Which GPU Providers Can Help Size B200 vs B300 for a Training Job?

The GPU providers worth recommending are the ones that will size both options against the same training job: they state usable memory per GPU for the exact SKU, the scale-out fabric the cluster actually runs, and NVFP4 software support, let you run a representative training step, and commit to a delivery date. GMI Cloud is a practical place to build the B200 side of that comparison. B200 on GMI Cloud is open for pre-order and coming soon, so you can size and price a B200 training plan now and ask for a delivery date, then hold any B300 quote, from whichever provider offers HGX B300 training capacity, to the same job, token budget, and deadline.

Sizing both matters because the spec sheet alone won't pick the winner. B300's main training advantages over B200 are memory capacity, FP4 compute, attention throughput, and scale-out bandwidth; NVIDIA's published peak BF16 and FP8 Tensor Core throughput is unchanged. B300 adds about 1.5× the HBM per GPU and the dense FP4 throughput, roughly 2× the attention-layer throughput, and twice the per-node scale-out bandwidth, while giving up most of B200's FP64 and INT8 performance. The better choice depends on the bottleneck in your training job, the quoted rate, and the delivery date, which is what a good provider should help you measure.

What actually differs between B200 and B300 for training

HGX B200 and HGX B300 are both eight-GPU platforms from the Blackwell generation. Inside the node they use the same fifth-generation NVLink: 1.8 TB/s per GPU and 14.4 TB/s across the node. Memory bandwidth is also unchanged at 8 TB/s per GPU. The NVLink domain you use for tensor parallelism is the same size and speed on both.

System figures come from NVIDIA's HGX product page; per-chip memory and bandwidth come from "Inside NVIDIA Blackwell Ultra," NVIDIA's technical overview of the chip on its developer blog. The FP8/FP6, BF16/FP16, TF32, and INT8 entries below are NVIDIA's sparse figures; their dense values are half those figures. The FP4 row explicitly uses dense throughput.

NVIDIA lists chip capacities and rounded system totals separately. Size a training job against the usable memory reported by the deployed system.

Equal peak BF16 and FP8 throughput does not imply equal training speed: memory pressure, attention, and communication can still change step time.

Where B300 changes a training plan

Each B300 advantage pays off only for a specific kind of bottleneck.

Memory-bound jobs

About 1.5× the memory per GPU gives you room to shard less, recompute fewer activations, or run larger micro-batches. That can raise useful throughput even at identical peak FLOPS, especially for jobs spending time on activation checkpointing or on parallelism added only to make the model fit.

NVFP4 training recipes

Dense FP4 throughput is about 1.5× higher on B300, but the gain applies only when the recipe actually runs in NVFP4. On its developer blog, NVIDIA has demonstrated NVFP4 pretraining on a 12B-parameter hybrid Mamba-Transformer model over 10 trillion tokens, with validation loss close to an FP8 baseline. A team training in BF16 or FP8 gets no FP4 benefit.

Attention-heavy, long-sequence training

NVIDIA rates Blackwell Ultra's attention-layer throughput at about 2× Blackwell, driven by faster exponent units used in softmax. That matters most when attention takes a large share of step time, as it often does at long context lengths. It does not make end-to-end training twice as fast; the rest of the step still runs at the same BF16 or FP8 rate.

Multi-node scale-out

NVIDIA's reference B300 platforms double per-node scale-out bandwidth, using 800 Gb/s ConnectX-8 NICs compared with 400 Gb/s ConnectX-7 on B200. Data-parallel jobs across many nodes can spend less time on gradient exchange, but only if the provider's switches and topology run at the same rate.

When B200 is the better fit

• BF16 or FP8 jobs that fit in memory. Peak compute is the same, so B300's extra memory may not buy any speed.
• Workloads that depend on FP64 or INT8. B300 drops FP64 from 296 to 10 TFLOPS and INT8 from 72 to 3 POPS per eight-GPU system. Double-precision code and INT8 pipelines may run better on B200.
• Software that doesn't yet support NVFP4. Without a working FP4 recipe, the largest B300 compute gain doesn't apply.
• Deadlines that can't wait. If B200 capacity can be delivered sooner than B300, the earlier start often beats a faster GPU that arrives a quarter later, so confirmed delivery dates belong in the cost comparison.

