H200 vs H100 vs B200 AI Inference: Which GPU Fits Your Workload?

Most GPU selection conversations start with the wrong question. Asking which GPU is most powerful does not help you choose, because the GPU that wins on compute benchmarks may underperform on your specific workload if it is bottlenecked by memory rather than FLOPS.For AI inference, the decision turns on two numbers: how much VRAM a model requires to run at your target precision, and how much memory bandwidth determines your token generation speed.This piece compares the H100, H200, B200, GB200 NVL72, and L40S on those two axes, maps each to the inference scenarios where it earns its price, and shows how GMI Cloud's GPU lineup covers the full range.

Why Inference Is Memory-Bound, Not Compute-Bound

Training workloads are compute-bound. Inference workloads are memory-bound.

During token generation, the GPU must load model weights from HBM on every forward pass. The speed at which it can do that is determined by memory bandwidth, not raw TFLOPS. A GPU with twice the bandwidth generates tokens roughly twice as fast on the same model, regardless of compute throughput differences.

VRAM sets a harder constraint. A model that does not fit in a single GPU's VRAM requires tensor parallelism across multiple GPUs, which adds latency and complexity. Fitting a 70B model on a single GPU versus splitting it across two changes both the operational setup and the cost calculation.

This is why the H100-to-H200 upgrade is almost entirely a memory story. The compute dies are identical. The memory went from 80GB HBM3 at 3.35 TB/s to 141GB HBM3e at 4.80 TB/s. For inference, that is the relevant change.

Specs That Drive the Selection Decision

GPU	VRAM	Memory Bandwidth	Architecture	GMI Cloud On-Demand
L40S	48 GB GDDR6	864 GB/s	Ada Lovelace	Not listed (reference)
H100 SXM5	80 GB HBM3	3.35 TB/s	Hopper	$2.00/hr
H200 SXM5	141 GB HBM3e	4.80 TB/s	Hopper (mem upgrade)	$2.60/hr
B200	180 GB HBM3e	8.0 TB/s	Blackwell	$4.00/hr
GB200 NVL72	13.5 TB (72 GPUs pooled)	130 TB/s (NVLink fabric)	Blackwell rack-scale	$8.00/hr per GPU

The bandwidth progression from H100 to B200 is 3.35 to 8.0 TB/s, a 2.4x increase. For memory-bandwidth-bound inference, this tracks closely with token throughput improvement. The GB200 NVL72 operates differently: its 130 TB/s reflects the all-to-all NVLink fabric across 72 GPUs in a rack, which allows the system to function as a single unified memory domain for models that would otherwise require multi-node tensor parallelism.

Where Each GPU Wins: Scenario-by-Scenario Breakdown

Lightweight inference: 7B to 13B parameter models

The L40S and H100 are the cost-optimal choices here. Models in this size range require 14-30 GB of VRAM at FP16, well within either GPU's capacity. Memory bandwidth at this model size is not the primary bottleneck.

The L40S runs at approximately $0.50-$0.90/hr on cloud platforms. Independent benchmarks show L40S reaching $0.15-$0.25 per million tokens on 7B-13B models, competitive with or better than H100 on a cost-per-token basis for this size range. FP8 quantization on L40S effectively doubles throughput for memory-bound small models.

The H100 at $2.00/hr earns its place when latency requirements are strict(low-batch, real-time serving) or when the same cluster needs to handle both 7B inference and occasional 70B workloads. For pure cost-optimized small-model inference with no latency SLA, L40S is typically more efficient per token dollar.

Standard batch inference: 30B to 70B parameter models

This is the H100's core competitive range. A 70B model in INT8 requires approximately 70 GB of VRAM, fitting tightly on a single H100. With FP8 quantization, H100 roughly doubles throughput without changing GPU count.

The H200 becomes relevant at this model size when:

• You want to run 70B at full precision (Q8 or FP16) without quantization tradeoffs. The H100's 80 GB leaves little KV cache headroom; the H200's 141 GB provides roughly 7x the KV cache capacity at 70B.
• Your batch sizes are large enough that extra bandwidth matters. The H200's 4.80 TB/s versus H100's 3.35 TB/s produces approximately 1.4x faster token generation on bandwidth-bound workloads.

