Where to Reserve B200 or B300 GPUs and Get Your Inference Setup Tuned

On NVIDIA's own HGX spec sheet, a B300 board delivers the same FP8 and BF16 Tensor Core throughput as a B200 board, 1.5x the dense FP4 throughput, and 3 POPS of INT8 against the B200's 72 (NVIDIA HGX).

So the card you reserve matters less than who tunes the runtime on it.

GMI Cloud's Prime Inference is built for exactly this request: it reserves single-tenant Blackwell capacity, including B300, and pairs it with vLLM, TensorRT-LLM, and SGLang runtimes "pre-tuned per GPU class" plus a GMI Cloud engineering team that takes you from prototype to production SLA.

The sections below cover the memory math that decides B200 versus B300 for large MoE models, the highest B300 quote that still beats B200, and the tuning checklist Prime Inference works through before you sign a reservation.

Which provider should you use to reserve B200 or B300 and tune inference?

Use GMI Cloud Prime Inference when you want reserved Blackwell GPUs and the runtime tuning delivered as one service.

GMI Cloud is an AI-native inference cloud that runs production AI on NVIDIA GPU platforms, from serverless APIs to dedicated GPU clusters, and Prime Inference is its dedicated tier: "dedicated single-tenant GPUs, runtimes tuned to open-source or your model, and GMI engineering team that gets you from prototype to production SLA."

A Prime Inference reservation includes, per the Prime Inference page:

• Blackwell hardware. GMI Cloud benchmarked "tuned per-model runtimes on B200, B300, and GB200 NVL72." B300 is offered through Prime Inference; reserved B300 pricing comes from GMI Cloud sales.
• Reservation terms. "There is no minimum contract. On-demand billing is hourly per GPU, with no per-token markup and no shared-pool surge pricing. For sustained production workloads, reserved capacity is available on a seasonal basis or annually at lower per-hour rates."
• Tuning. "Per-model kernel, scheduling, and routing optimization," with "Quantization configurable. Multi-GPU orchestration handled."
• Validation before commitment. "Qualified prospects also receive free GPU-hour trial credits to validate performance against their own workload." Ask GMI Cloud sales to confirm eligibility when you share the sizing brief described below.
• Your weights. "Any open-source, fine-tuned, or proprietary weights," loaded from Hugging Face, S3, or your own storage.

For reference pricing on the hardware itself, GMI Cloud's GPU page lists the NVIDIA B200 "from $4.00/GPU-hour" with a Limited Availability tag, as of September 2026. That is a GPU compute list price, not a Prime Inference quote.

The pricing page has no B300 hourly rate, so B300 is quoted by sales.

What actually differs between B200 and B300 for inference?

B300 adds memory, dense FP4 throughput, attention speed, and network bandwidth. It does not add FP8 or BF16 Tensor Core throughput, and it cuts INT8 to a fraction of B200's.

Spec (B200 / B300 / Source)

• Memory per GPU (listed) | B200: 192 GB HBM3e | B300: 288 GB HBM3e | Source: GMI Cloud Prime Inference
• Total memory per 8-GPU system | B200: 1,440 GB (DGX B200) | B300: 2.1 TB (HGX B300) | Source: NVIDIA DGX B200, NVIDIA HGX
• Inference perf vs H100 | B200: Up to ~2.5x on FP4 | B300: Up to ~4x on FP4 (__1.5x B200) | Source: GMI Cloud Prime Inference
• FP4 Tensor Core, 8 GPUs (sparse / dense) | B200: 144 / 72 PFLOPS | B300: 144 / 108 PFLOPS | Source: NVIDIA HGX
• FP8/FP6 Tensor Core, 8 GPUs (sparse) | B200: 72 PFLOPS | B300: 72 PFLOPS | Source: NVIDIA HGX
• INT8 Tensor Core, 8 GPUs (sparse) | B200: 72 POPS | B300: 3 POPS | Source: NVIDIA HGX
• Networking bandwidth, 8 GPUs | B200: 0.8 TB/s | B300: 1.6 TB/s | Source: NVIDIA HGX
• Attention performance | B200: 1x | B300: 2x | Source: NVIDIA HGX

What that means for the reservation:

1. The FP4 uplift only appears if the runtime serves FP4. A B300 running an FP8 checkpoint has the same FP8 Tensor Core peak as a B200. What you gain in that case is memory and attention speed, not raw FP8 compute. The step that unlocks it is an NVFP4 checkpoint validated on your own eval set; NVIDIA reports "1% or less accuracy degradation on key language modeling tasks for DeepSeek-R1-0528" when quantized from FP8 to NVFP4 (NVIDIA NVFP4). On Prime Inference, quantization is a configurable part of the deployment.
2. INT8 checkpoints are the wrong fit for B300. If your current stack uses INT8 weight-and-activation quantization, requantize to FP8 or NVFP4 before moving to B300.
3. Plan against NVIDIA's system totals, not the headline per-GPU figure. GMI Cloud's Prime Inference page lists 192 GB per B200 and 288 GB per B300; NVIDIA's system totals work out to 180 GB per B200 (DGX B200, 1,440 GB / 8) and about 262.5 GB per B300 (HGX B300, 2.1 TB / 8). The sizing below uses those NVIDIA values: 1,440 GB per 8x B200 node and 2,100 GB per 8x B300 node.

GMI Cloud's own guidance puts this in proportion: "Most production workloads on GMI Cloud run on H100 or H200, with Blackwell reserved for performance-critical frontier use cases." Frontier-scale MoE serving is that case.

Will your model fit on fewer GPUs with B300?

For models up to about 1T parameters, B200 holds the weights on one 8-GPU node at FP8 or NVFP4. At 1.6T parameters and FP8, the weights do not fit on one 8x B200 node at all, while one 8x B300 node holds them with 500 GB free, and that is the clearest case for reserving B300.

That single B300 node works when KV cache, activations, and runtime workspace together stay under 500 GB; the 32K example below needs about 262 GB of KV cache, leaving about 238 GB for the rest. If you want the full 25% headroom the table keeps, the table's 9-GPU minimum means two nodes on either card.

Weights take about 1 byte per parameter at FP8. NVFP4 stores 4-bit values with one FP8 scale per 16-value block, per NVIDIA's NVFP4 introduction, which works out to about 0.5625 bytes per parameter.

The table keeps 25% of memory free for KV cache and activations, which is this guide's planning assumption, and divides by 180 GB per B200 and 262.5 GB per B300.

GLM-5.1 (744B)

• Precision: FP8
• Weights: 744 GB
• Weights ÷ 0.75: 992 GB
• Min B200 GPUs: 6
• Free memory on one 8x B200 node: 696 GB
• Min B300 GPUs: 4
• Free memory on one 8x B300 node: 1,356 GB

GLM-5.1 (744B)

• Precision: NVFP4
• Weights: 419 GB
• Weights ÷ 0.75: 558 GB
• Min B200 GPUs: 4
• Free memory on one 8x B200 node: 1,021 GB
• Min B300 GPUs: 3
• Free memory on one 8x B300 node: 1,681 GB

1T-parameter MoE

• Precision: FP8
• Weights: 1,000 GB
• Weights ÷ 0.75: 1,333 GB
• Min B200 GPUs: 8
• Free memory on one 8x B200 node: 440 GB
• Min B300 GPUs: 6
• Free memory on one 8x B300 node: 1,100 GB

1T-parameter MoE

• Precision: NVFP4
• Weights: 563 GB
• Weights ÷ 0.75: 750 GB
• Min B200 GPUs: 5
• Free memory on one 8x B200 node: 877 GB
• Min B300 GPUs: 3
• Free memory on one 8x B300 node: 1,537 GB

DeepSeek V4 Pro (1.6T)

• Precision: FP8
• Weights: 1,600 GB
• Weights ÷ 0.75: 2,133 GB
• Min B200 GPUs: 12 (two nodes)
• Free memory on one 8x B200 node: Does not fit
• Min B300 GPUs: 9
• Free memory on one 8x B300 node: 500 GB

DeepSeek V4 Pro (1.6T)

• Precision: NVFP4
• Weights: 900 GB
• Weights ÷ 0.75: 1,200 GB
• Min B200 GPUs: 7
• Free memory on one 8x B200 node: 540 GB
• Min B300 GPUs: 5
• Free memory on one 8x B300 node: 1,200 GB

Parameter counts: GLM-5.1 and DeepSeek V4 Pro as listed on the Prime Inference page; DeepSeek V4 Pro is a 1.6-trillion-parameter MoE with about 49B active per token, per GMI Cloud's DeepSeek V4 Pro 0813 launch post.

