Who Can Run Low-Latency B200 or B300 Inference on Dedicated GPUs and Tune It for Your Model?

Choose a provider that will serve your own weights on single-tenant B200 or B300 GPUs, tune the inference engine for that specific model, and show measured results against latency numbers you have written down in advance. GMI Cloud Prime Inference is built for dedicated, tuned serving: single-tenant GPUs, runtimes tuned to open-source or your own model, per-model kernel, scheduling, and routing optimization, configurable quantization, weights kept warm, and endpoints pinned to the region closest to your users. B200 and B300 are in pre-order on GMI Cloud, so this is capacity you reserve now; H200 is available today for your latency baseline. Our engineering team works with you from prototype to production.

While Blackwell capacity is scheduled, H200 is the right place to build that baseline and your quality gate. What turns "tuned for your model" from a sales phrase into something you can check is three things: a written latency spec, a clear reason for B200 or B300, and a tuning plan with evidence attached to each item.

Start with a latency spec a provider can be held to

"Low latency" is not a target anyone can tune toward. A target with percentiles, load, and request shape is. Write the spec before the first provider conversation, because every tuning decision that follows is a trade between these fields.

A usable spec reads like a single sentence: p99 TTFT under X ms and p99 ITL under Y ms at N concurrent streams, measured at the client in a named region, for the prompt and completion lengths in the attached sample, with eval scores no lower than Z.

B200 or B300: the latency trade-off

On a single node, latency is shaped mostly by two things: how many GPUs each model replica spans, known as the tensor parallel (TP) width, and how much memory is left for KV cache once the weights are loaded. B200 and B300 differ on both.

NVIDIA's HGX specifications list 2.1 TB of GPU memory per 8-GPU HGX B300 board against 1.4 TB for HGX B200. The two share the same fifth-generation NVLink bandwidth and the same listed FP8 throughput. HGX B300 lists about 1.5x the dense FP4 throughput and 2x the attention performance of the original Blackwell (B200), per NVIDIA. On our Prime Inference page, B300 is described as offering the maximum memory per GPU, for trillion-parameter models, ultra-long context, and the highest-throughput FP4 inference, when B200 isn't enough.

That gives a practical rule:

• Choose B200 when the weights plus the KV cache for your concurrency floor fit on one 8-GPU node with headroom, at a TP width that meets your ITL target. Leftover GPUs become extra replicas, which spreads streams and helps p99 under load.
• Choose B300 when B200 only fits your model at full-node TP with little room left for KV cache, when your context lengths push KV cache past what B200 leaves, or when the alternative is splitting a replica across nodes. The extra memory lets the same model run at a narrower TP width or with more concurrent sequences per replica. The higher attention throughput helps TTFT on long prompts.
• Stay on B200 when an FP8 model already fits comfortably there. The listed FP8 throughput and NVLink bandwidth are the same on both, so the case for B300 has to come from measured latency on your model, not from the spec sheet.

TP width works in both directions. A wider replica splits each token's math across more GPUs but adds communication at every layer. The right width for your model is something the provider measures at your concurrency floor, not something to assume.

What "tuned for your model" should include

Tuning is a set of decisions, and each one should come with evidence you can check against your spec. This is the checklist to agree on before reserving capacity.

Two of these deserve a closer look.

Speculative decoding can cut ITL sharply for some models and traffic, and barely help for others. vLLM, SGLang, and TensorRT-LLM all document speculative decoding, but the gain depends on how often the draft tokens are accepted, and it usually shrinks as batches grow. Ask for results at both your concurrency floor and your peak, and for a quality check on the same eval set.

The concurrency cap is where latency and cost meet. Letting more sequences share a replica raises throughput and lowers cost per token, but it pushes p99 ITL up. The provider should show you the curve and the point they picked, so the trade is visible rather than buried in a config file.

