NVIDIA H200 vs B200 vs GB200: Which GPU to Rent for AI in 2026?

All Spheron prices in this article are as of 17 March 2026 and can fluctuate over time based on GPU availability. Check current GPU pricing for live rates.

Three GPU generations. Three price points. Spheron on-demand pricing as of 17 March 2026: H200 at $4.54/hr, B200 at $6.03/hr, and GB200 Superchips at $9.08/hr per Superchip. Pricing can fluctuate over time based on GPU availability. If you're picking infrastructure for a new deployment in 2026, the right answer depends entirely on model size, throughput requirements, and whether you need NVIDIA's FP4 quantization.

This post gives you the concrete answer: architecture differences that matter, benchmark data, cost-per-token math, and a clear decision framework for each GPU.

Quick Answer: Which GPU Should You Choose?

Note: GB200 pricing is per GB200 Superchip (1 Grace CPU + 2 B200 dies), not per individual GPU die or per full NVL72 rack. A complete GB200 NVL72 rack contains 36 Superchips (72 B200 dies total). There is no GB200 single-GPU rental; for individual Blackwell access, see B200 or B300.

Check current GPU pricing for live rates.

Architecture: Hopper vs Blackwell vs Grace Blackwell

H200 (Hopper GH100)

The H200 uses the same GH100 die as the H100. Same CUDA cores, same fourth-generation Tensor Cores, same Transformer Engine with FP8. The only meaningful change is the memory subsystem: 141 GB HBM3e at 4.8 TB/s, up from the H100's 80 GB HBM3 at 3.35 TB/s.

That memory upgrade is significant. It's what allows the H200 to serve Llama 70B on a single GPU, something the H100 can only do with 2-way tensor parallelism. For the full Hopper architecture breakdown, see our H100 vs H200 comparison.

The H200 does not support FP4. It supports FP8 via the Transformer Engine, with dense FP8 performance around 3,958 TFLOPS per GPU.

B200 (Blackwell GB100)

The B200 is a new architecture, not an incremental upgrade. Key differences from H200:

• 180 GB HBM3e at 7.7 TB/s in HGX form factor (28% more VRAM, ~60% more bandwidth)
• Native FP4 compute: 9,000 dense TFLOPS FP4, 4,500 dense TFLOPS FP8
• NVLink 5 at 1.8 TB/s per GPU (all form factors including HGX and NVL72)
• 1,000W TDP (vs 700W for H200)

The FP4 support is the architectural story. For models that support FP4 quantization, you get roughly 2x the effective throughput compared to FP8 on the same GPU. Newer Blackwell-optimized models and inference frameworks are increasingly targeting FP4. For a detailed breakdown of the quantization benefits, see our FP4 quantization guide.

GB200 (Grace Blackwell)

The GB200 is not a GPU in the traditional sense. It's NVIDIA's Grace Blackwell Superchip: two B200 GPUs and one Grace ARM CPU on a single package, connected by NVLink-C2C at 900 GB/s.

What distinguishes GB200 from pairing a server CPU with two B200s is the tight NVLink-C2C interconnect. Standard PCIe connects a CPU to a GPU at 128 GB/s (Gen 5). NVLink-C2C runs at 900 GB/s, roughly 7x the bandwidth, and dramatically reduces CPU-GPU data transfer latency for workloads that require heavy host-side data movement.

The GB200 NVL72 rack system pairs 36 GB200 Superchips (72 B200 chips total) with NVSwitch for all-to-all GPU communication. This is what hyperscalers deploy for large-scale inference of reasoning models. It is not available as individual GPU rentals. There is no /gpu-rental/gb200/ page because GB200 access is sold as rack-node capacity.

Full Specifications Comparison

†H200 figures reflect dense compute per the official NVIDIA H200 datasheet (TF32: 989 TFLOPS, FP16: 1,979 TFLOPS, FP8: 3,958 TFLOPS). With 2:4 structured sparsity, H200 FP8 reaches 7,916 TFLOPS. The B200's 4,500 dense TFLOPS FP8 is approximately 1.14x the H200's dense FP8 (3,958). The larger B200 compute advantage comes from FP4 support (9,000 dense TFLOPS), a precision tier unavailable on Hopper.