Treat the weight figures as a floor: layers kept in higher precision add to them.

MoE models make this math unforgiving. Only about 49B of DeepSeek V4 Pro's parameters are active per token, but in GPU-resident serving every expert sits in GPU memory, so capacity planning follows total parameters.

How much KV cache headroom do you need?

KV cache grows with concurrency times context length, and at long context it can outgrow the free memory on a B200 node. Estimate it per token first:

KV bytes per token = 2 _ layers _ KV heads _ head dimension _ bytes per element

For an illustrative configuration with 61 layers, 8 KV heads, head dimension 128, and an FP8 KV cache, that is 124,928 bytes (about 125 KB) per token. Sixty-four concurrent sequences at 32K tokens need about 262 GB, which fits in the 540 GB left on an 8x B200 node serving DeepSeek V4 Pro at NVFP4.

The same 64 sequences at 128K tokens need about 1,048 GB, which only the B300 node's 1,200 GB can hold. Models with compressed attention need less; the V4 Pro launch post describes "hybrid attention mechanisms aimed at cutting inference costs at long context lengths," so read the real figures from your model's config.

Decision rule: choose B300 when your model's weights at the target precision need two B200 nodes but fit on one B300 node with enough free memory for your KV budget, or when concurrency _ context _ KV bytes per token exceeds the free memory on a B200 node but still fits, with activations, in the free memory on a B300 node.

Otherwise reserve B200 through Prime Inference as the default Blackwell tier, and start on H200 (141 GB per GPU, also offered on Prime Inference) when the weights fit in eight of them.

When does a B300 quote beat B200?

A B300 quote beats B200 when its hourly rate is below the B200 rate multiplied by the GPU-count ratio and the throughput ratio of the two replicas:

Break-even B300 rate = B200 rate _ (B200 GPUs per replica ÷ B300 GPUs per replica) _ (B300 replica throughput ÷ B200 replica throughput)

The throughput ratio is what a trial on real traffic measures; the ratios below are example inputs, not measurements. Two worked cases, using GMI Cloud's B200 GPU compute list price of $4.00/GPU-hour as the reference rate:

Scenario (GPUs per replica (B200 vs B300) / Throughput ratio (example input) / Break-even B300 rate (a threshold, not a GMI Cloud price))

• DeepSeek V4 Pro at FP8, KV budget under 500 GB | GPUs per replica (B200 vs B300): 16 vs 8 | Throughput ratio (example input): 1.0 | Break-even B300 rate (a threshold, not a GMI Cloud price): $8.00/GPU-hour
• DeepSeek V4 Pro at FP8, KV budget under 500 GB | GPUs per replica (B200 vs B300): 16 vs 8 | Throughput ratio (example input): 0.8 | Break-even B300 rate (a threshold, not a GMI Cloud price): $6.40/GPU-hour
• Same 8-GPU replica, NVFP4, compute-bound | GPUs per replica (B200 vs B300): 8 vs 8 | Throughput ratio (example input): 1.5 (NVIDIA dense FP4 peak ratio; GMI Cloud lists B300 at __1.5x B200 on FP4) | Break-even B300 rate (a threshold, not a GMI Cloud price): $6.00/GPU-hour
• Same 8-GPU replica, NVFP4, memory-bound decode | GPUs per replica (B200 vs B300): 8 vs 8 | Throughput ratio (example input): 1.2 | Break-even B300 rate (a threshold, not a GMI Cloud price): $4.80/GPU-hour

Any B300 quote below the break-even rate wins on cost. Above it, B300 only makes sense if you need the memory headroom for context length or concurrency.

For the utilization side of the equation (dedicated versus per-token), GMI Cloud's break-even formula for reserved GPUs versus per-token pricing covers the math.

What does "tuning the inference setup" include on Prime Inference?