Ask for a short report at the end of tuning: the engine and version, precision, TP width, replica count, speculative settings, concurrency cap, and measured TTFT and ITL percentiles against your spec. That report is also your baseline for every later model update.

How Prime Inference runs dedicated B200 and B300 endpoints

Prime Inference is our dedicated inference service on reserved NVIDIA GPUs. Here is what it provides for a latency-sensitive custom model:

• Your weights on single-tenant GPUs. Deploy any open-source, fine-tuned, or proprietary weights, loaded from Hugging Face, S3, or your own storage. GPUs are reserved only for your workload, so there are no noisy neighbors and no contention under load. Dedicated endpoints run on a private network, separate from other tenants.
• Engines tuned per GPU class and per model. vLLM, TensorRT-LLM, and SGLang are pre-tuned per GPU class, and we add per-model kernel, scheduling, and routing optimization rather than a generic stack. Custom weights load on the same inference stack without re-engineering your serving layer. Quantization is configurable, and multi-GPU orchestration is handled.
• Warm by default. Reserved GPUs stay warm with weights pre-loaded, so requests don't wait on a cold start or a model load. Steady first-token latency under your traffic is still something to measure against your spec.
• Your choice of layout. On GPUs available now, such as H200, you choose the GPU type, GPUs per replica, replica count, and target region, then monitor latency and throughput as the endpoint runs. The dedicated endpoint documentation walks through deployment, scaling settings, and endpoint status. B200 and B300 layouts are planned with our team as part of the pre-order.
• Placement close to your users. Endpoints can be region-pinned for first-token latency, or region-locked for data residency, across Asia-Pacific, North America, and Europe.

Because B200 and B300 are in pre-order, the region that will host your endpoint and the tuning items in scope beyond our per-model runtime work are planned with our engineering team. Ask for the measurement method and sign-off criteria for your latency spec to be written into the deployment scope. Uptime SLAs are determined by GPU series, deployment configuration, and contract.

What to do while B200 and B300 are in pre-order

The latency spec, eval set, and traffic sample do not depend on Blackwell, so there is no reason to wait for them.

1. Deploy your model on an H200 Prime endpoint. H200 is available now. Measure TTFT and ITL against your spec at your concurrency floor. This gives you a working quality gate, a real request-shape distribution, and an FP8 baseline. A native FP4 baseline for B200 or B300 has to be measured on that Blackwell hardware itself.
2. Find where the model gets tight. Note the TP width the model needs on H200 and how much KV cache headroom remains at your concurrency floor. That tells you early whether B200 is likely to be comfortable or whether B300's memory is the reason to reserve it.
3. Bring a complete package to the pre-order discussion. Include the spec table, eval set, weights location, target region, and a traffic profile. We quote Prime Inference based on your model and traffic profile. Qualified teams can also receive free GPU-hour trial credits to validate performance against their own workload.
4. Ask for the B200 or B300 recommendation in writing, with the reason. The reason should point back to memory headroom, TP width, or measured latency, not to a generic "newer is faster."

FAQ

Can a latency target be part of the deployment agreement? Bring the target as a written spec with percentiles, concurrency, request shape, and measurement point. Include the measurement method and sign-off criteria in the deployment scope. Uptime SLAs are set separately, by GPU series, configuration, and contract.

Is B300 always lower latency than B200? No. For a model that already fits on B200 at the TP width your ITL target needs, the listed FP8 throughput and NVLink bandwidth are the same on both. B300 earns its place through memory headroom, FP4 throughput, and attention performance on long prompts.

Can we use our own draft model for speculative decoding? The engines Prime Inference runs support speculative decoding. Whether it is part of your deployment, and with which draft model, is confirmed during planning and should be validated against your eval set.

Plan your dedicated B200 or B300 endpoint

Bring your latency spec, eval set, and traffic profile, and talk to our team about a B200 or B300 pre-order and the tuning scope for your model. To start measuring now, deploy your model on H200 from the GMI Cloud console.