How to size the same job on both

A fair comparison holds the job constant and changes only the hardware.

1. Calculate the memory floor. With mixed-precision Adam, model states alone take about 16 bytes per parameter before activations: 2 for weights, 2 for gradients, and 12 for the FP32 master weights and Adam states, as described in the ZeRO paper from Microsoft researchers. Back-of-the-envelope example, not a benchmark: a 70B model needs about 1.12 TB for model states. Fully sharded (ZeRO-3 or FSDP) across one eight-GPU node, that is about 140 GB per GPU. Subtract that 140 GB from each GPU's usable memory to estimate the remaining budget for activations, communication buffers, and fragmentation, and FSDP also temporarily gathers the full weights of the current layer during forward and backward passes, which takes part of it. Activations grow with sequence length and micro-batch size, and 8-bit optimizers change the math, so recompute with your own settings and the provider's usable memory per GPU.
2. Identify the bottleneck. Profile a few steps and see where time goes: compute, memory pressure, intra-node communication, inter-node communication, or data loading. Only memory pressure, FP4 math, attention, and inter-node traffic point toward B300.
3. Run a representative step. Keep the same code, global batch size, precision, and target quality. Record step time, model FLOPS utilization, peak memory, and time spent waiting on communication. If B300's memory allows a different parallelism layout, test that layout separately.
4. Compare time and cost to the same checkpoint. Divide total tokens by measured throughput to estimate wall-clock time to a target evaluation checkpoint, then multiply by GPU count and rate. Add the time spent writing checkpoints and recovering from failures. A cluster with a lower hourly rate but slow recovery can lose on total cost.

What to ask a provider for each option

Ask for each plan to be built on the same job description, with these fields filled in:

• GPU count, node type, and usable memory per GPU for the exact SKU, since per-GPU figures vary by system
• Scale-out fabric type and the per-GPU NIC speed the cluster actually runs, not just the platform maximum
• Driver, CUDA, and framework versions, and whether they support NVFP4 training
• Shared storage throughput for checkpoint writes and data loading
• Scheduler (Kubernetes, Slurm, or bare metal you manage yourself) and region
• Pricing basis, support scope, and a confirmed delivery date
• An acceptance test you can run before the full commitment starts

Setting up a B200 training plan with GMI Cloud

B200 on GMI Cloud GPU infrastructure is open for pre-order and coming soon, and the GPU page lists pricing from $5.00 per GPU-hour; check current B200 pricing for the latest rate before you commit. The pricing page distinguishes on-demand usage from reserved commitments, and a starting rate is not the total cost of a training cluster.

Our bare metal offering is built for large-scale training, with full root access so your team controls the software stack, drivers, and framework versions. Managed GPU Clusters, in early access, run Kubernetes with H200 or B200 nodes provisioned and operated by GMI (H200 is available now, while B200 nodes follow the Blackwell pre-order); after you submit a cluster request, contact our support team to activate it. Our networking is RDMA-ready, and we deploy across the US, APAC, and EU.

For Blackwell Ultra, GB300 NVL72 is open for pre-order and coming soon. It is a rack-scale system, not an eight-GPU HGX B300 node, so confirm whether any Blackwell Ultra quote covers the node or the rack; the rack requires a separate sizing exercise from the comparison above.

FAQ

How much checkpoint storage does a training run need? A resumable checkpoint stores the FP32 master weights and both Adam moments, about 12 bytes per parameter, plus 2 more if the framework also saves the 16-bit weights; gradients are not saved. For a 70B model that is roughly 0.84–0.98 TB per checkpoint in decimal units, before serialization overhead. Multiply by the number of checkpoints you keep, and ask for sustained write throughput so saves don't stall training.

Will our PyTorch and CUDA stack use B300's FP4? Not automatically. FP4 training needs framework, library, and recipe support for NVFP4, and results depend on your model. Validate it in a short trial run, including loss curves against your FP8 or BF16 baseline, before the FP4 gain goes into your sizing.

Plan a B200 training configuration

Contact Sales with your model size, precision, token budget, and deadline to discuss a B200 pre-order and training configuration, or open the console to set up your account.