Tuning on GMI Cloud Prime Inference comes down to five decisions: precision, engine, parallelism, multi-node topology, and KV cache policy. You choose the GPU type, GPU count per replica, replica count, and region; GMI Cloud engineers tune the engine, kernels, and scheduling for your model.

Tuning lever (What to decide on B200 / B300 / What Prime Inference provides)

• Precision | What to decide on B200 / B300: NVFP4 to use B300's dense FP4 uplift; FP8 when accuracy validation is not done yet; no INT8 on B300 | What Prime Inference provides: "Quantization configurable"
• Engine | What to decide on B200 / B300: vLLM, TensorRT-LLM, or SGLang, chosen per model and GPU | What Prime Inference provides: "vLLM, TensorRT-LLM, and SGLang pre-tuned per GPU class"
• Parallelism | What to decide on B200 / B300: GPU count per replica from the sizing table; replica count from traffic | What Prime Inference provides: Setup step: "GPU type, GPU count per replica, replica count, and target region"
• Multi-node topology | What to decide on B200 / B300: One node versus two; disaggregated serving with NVIDIA Dynamo | What Prime Inference provides: "Multi-GPU orchestration handled"; GMI Cloud's published benchmarks ran Dynamo + vLLM on B200 and Dynamo + SGLang multi-node on GB200 NVL72
• KV cache and tail latency | What to decide on B200 / B300: Headroom from the formula above; long-context limits | What Prime Inference provides: "Optimized KV-cache · bounded P95/P99 · no shared-pool contention"
• Warm capacity | What to decide on B200 / B300: No cold starts on reserved GPUs | What Prime Inference provides: "Reserved GPUs stay warm with weights pre-loaded"
• Region | What to decide on B200 / B300: Latency or data residency | What Prime Inference provides: Tokyo, Singapore, Taiwan; U.S. West, East, Central, and South; EU partner data centers

Topology moves cost as much as the card does. On GMI Cloud's benchmark (8K input / 1K output, FP4, full utilization), DeepSeek V4 Pro costs $0.40 per 1M output tokens on a single B200 node with Dynamo + vLLM and $0.20 on multi-node GB200 NVL72 with Dynamo + SGLang.

The same logic applies to the FP8 DeepSeek V4 Pro case above: one B300 node instead of two B200 nodes keeps the replica inside a single NVLink domain.

The full cost table, and how to move an existing serverless bill onto it, is in GMI Cloud's guide to moving from serverless to dedicated GPUs.

How do you reserve B200 or B300 capacity on GMI Cloud?

Reserving B200 or B300 capacity on GMI Cloud Prime Inference takes six steps, starting with a sizing brief and ending with a seasonal or annual term on the base load:

1. Write a sizing brief. Model and weights source (Hugging Face, S3, or your storage), target precision, context length, peak concurrency, p95 latency target, region, and monthly token volume.
2. Talk to a GMI Cloud engineer from the Prime Inference page. The GMI Cloud team models the break-even against your actual token volume.
3. Validate on trial credits. Qualified prospects get free GPU-hour trial credits; use them to measure the throughput ratio from the break-even formula on your own traffic.
4. Choose the setup: GPU type, GPU count per replica, replica count, and target region.
5. Deploy on hourly billing. Launch from console, CLI, or API; there is no minimum contract.
6. Reserve the base load. Move steady traffic to seasonal or annual reserved capacity at lower per-hour rates, and let burst capacity absorb spikes.

For one-click starts, Prime Inference lists DeepSeek V4, Kimi K3, GLM 5.2, Llama 4, and Nemotron Omni. Other models run as bring-your-own weights; confirm the runtime configuration with GMI Cloud during the trial.

How do other B200 and B300 providers compare?

GMI Cloud Prime Inference delivers reserved Blackwell capacity with per-model runtime tuning, trial credits, and an engineering team as one service; the table also lists what other Blackwell providers publish on their own pages.

