H200 vs B200: What You Actually Need to Know in 2026

If you’re weighing H200 vs B200 for your next deployment, the decision turns on one question: can your workload use FP4? The H200 is the mature Hopper card that has been the default for 70B-class work for over a year. The B200 is Blackwell, newer and pricier, and on paper it should win everything. It doesn’t, and the reason it doesn’t is the most useful thing to understand before you spend anything.

So here’s the short version. For models up to 70B served in FP8, the H200 is the call, with no migration tax if you’re already on Hopper. For 100B+ models, or real concurrency where you can run FP4, the B200 usually wins, often on cost per token despite the higher hourly rate. And if you need FP4 in any form, it has to be the B200, because the H200 cannot run it natively. The rest of this piece is the detail behind that split: the specs, the public benchmarks, where the H200’s memory and precision ceilings turn into hard limits, and how the economics work once throughput is in the picture.

H200 vs B200 specs

Sources: NVIDIA H200 datasheet, NVIDIA Blackwell B200 HGX specifications. FP8 and FP4 figures are dense; NVIDIA’s headline numbers include sparsity, which doubles them.

The FP4 row decides this comparison, the same way the FP8 row decided the older H200 vs A100 one. The H200 has no native FP4, and no software trick gives Hopper what Blackwell does in hardware. By contrast, the bandwidth jump from 4.8 to 7.7 TB/s is real but quieter: for models that fit in both cards, neither saturates its tensor cores, so the B200’s lead narrows to roughly its bandwidth ratio. You feel bandwidth on data movement, not raw matrix throughput.

Architecture: why the gap is not where you expect

Hopper and Blackwell were built for different points on the model-size curve.

The H200 is not a new chip. It’s the same GH100 die as the H100 with a better memory subsystem strapped on, same CUDA cores, same Transformer Engine, same FP8 path. The jump to 141 GB of HBM3e at 4.8 TB/s is what lets a single H200 hold Llama 70B in FP16 without tensor parallelism. But the compute die is unchanged, and it has no FP4.

The B200 is a ground-up Blackwell design: native FP4 compute, NVLink 5 at double the per-GPU bandwidth, and a 1,000W envelope to feed it. The catch with FP4 is that the model and the serving framework both have to support it, and not all of them do yet.

This explains a result that surprises people who only read spec sheets. Practitioners benchmarking 8x H200 against 8x B200 on community hardware tests repeatedly find the FP8 and BF16 gap far smaller than expected, sometimes nearly flat. Three things are going on:

• Blackwell is tuned for rack-scale, so it shines on very large models and high concurrency, not single-node FP8 serving.
  
• The Hopper stack has years more optimization behind it, so its software is closer to the metal.
  
• The B200’s biggest lever only fires on FP4. The ~25x figures in launch slides are the FP4-and-rack-scale best case, not what a single eight-card node does on your current stack, where the real-world gain is closer to 2x.

Memory: where the H200 runs out of room

A 70B model in FP16 needs roughly 140 GB of weights. The H200’s 141 GB holds it on a single card, just. That is the H200’s signature trick, and also its ceiling. Push past 70B, to a 100B+ model or a 70B model with a large KV cache for long-context serving, and you’re sharding across cards.

The B200’s 180 GB clears the 141-to-180 GB band on a single card, with no tensor parallelism and none of the inter-GPU sync overhead it brings. As a result, you get fewer cards per replica, lower communication cost and a simpler topology. For models just above the H200’s ceiling, the B200 is the most cost-efficient single card you can rent.

It’s the same constraint the A100 hit at 80 GB, moved up the curve. Each generation, the memory ceiling that felt generous at launch becomes the thing that dictates your sharding strategy two years on.

H200 vs B200 for inference

The clearest public benchmark comes from SemiAnalysis InferenceX (originally InferenceMAX v1), via Spheron’s published comparison, running Llama 3.3 70B on vLLM.

Source: SemiAnalysis InferenceX, 50 TPS/user, 1K/1K sequence length, vLLM. NVIDIA’s own InferenceMAX writeup reports the same 4x per-GPU throughput on this workload.

The B200 costs more per hour and produces roughly four times the tokens per hour in FP4, which lands it near a third of the H200’s cost per token. The higher hourly rate is the price of a faster meter, not the cost of the work.

The value of the newer card scales with model size, context length, and concurrency. The bigger, longer and busier the workload, the wider the gap. Run the inverse and it closes:

H200 vs B200 for training

For models that fit in both cards, training is mostly bandwidth-bound rather than compute-bound, because at typical batch sizes for 7B to 13B models neither GPU maxes its tensor cores. The B200’s bandwidth edge speeds data movement, but the training gap is smaller than the inference one.