At $2.60/hr versus $2.00/hr, the H200 premium for 70B workloads is 30%. Whether that is justified depends on whether you are hitting the H100's memory ceiling or can achieve your latency target within its bandwidth.

Long-context inference and large-batch workloads: 70B to 180B models

The H200 is the most important GPU in this scenario.Long-context inference at 128K+ token windows grows the KV cache in proportion to sequence length. On an H100, a 70B model with a 128K context window may exhaust VRAM entirely, forcing offloading or truncation. The H200's 141 GB accommodates this with headroom.

For models in the 70B-180B range that require multi-GPU deployment on H100 hardware, the H200 frequently eliminates the need for tensor parallelism. Running a 130B model on a single H200 versus two H100s removes the GPU-to-GPU synchronization overhead that adds latency on every forward pass. The operational simplification also matters: single-GPU deployments are substantially easier to manage than distributed setups.

The B200's 180 GB addresses the upper end of this range. Models above 130B at full precision or models requiring extensive KV cache for very long contexts fit cleanly on a single B200. The B200's 8.0 TB/s bandwidth also makes it the highest-throughput single GPU for large-batch inference.

Very large models and trillion-parameter workloads

The B200 handles 180B models on a single GPU. Beyond that, the GB200 NVL72 is the relevant comparison.

The GB200 NVL72 is a rack-scale system: 72 Blackwell GPUs and 36 Grace CPUs connected by fifth-generation NVLink into a single 13.5 TB unified memory domain. All 72 GPUs communicate at 130 TB/s with no node-crossing latency. NVIDIA's official benchmarks show 30x faster real-time inference on the GPT-MoE 1.8T parameter model compared to H100, and over 1.5 million tokens per second on OpenAI's GPT-OSS model.

For teams serving trillion-parameter models or mixture-of-experts architectures where expert routing generates irregular memory access patterns across large parameter sets, the GB200 NVL72's unified memory and high-bandwidth fabric are not incremental improvements. They are a different class of infrastructure.

At $8.00/hr per GPU on GMI Cloud, accessing 8 GPUs for one hour costs $64. For workloads that require this class of hardware, the alternative is multi-rack H100 or H200 clusters with InfiniBand interconnects, which are both more expensive and architecturally less suited to the memory-access patterns these models require.

Accessing These GPUs Through GMI Cloud

GMI Cloud provides on-demand access to all four GPU tiers with no minimum commitment.

• H100 at $2.00/hr: The entry point for production LLM inference. Covers 7B-70B workloads with a mature software stack (CUDA 12.x, TensorRT-LLM, vLLM pre-configured). The most cost-efficient GPU for teams serving standard model sizes at high utilization.
• H200 at $2.60/hr: The right upgrade when 80 GB VRAM becomes the constraint. Long-context workloads, 70B+ models at full precision, and batch inference where bandwidth matters enough to justify the 30% hourly premium.
• B200 at $4.00/hr: The highest single-GPU throughput available for large-batch and 180B-scale workloads. 2.4x the bandwidth of H200, 180 GB VRAM for models that would otherwise require multi-GPU H200 deployments.
• GB200 NVL72 at $8.00/hr per GPU: Rack-scale infrastructure for trillion-parameter models and workloads that require unified memory across the full GPU domain. Not incremental over the B200 in the same way the B200 improves over the H200. It is a different architecture category.

The pricing progression is not arbitrary.Each step up in GPU tier corresponds to a specific memory constraint being addressed: H100 fits 70B with quantization, H200 fits 70B at full precision and extends to 130B, B200 fits 180B on one GPU, GB200 NVL72 handles the models that fit in none of the above. Choosing the right tier requires knowing which memory constraint your workload actually hits.

GPU access and current availability are atconsole.gmicloud.ai. Pricing details are atgmicloud.ai/en/pricing.

Match the GPU to the Workload Constraint, Not the Prestige

The B200 is not a better choice than the H100 for a team serving a 13B model. The H200 is not automatically the right upgrade for every team currently on H100. The upgrade is justified when the workload hits a specific ceiling: VRAM for model loading, bandwidth for token throughput, or context length for KV cache capacity.

The selection question is: what constraint is limiting your current GPU, and which tier resolves it at the lowest cost increase. That framing produces a more useful answer than asking which GPU is most powerful.