Note: The B200 GPU die inside the GB200 NVL72 is configured with 186 GB HBM3e at 8 TB/s and a 1,200W TDP, while the standalone B200 SXM (HGX form factor) ships with 180 GB HBM3e at 7.7 TB/s and a 1,000W TDP. The higher TDP in the NVL72 configuration also yields approximately 11% higher compute throughput per die: FP4 dense reaches ~10,000 TFLOPS per die (vs 9,000 for standalone HGX B200), based on the GB200 NVL72 system delivering 1,440 PFLOPS FP4 sparse total across 72 B200 dies per NVIDIA's official specification. (The NVL72 has 13.4 TB HBM3e total across 72 GPUs, per NVIDIA's official specification.)

LLM Inference Performance: Real Benchmark Data

The table below uses SemiAnalysis InferenceX benchmark data (originally published as InferenceMAX v1) for Llama 3.3 70B (at 50 TPS/user interactive serving with vLLM) and community throughput estimates for other models. Numbers marked with * are estimates from NVIDIA published bandwidth scaling ratios. Real-world results will vary by batch size, framework version, and quantization level.

Llama 3.3 70B numbers from SemiAnalysis InferenceX benchmarks (originally InferenceMAX v1; 50 TPS/user, 1K/1K sequence length) using vLLM. Mistral 7B and Llama 3.1 405B throughput estimates are from NVIDIA published bandwidth scaling ratios. Cost calculations assume single-GPU deployment unless noted.

The cost-per-token gap is more revealing than the throughput gap. For Llama 3.3 70B, the B200 at $6.03/hr delivers roughly 4x more throughput than the H200 at $4.54/hr in FP4 optimized serving, per SemiAnalysis InferenceX benchmarks using vLLM. The combined effect: approximately $0.17 per million tokens on B200 vs $0.50 on H200, roughly 3x lower cost per output token at this model size.

For small models like Mistral 7B, the relative throughput advantage narrows to roughly 1.6x (driven by B200's bandwidth ratio over H200), while the Llama 70B gain reaches 4x with FP4 optimization. The H200 at $4.54/hr still delivers better cost-per-token than older H100 PCIe for models that benefit from 4.8 TB/s bandwidth.

Training Performance: When Architecture Differences Matter

Small and medium models (sub-30B)

For models that fit comfortably in both H200 and B200 VRAM, the performance difference during training is primarily memory-bandwidth-driven, not compute-driven. At typical batch sizes for 7B-13B models, neither GPU is saturating its tensor cores. The B200's bandwidth advantage (7.7 vs 4.8 TB/s for HGX) translates to faster data movement, but the training throughput gap is smaller than in inference.

Large models (70B+)

The B200's 180 GB eliminates the need for 2-GPU tensor parallelism for 70B models. Fewer GPUs per model instance means lower inter-GPU communication overhead. For a 405B model, the math is stark: H200 needs 4 GPUs at FP8 (4 x 141 GB = 564 GB), while B200 needs 3 GPUs (3 x 180 GB = 540 GB). The saved GPU also saves the NVLink bandwidth consumed by the fourth node.

GB200 for training

The NVLink-C2C interconnect eliminates the PCIe bottleneck between CPU and GPU. For training pipelines where CPU data preprocessing feeds GPU compute, this 900 GB/s CPU-GPU bandwidth matters. Standard PCIe Gen 5 runs at 128 GB/s; NVLink-C2C is roughly 7x faster. If your training profiling shows data loading as the bottleneck, GB200's tight CPU-GPU integration directly reduces iteration time.

Price-Performance: Cost per Token and Cost per Training Step

Pricing reality check

Spheron on-demand pricing as of 17 March 2026. Rates can fluctuate based on GPU availability.

The B200 commands a higher hourly rate than the H200, at $6.03 vs $4.54. This premium reflects the newer Blackwell architecture and its significantly higher throughput. As the cost-per-token analysis below shows, the B200's throughput advantage more than compensates for its higher hourly rate for large model workloads.

Cost-per-token analysis

GB200 per-B200-die estimate based on $9.08/Superchip divided by 2 B200 dies per GB200 Superchip.

Judge GPUs by cost-per-output, not cost-per-hour. Even though the B200 costs more per hour than the H200, it delivers significantly higher throughput, resulting in lower cost per output token for large models. The cost-per-token advantage compounds at larger model sizes and higher batch utilization.

When the H200 Wins

Models 7B-70B at standard precision. The 141 GB VRAM fits Llama 70B in FP16 on a single GPU. If your workload doesn't exceed that envelope, the H200 handles it efficiently with a mature software stack.