It widens at 70B and up. The B200’s 180 GB removes two-way tensor parallelism on a 70B model, and for holding a 405B model’s FP8 weights the arithmetic is stark:

• H200: four cards at FP8 (4 x 141 GB = 564 GB)
  
• B200: three cards (3 x 180 GB = 540 GB)

In addition, dropping a card removes the NVLink traffic it would have generated.

One caveat cuts against the B200: FP4 training is immature. Most workloads aren’t optimized for four-bit yet, and kernels you’d want, like a practical flash-attention at FP4, aren’t there. The B200’s training advantage today comes from memory and bandwidth, not its headline FP4 number. Plan for what ships now.

H200 vs B200 cost: TCO and cost per token

A useful way to ground hourly rates is total cost of ownership, the all-in cost of running the hardware independent of cloud supply swings. A widely shared operator breakdown of an 8-GPU system, covering electricity, depreciation, cost of capital, spares, colocation and fixed ops, lands at roughly:

Overall, the B200 costs more, mostly through heavier depreciation and the 1,000W draw against 700W. At hyperscaler scale both drop 30% to 50%, but the ratio holds. The figures move with electricity and financing; the shape does not.

So the B200 carries a real run-cost premium, and whether it’s worth paying comes down to one question: can your workload turn the extra throughput into lower cost per token? For 70B+ at FP4 and volume, yes, the same logic that made the pricier H200 cheaper per token than the A100 makes the pricier B200 cheaper than the H200 here. A card at a higher hourly rate that produces four times the tokens is the cheap option, measured the way your bill measures it. For a 7B model in FP8, the H200 wins on both hourly rate and cost per token.

When to reach for the B200

• Your 70B+ model doesn’t fit in 141 GB and you’re sharding across cards you’d rather not. The B200’s 180 GB pulls it back to a single card.
  
• You can use FP4. With model and framework support, the B200 roughly doubles throughput over FP8 and the cost-per-token math tips hard in its favor.
  
• You run high-volume inference where cost per token is what lands on the invoice. On large models in FP4 the B200 sits near a third of the H200’s, and it compounds at scale.
  
• You’re starting fresh with no Hopper estate, so the current-generation stack avoids a migration later as Hopper capacity tightens.

When the H200 is still the right call

• Models from 7B to 70B in FP8 or FP16, where 141 GB holds the model on one card and the mature Hopper stack does the rest. This is a large share of production work.
  
• Cost-sensitive serving that doesn’t touch FP4. Running FP8 or INT8, you gain nothing from paying for FP4 you won’t use.
  
• Existing Hopper clusters hitting their SLOs. No capacity pressure means no urgent case to move; the upgrade only pays back when you’re bottlenecked on something the B200 fixes.
  
• Fine-tuning 70B models. QLoRA on a 70B model (4-bit base weights, 16-bit adapters) fits comfortably in 141 GB on a single H200. One card, no tensor parallelism.

Where the B300 fits

The B200 is already not the newest Blackwell card. The B300, or Blackwell Ultra, pushes memory to 288 GB, covering the same sizes the B200 handles plus far more headroom for very large models and long-context KV caches.

If your model fits in 180 GB and you can use FP4, the B200 is the most cost-efficient single card. You step up to the B300 only when you need the room, either because the model exceeds 180 GB or because a long-context, high-concurrency KV cache runs into hundreds of gigabytes. Frontier reasoning models increasingly do; a 70B inference service does not, yet.

The bottom line on H200 vs B200

The decision reduces to three cases:

• Up to 141 GB, FP8, cost-sensitive: the H200. Mature stack, single-card 70B serving, no migration tax.
  
• 141 to 180 GB, or FP4 at volume: the B200. Higher hourly rate, lower cost per token.
  
• Past 180 GB, or long-context with large KV caches: the B300, where the extra memory earns its premium.

Judge these cards by cost per output, not cost per hour.

Run your H200 workloads on Neysa

Neysa Velocis gives you H200 SXM on bare metal and as on-demand VMs, from a single card to 8-card nodes, billed in INR or USD with committed-use discounts down to 36 months. A single H200 SXM VM (141 GB) starts at $4.72 per hour on demand, and an 8x H200 bare-metal node (1,128 GB total) is available on terms from one month upward, with no charge for data ingress, egress, or inference transactions. It’s a general-purpose AI cloud with bare-metal GPU access and an operator pedigree behind the platform, built for teams running serious inference and training in India and the wider region.

Evaluating Blackwell for an upcoming deployment? Talk to the team about B-series availability and we’ll size the H200 against Blackwell for your models and volumes. View GPU pricing.