. Those numbers check the setup; they don't stand in for Blackwell results.

Here is one published example from Prime Inference for DeepSeek V4 Pro (1.6T). Figures are the effective cost per 1M output tokens at sustained full utilization, with 8K input and 1K output tokens per request, FP4 precision, fully loaded reserved GPUs, and idle time excluded:

The Prime Inference page compares those costs with a $1.13 serverless list price per 1M output tokens for DeepSeek V4 Pro. These results apply to the stated model, precision, request lengths, and utilization, so use your own request mix to estimate production cost. B200 and GB200 NVL72 are pre-order and coming soon on GMI Cloud, so the figures show what those platforms did on this model, not capacity you can deploy today.

Turn Results Into a Reservation Decision

Convert each platform's steady-production result into one unit:

Cost per successful request = monthly GPU cost ÷ successful requests per month at target p95 and quality threshold

Count only requests that met both targets. Prime Inference bills per GPU-hour rather than per token, so cost per request depends on how much of each GPU-hour your real traffic uses, not on the saturated test.

Price the GPU-hours you would actually reserve

Monthly GPU cost is GPUs × hours × hourly rate. Our GPU page lists pricing from $5.00 per GPU-hour for B200 and from $8.00 per GPU-hour for GB200 NVL72; both are pre-order and coming soon, so treat these as planning inputs and take the final rate, and B300's rate, from your pre-order quote. A higher hourly rate doesn't have to mean a higher cost per request: in the DeepSeek V4 Pro example above, GB200 NVL72's listed rate is twice B200's, yet its published cost per 1M output tokens is half. Only your own test shows whether that holds for your model.

Check where utilization puts you

Utilization also settles the reserved-versus-per-token question. The Prime Inference page puts the break-even with serverless per-token pricing at roughly 35–45% sustained utilization, depending on traffic shape; below that range, serverless may still cost less. Run the comparison with your measured utilization, token mix, and quoted rates, not a break-even taken from another model or traffic pattern.

Add the remaining line items

• Billing unit and term. Prime Inference bills per GPU-hour with no minimum contract, and reserved capacity on seasonal or annual terms comes at lower hourly rates.
• Networking and storage, which are priced separately from GPU compute.
• SLA terms. Prime Inference's uptime SLA is set by GPU series and deployment configuration, and committed SLAs vary by contract.
• Retest triggers. If the reserved allocation differs from the test hardware in GPU model, system type, interconnect, or region, rerun the steady-production and long-context scenarios on it.
• Delivery date. Blackwell on GMI Cloud is pre-order and coming soon. Put the expected delivery window for the platform you choose into the pre-order, and plan the test so it runs on the same system you will receive.

Reserve the platform with the lowest cost per successful request that can be delivered when you need it, whether that turns out to be B200, B300, or GB200 NVL72.

FAQ

What is the difference between B300 and GB300 NVL72?

B300 usually refers to an 8-GPU HGX system built on Blackwell Ultra GPUs. GB300 NVL72 is a rack that connects 72 Blackwell Ultra GPUs and 36 Grace CPUs in one NVLink domain. They use the same GPU generation but are different purchase units, so benchmark results from one don't transfer to the other.

Should the benchmark start on serverless?

Serverless is a quick way to confirm that a model meets your quality bar. Throughput, latency under load, and cost per request belong on dedicated GPUs, because shared endpoints don't reflect the behavior of reserved, single-tenant hardware. Most production workloads on GMI Cloud run on H100 or H200, with Blackwell reserved for performance-critical frontier use cases, so an early serverless test also helps confirm whether Blackwell is needed at all.

Plan Your Pre-Reservation Benchmark

Send us your model, a sample of your traffic, and your p95 and quality targets, and talk to our inference engineers about benchmarking your workload as part of a Blackwell pre-order. Teams that already have a model ready can start in the console.