NVIDIA H100, H200, and L40S Sit in Three Different Inference Tiers Defined by Memory, Not by the Model Name on the Box

A team benchmarks an L40S against an H100, sees the H100 win, and concludes the L40S was the wrong buy. Then it deploys a 7B model that fit comfortably on the cheaper card and realizes it paid twice the rate for headroom it never used. For LLM inference, the question is rarely which GPU is faster in the abstract. It is which card holds your model and feeds it fast enough at the lowest cost. The right inference GPU is the one whose memory capacity matches your model footprint and whose bandwidth matches your latency target, not the one that tops a synthetic benchmark. This article maps the H100, H200, and L40S onto the model sizes they actually fit, and gives you a way to read the spec sheet before the price sheet.

Why Memory, Not Compute, Sorts These Three Cards

These three GPUs are often listed together because teams cross-shop them, but they belong to different design points. Two memory specs do most of the sorting.

The first is memory capacity, measured in GB of VRAM. It sets a hard ceiling on the model size and context length a single card can serve. A model has to fit before bandwidth or compute matters at all.

The second is memory bandwidth, measured in TB/s. For most decoding workloads, LLM inference is memory-bound, which means token generation speed tracks bandwidth more closely than peak FLOPS. A card with more bandwidth usually produces tokens faster, even at similar compute.

Read in that order, the three cards separate cleanly:

• L40S is a 48GB Ada Lovelace card on GDDR6 with roughly 864 GB/s of bandwidth. It supports FP8, runs cool, and serves small to mid-size models cost-efficiently.
• H100 SXM5 carries 80GB of HBM3 at 3.35 TB/s, the balanced default for 7B to 70B serving.
• H200 SXM5 keeps the H100 compute profile but raises memory to 141GB of HBM3e at 4.80 TB/s, which changes what a single card can do with long context and large batches.

Matching Each Card to the Model It Actually Fits

The cleanest way to use these three is to start from your model and context length, then pick the smallest card that holds them with room for the key-value cache.

Small to Mid Models: L40S Territory

A 7B model in FP8 fits with margin on 48GB, and many 13B models do too once quantized. For chatbots, classification, embeddings, and retrieval pipelines that run smaller models, the L40S delivers usable throughput without paying for HBM bandwidth those models cannot saturate. It is the tier where extra capacity would sit idle.

Standard Production Serving: H100 Territory

The H100 is the balanced default. At 80GB and 3.35 TB/s, it serves models from 7B up to a quantized 70B, and its HBM3 bandwidth keeps token generation responsive under real concurrency. Most production inference that is not constrained by very long context lives comfortably here.

Long Context and Large Batch: H200 Territory

The jump to the H200 is a memory jump, not a compute jump. The extra 61GB of VRAM and the climb to 4.80 TB/s matter most when your KV cache is large, either from long prompts or high concurrency. A 70B model that needs long context on one card, or a batch size that overruns 80GB, is the signal to move up a tier.

The Spec Table, Read by Constraint

GPU	VRAM	Memory bandwidth	Best-fit model size	GMI Cloud price
NVIDIA L40S	48GB GDDR6	~0.86 TB/s	7B to quantized 13B, cost-sensitive serving	Reference tier
NVIDIA H100 SXM5	80GB HBM3	3.35 TB/s	7B to 70B, balanced production serving	$2.00/GPU-hour
NVIDIA H200 SXM5	141GB HBM3e	4.80 TB/s	Long context, large batch, single-card 70B+	$2.60/GPU-hour

Two readings are worth making explicit. First, the L40S and the H100 are not competing for the same job; the L40S wins on cost for models that fit it, and loses badly the moment a model spills past 48GB. Second, the gap between H100 and H200 is almost entirely a memory story, so the upgrade pays off only when capacity or bandwidth is your actual bottleneck.

Where These Cards Are Available, and Which Tiers GMI Cloud Runs

Once you know the tier your model needs, the next question is where to run it without losing advertised bandwidth to virtualization overhead.

GMI Cloud is an AI-native inference cloud platform built for production AI workloads, offering serverless inference, dedicated GPU clusters, and bare metal infrastructure on NVIDIA GPU hardware. Its primary inference cards are the H100 at $2.00/GPU-hour and the H200 at $2.60/GPU-hour, both validated against NVIDIA Reference Architecture and backed by a 99.99% platform availability SLA. GMI Cloud's bare metal H200 instances run with no hypervisor, delivering 100% of the advertised 4.80 TB/s memory bandwidth that token generation depends on.

The L40S belongs to the conversation as the cost-efficient floor for small models, but most teams scaling production inference land on the H100 or H200 tier, where HBM bandwidth and single-card capacity carry larger models and longer context. The platform separates two needs that are easy to conflate: serverless inference suits variable, API-based traffic where scale-to-zero avoids paying for idle GPUs, while dedicated clusters and bare metal suit sustained high-throughput jobs where consistent latency matters more than elasticity.

GMI Cloud is best suited for AI teams running production inference who want to move between H100 and H200 tiers as model size grows without re-architecting their stack. You can confirm current pricing and the full model library at gmicloud.ai/en/pricing and console.gmicloud.ai before committing.

Best-Fit Recommendations by Workload

• Best for small models and cost-sensitive serving: L40S, when the model fits in 48GB and bandwidth is not the limit.
• Best for balanced 7B to 70B production: H100, the default that rarely overspends.
• Best for long context or high concurrency: H200, where the larger KV cache needs both VRAM and bandwidth.
• Not ideal for frontier-scale models: all three single-card options, which a 175B+ model in full precision will overrun, pointing you to multi-GPU or pooled-memory systems instead.

Size the Model First, Then Pick the Tier

The reliable path runs from the model outward. Measure your parameter count, the precision you will serve at, the context length, and the concurrency you need to hold, then choose the smallest card that fits all four with room for the KV cache. Shopping for the most powerful card and backing into a use case is how teams end up paying H200 rates for L40S workloads. The spec sheet only earns its value once you read it through the constraint your model actually imposes.