B200 vs B300: is Blackwell Ultra worth the upgrade?

The B200 vs B300 choice is narrower than it looks. Both are Blackwell-generation GPUs with about 8 TB/s of memory bandwidth, NVLink 5 at 1.8 TB/s per GPU and, per NVIDIA's published specifications, the same dense FP8 (4.5 PFLOPS) and BF16 (2.25 PFLOPS) throughput. The B300, NVIDIA's Blackwell Ultra, changes three things: 288 GB of HBM3e instead of 180 GB, 13.5 PFLOPS of dense FP4 instead of 9, and, on our HGX servers, 800G networking per GPU instead of 400G. Everything on this page follows from those three differences.

B200 vs B300: the short answer

If your work runs in FP8 or BF16 and fits in 180 GB per GPU, the B200 does the same job, costs $600 less per GPU and $5,000 less per 8-GPU server, and is in stock. The B300 is worth its premium when you are short of memory, when you serve in FP4, or when you train across several servers and need faster links between them.

288 GB vs 180 GB: what the extra memory changes

108 GB more per GPU is the B300's main advantage. The table compares approximate memory needs with capacity, before runtime overhead, for which you should keep 10–30% in reserve:

Extra memory also buys concurrency. Serving a 70B model in FP8 on one GPU, the B200 has about 110 GB left for the KV cache and the B300 about 218 GB, roughly twice as many tokens in flight. For long-context serving and large-batch inference, that can matter more than any compute figure. It can also change how many GPUs a model needs: a model that has to be split across two B200s may fit on one B300, which removes the communication between them entirely.

Compute: the FP4 uplift and what stays the same

NVIDIA lists the B300 at 13.5 PFLOPS of dense FP4 against 9 PFLOPS for the B200, 50% more. Sparse FP4 is 18 PFLOPS on both, and dense FP8 and BF16 are identical. In practice:

• Serving in FP4: compute-bound phases such as prefill on long prompts and large-batch decoding can benefit from the higher dense FP4 peak.
• Training and fine-tuning in BF16 or FP8: no compute difference. The B300 helps only through memory: bigger micro-batches, longer sequences, fewer GPUs per model replica.
• Bandwidth-bound decoding: both have about 8 TB/s, so reading weights and KV cache runs at the same pace on either GPU.

FP4 brings its own trade-offs in accuracy and tooling: quality depends on the model and the quantization recipe, so validate on your own evaluation set. Our FP8 and FP4 explainer covers the formats.

Which workloads gain the most from the B300

• Mixture-of-experts models. They activate only a fraction of their parameters per token, so compute per token is modest while every expert must stay in memory. Capacity, not compute, decides how many GPUs they need, which is exactly where the B300 differs.
• Long-context serving. The KV cache grows with context length and concurrency; twice the free memory means about twice the tokens in flight.
• Several models on one GPU. A main model plus a draft model for speculative decoding, an embedding model and a reranker fit together more easily in 288 GB.
• Large fine-tunes on one node. Full fine-tuning of a 70B model fits on one HGX B300 instead of spilling onto a second server.

A worked example: a mixture-of-experts model of about 670B parameters served in BF16 needs about 1,340 GB for its weights alone. One HGX B300 holds it with about 960 GB to spare, for $39,900 a month. On B200s it needs two HGX servers, $69,800 a month, plus the networking to link them. If your models fit in 180 GB per GPU, none of this applies, and the B200 is the better buy.

800G vs 400G: networking between servers

Inside the server, both HGX platforms connect eight GPUs over NVLink 5 at 1.8 TB/s per GPU, so single-node jobs see no difference. Between servers they differ: our HGX B300 has 8× 800G ConnectX-8 adapters, one per GPU, against 8× 400G ConnectX-7 on the HGX B200. Twice the per-GPU network bandwidth shortens gradient exchanges in data-parallel training and transfers in pipeline and expert parallelism across nodes. The host is larger too: two Xeon 6776P CPUs with 128 cores in total and 4 TB of DDR5, against two Xeon 8570 with 112 cores and 2 TB on the B200 server.

If you plan to link several servers, ask about private networking between them or a multi-node GPU cluster. On a single server, the 800G adapters alone are not a reason to choose the B300.

Cost per month, cost per GB and stock

A single B300 costs $5,190 a month against $4,590 for a B200: $600 more, or 13%, for 60% more memory. The 8-GPU HGX B300 costs $39,900 against $34,900, $5,000 more (14%). Per GB of memory the B300 is clearly cheaper: about $18.02 against $25.50 a month on single-GPU plans, and $17.32 against $24.24 on 8-GPU servers. Per unit of bandwidth or of FP8 and BF16 compute, the B200 is cheaper, because those are equal and it costs less.

The single-GPU plans also differ beyond the GPU: the B300 comes with 28 vCPUs, 384 GB of RAM and 3.5 TB of NVMe, the B200 with 26 vCPUs, 256 GB and 2.75 TB. For deployments beyond one server, the GB300 NVL72 rack places 72 Blackwell Ultra GPUs in one NVLink domain, on 12–36 month terms.

How to decide

1. Size the memory: weights at your precision, plus KV cache for your context and concurrency, plus 10–30% headroom. Under about 160 GB per GPU, the B200 covers it.
2. Check your precision: in FP8 or BF16 the compute is the same, so pay for the B300 only if you need the memory.
3. Check your topology: on one server either works; for several servers training together, the 800G links help.
4. Check timing: the B200 is in stock; for the B300, ask for a confirmed delivery date through our contact form.

Timing does not have to block you. Terms are monthly with no cancellation fee, so you can start on a B200 that is in stock today, order a B300 once its delivery date is confirmed, and cancel the B200 at the end of its paid month.