H200 vs B200: Which GPU Fits Your Workload
H200 vs B200 for training, fine-tuning and inference: when Hopper is enough, when Blackwell earns its place, and how to pilot and compare cost per job.
The one-line difference
H200 vs B200 is a generation change from NVIDIA Hopper to NVIDIA Blackwell. In Pacific's catalog the first visible gap is memory: H200 SXM lists 141 GB per GPU and B200 SXM lists 180 GB per GPU, both in 8-GPU nodes.
NVIDIA's published specs also give B200 more compute and higher memory bandwidth than H200, and they add lower-precision formats such as FP4. H200 is the mature Hopper option, with plenty of memory and a software path that teams have tuned for years.
If you lead an ML or infrastructure team choosing nodes for a training run, a fine-tuning pass or an inference fleet, the real question is which generation finishes your work with less risk and less budget. If you are still deciding within Hopper, read the H100 vs H200 comparison guide first.
When H200 is the sensible rental
Rent H200 when your model and working set fit in 141 GB with headroom. Headroom matters because activations, KV cache and optimizer state often grow past what the weights alone suggest.
H200 also fits when your stack is already tuned for Hopper. A team serving a model with batching, kernels and quantization already dialed in on Hopper often gets more from that stable setup than from moving to new hardware and retuning everything.
Short experiments are another good case. If you are testing a data mix, a learning rate schedule or a fine-tuning recipe, the most proven software path keeps your attention on the experiment. Fewer moving parts, fewer surprises.
For the mechanics of booking Hopper nodes, see how to rent H200 GPUs.
When B200 earns its place
B200 earns its place when your job is limited by compute or by memory. A good test is whether the extra headroom changes your plan, such as fewer nodes, less sharding or a shorter run.
Compute-bound training is the clearest case. Large models are the second: a team that today spreads a model across extra nodes with tensor or pipeline parallelism may find that 180 GB per GPU lets it drop a sharding layer, which simplifies the code and the node count.
Lower-precision formats such as FP4 only help if you are ready to use them. Validate accuracy on your own evals before you trust the result, and keep a higher-precision baseline to compare against.
Longer runs are where faster time to finish pays off most. Prove the speedup on your own job before you plan around it.
Check software readiness before you move a job
Blackwell needs recent NVIDIA driver and CUDA releases plus framework builds that support it. Confirm those versions before you move a job, because a mismatch burns pilot time on setup instead of measurement.
Walk the stack from the bottom up. Check the driver and CUDA versions on the node, your framework release, any custom or third-party kernels such as fused attention, your communication libraries and the container images your jobs actually launch from.
An infrastructure engineer porting a training pipeline should rebuild the container against the supported CUDA release, run the unit tests and a short training step, then confirm that loss curves and eval scores match the Hopper baseline before anyone schedules the full run.
Hopper stacks are mature and widely tuned. That maturity is a real asset, so count the porting work in your budget.
Run a short pilot on your own workload
Start narrow with a short pilot on your own code and data. Use the same model, dataset, batch logic and checkpoint cadence on both GPU types so the comparison holds.
Measure useful work per run: tokens or samples processed for training, requests served at your latency target for inference. Record time to finish, the number of nodes the job needs, and any failures or restarts along the way.
A good pilot for a fine-tuning team is one representative job run end to end on H200 and on B200, with the same eval suite at the end, so the lead can show finance and engineering a single page of proof.
Write down the versions you used. A pilot result without its software stack attached is hard to repeat.
Compare cost per finished job
Compare cost per finished job and node count before you look at hourly rates. A per GPU-hour figure hides how many hours and how many nodes the job actually consumes.
Take your pilot numbers and multiply through: rate times GPUs times hours to finish, plus the engineering time spent porting and validating. If B200 removes a node or shortens the run, that shows up here; if H200 finishes on fewer retries with no porting, that shows up too.
Pacific Compute shows on-demand preview rates per GPU-hour for H200 and B200, and those rates are indicative until Pacific confirms the allocation. Use them for a first pass, then rerun the math on the confirmed quote. The guide to GPU cloud pricing cost drivers covers the other line items to include.
When reserved B300 is the better question
Look at reserved B300 when your horizon is long and memory per GPU dominates. In Pacific's catalog B300 SXM lists 288 GB per GPU in 8-GPU nodes, and it is offered by request.
Pacific's reserved B300 cluster offer is a single-tenant bare-metal cluster. The Pod 32 reference comprises 32 HGX B300 nodes with a dedicated, configurable fabric, plus witnessed factory, site and integration acceptance evidence.
A platform team planning a long training program on a dedicated cluster should treat this as a project review: configuration and delivery window are confirmed through a written offer, and the deployment timeline is a site-specific target set out in that offer. For the step beyond B200, read this comparison of B300 vs B200 for reserved capacity buyers.
How to start with Pacific
Pick a GPU type and size on Pacific Compute, then review the full commitment. Review revalidates capacity, the current quote and terms before you commit.
Confirm the node details for your choice. Pacific's H100 listing states dedicated bare-metal Linux nodes with SSH access, NVIDIA drivers and CUDA installed, InfiniBand interconnect, local NVMe erased when the resource ends, and on-demand plus reserved terms by request. Ask Pacific to confirm the same for H200 and B200 nodes, along with the exact driver and CUDA versions you need.
Availability shown is recorded by Pacific and confirmed when an order is accepted. A paid order is not a ready resource, so plan your pilot start around confirmation. For the broader rental process, see the GPU rental overview.