Cost-sensitive inference not saturating FP4. FP4 compute helps when your model and serving framework support it. Older models and frameworks without FP4 support see little benefit from the B200's extra compute. If you're running FP8 or INT8 workloads, there's no benefit to paying for B200 FP4 capability you don't use, and the H200's mature Hopper software stack reduces operational risk.

Existing Hopper deployments. If your team has tuned vLLM configurations, quantization settings, and serving parameters for H100/H200, migration to B200 carries a real cost in engineering time. For stable, mature workloads, the H200 is the lower-risk choice.

Fine-tuning 70B models. QLoRA fine-tuning of a 70B model in FP16 fits in 141 GB on a single H200. One GPU, no tensor parallelism, straightforward setup. The B200 gives more headroom for larger batch sizes, but the H200 covers this use case well.

Rent an H200 on Spheron.

When the B200 Wins

Models requiring 100B+ parameters or large KV caches. At 180 GB VRAM, the B200 handles models that overflow the H200's 141 GB without multi-GPU sharding. For models between 141-180 GB, the B200 is the most cost-efficient single-GPU option. The B300 (288 GB) also fits those sizes, but at a higher cost premium that's only justified if you need the extra headroom.

Workloads benefiting from FP4 quantization. Newer model releases and inference frameworks are increasingly targeting FP4. For Blackwell-optimized models, FP4 roughly doubles effective throughput vs FP8 on the same GPU. See our FP4 quantization guide for real throughput numbers.

High-throughput inference where cost-per-token matters. The benchmark data is clear: for large models, the B200 delivers roughly 3x lower cost-per-token than H200 in FP4 optimized serving, per SemiAnalysis InferenceX benchmarks. If you're running significant daily volume, this compounds quickly.

New deployments in 2026 without existing Hopper infrastructure. Starting fresh on B200 gives you the current-generation software stack and avoids migrating later as Hopper capacity contracts.

Explore B200 GPU rental on Spheron.

When GB200 Makes Sense

Training or inference on 200B+ parameter models. The GB200 NVL72's 72-GPU all-to-all NVLink fabric minimizes inter-GPU communication for models that require massive parallelism. For workloads where tensor parallelism across many GPUs is unavoidable, the tight NVLink topology reduces the overhead.

Massive inference fleets where NVLink-C2C integration reduces host-side bottlenecks. If your inference pipeline is bottlenecked by CPU-GPU data transfers (data preprocessing, tokenization pipelines, CPU-side decoding), the 900 GB/s NVLink-C2C bandwidth directly reduces those bottlenecks. Standard PCIe won't get you there.

Serving reasoning models with enormous KV caches. DeepSeek R1 671B, comparable frontier reasoning models, and chain-of-thought workloads generate KV caches that can consume hundreds of gigabytes per active session. The GB200's 186 GB per chip and tight inter-chip fabric make it the right fit here.

For a multi-node training guide that covers the infrastructure tradeoffs without requiring InfiniBand, see our multi-node GPU training guide.

There is no Spheron GB200 single-GPU rental page. The GB200 is a rack-scale system and is not available as individual GPU slots. For the closest available Blackwell access, see B200 GPU rental or B300 GPU rental. For enterprise GB200 capacity, check current pricing or contact Spheron directly.

The Bottom Line

The decision comes down to three cases:

• Models up to 141 GB and cost-sensitive: H200. At $4.54/hr (as of 17 March 2026), it's the right tool for 7B-70B inference and fine-tuning. Mature software stack, single-GPU 70B serving, no migration cost. Rent an H200 on Spheron.
• Models 141-180 GB, or you need FP4 for throughput at scale: B200. At $6.03/hr, the B200 costs more per hour than the H200 but delivers lower cost-per-token for large models due to superior throughput. Future-proof for FP4 workloads. The clear choice for new deployments running 70B models at volume or anything larger. Explore B200 rentals on Spheron.
• 200B+ models, distributed training, or enterprise reasoning fleets: GB200. Not a per-GPU rental, but the right infrastructure for hyperscale inference and training on the largest models. Talk to Spheron about enterprise capacity or see B300 GPU rental as the closest single-GPU alternative.

Spheron gives you access to H200, B200, and B300 from the same dashboard, no contracts, no quotas. Compare pricing and deploy in minutes. View GPU pricing