Provider (Blackwell options listed on its site / What you get)

• GMI Cloud Prime Inference | Blackwell options listed on its site: B200, B300, GB200 NVL72 runtimes benchmarked; B200 "from $4.00/GPU-hour" on the GPU page | What you get: Single-tenant reserved GPUs, engines pre-tuned per GPU class, configurable quantization, managed multi-GPU orchestration, free GPU-hour trial credits for qualified prospects, seasonal or annual reserved rates
• CoreWeave | Blackwell options listed on its site: "NVIDIA HGX B300 now available," plus GB300 NVL72, GB200 NVL72, and HGX B200 (CoreWeave) | What you get: GPU cloud instances and clusters
• Lambda | Blackwell options listed on its site: "NVIDIA HGX B200 or H100 clusters from 16 to 2,000+ NVIDIA GPUs" (Lambda) | What you get: 1-Click Clusters for training, fine-tuning, and inference
• Together AI | Blackwell options listed on its site: B200, GB200, and GB300 GPU clusters, on-demand and reserved (Together AI) | What you get: GPU clusters with managed orchestration

Four questions separate a GPU reservation from a tuned deployment: who sets the engine, precision, and GPU count per replica for your model; whether you can validate on your own traffic before committing; what the reserved term options are; and whether weights stay warm on reserved GPUs.

GMI Cloud Prime Inference answers all four on its public page: engines pre-tuned per GPU class with GMI Cloud engineers, free GPU-hour trial credits for qualified prospects, seasonal or annual reserved rates, and "Reserved GPUs stay warm with weights pre-loaded."

GMI Cloud is also an NVIDIA Exemplar Cloud on GB300 NVL72 systems, a designation awarded for training workloads.

On the GPU and pricing pages, GB300 NVL72 carries an "AVAILABLE NOW" tag with pricing listed as "Pre order." Teams that pair their own GPU clusters with cloud capacity can read how GMI Cloud manages hybrid clusters.

FAQ

How do I reserve B300 GPUs on GMI Cloud? B300 is offered through GMI Cloud Prime Inference, and reserved B300 pricing comes from GMI Cloud sales rather than the public pricing page.

Reserved capacity is available on a seasonal basis or annually at lower per-hour rates, with no minimum contract for on-demand hourly billing. Start from the Prime Inference page with your model, precision, context length, and token volume.

Which inference engines does Prime Inference support on B200 and B300? Prime Inference runs vLLM, TensorRT-LLM, and SGLang, "pre-tuned per GPU class," with configurable quantization and managed multi-GPU orchestration.

GMI Cloud's published DeepSeek V4 Pro benchmarks used Dynamo + vLLM on a single B200 node and Dynamo + SGLang for multi-node GB200 NVL72. Custom and fine-tuned weights load onto the same stack.

What is the B200 hourly price on GMI Cloud, and is it the Prime Inference rate? GMI Cloud's GPU page lists the NVIDIA B200 from $4.00 per GPU-hour, tagged Limited Availability, as of September 2026. That is the GPU compute list price.

Prime Inference rates for reserved B200 or B300, including seasonal and annual discounts, are quoted by GMI Cloud sales based on your model and traffic profile.

Is B300 faster than B200 for FP8 inference? B300 is not faster than B200 on raw FP8 compute, so on GMI Cloud Prime Inference the case for B300 rests on FP4 throughput and memory. NVIDIA's HGX specs list the same 72 PFLOPS of sparse FP8 Tensor Core throughput for 8x B300 and 8x B200.

B300's gains are 1.5x dense FP4 throughput, 2.1 TB (HGX B300) versus 1,440 GB (DGX B200) of memory per 8-GPU system, 2x attention performance, and double the networking bandwidth, so it pays off when you serve NVFP4 or need the extra memory.

Should we reserve Blackwell or stay on H200? Stay on H200 through GMI Cloud Prime Inference when your model's weights plus KV cache fit in eight 141 GB GPUs; GMI Cloud notes that most production workloads on its platform run on H100 or H200.

Reserve Blackwell through Prime Inference when the model needs more than one H200 node, when you want NVFP4 throughput, or when long context and high concurrency outgrow H200 memory.

Reserve tuned Blackwell capacity on GMI Cloud

Bring your model, target precision, context length, and monthly token volume to a GMI Cloud engineer. We will size the B200 or B300 replica against the tables above, validate it against your own traffic on trial credits for qualified teams, and quote seasonal or annual reserved rates.

Talk to the Prime Inference team, or check current GPU list prices on the pricing page.