GPU Cloud Pricing Comparison: A100 vs H100 vs H200

The H100 is the balanced workhorse for most production AI workloads, and the H200 is the right call once you're running 70B+ parameter models where decode speed bottlenecks your cost.

If you've been staring at GPU cloud pricing pages and wondering why the numbers feel disconnected from actual performance, that's because raw hourly rates only tell half the story.

GMI Cloud offers both H100 SXM and H200 SXM on-demand and reserved, giving you a direct apples-to-apples environment to run your own benchmarks.

Why GPU Cloud Pricing Comparisons Mislead Without Performance Context

A $2.10/GPU-hour H100 and a $1.50/GPU-hour A100 don't compete on the same playing field. The A100 delivers 624 INT8 TOPS; the H100 delivers 3,958 INT8 TOPS — roughly 6x the throughput at 1.4x the price (Source: NVIDIA A100 Tensor Core GPU Datasheet; NVIDIA H100 Tensor Core GPU Datasheet, 2023).

That gap means your cost-per-output-token on the H100 can easily beat the A100 even though the hourly rate is higher.

The mistake engineers make is optimizing for GPU-hours instead of optimizing for work completed per dollar. A GPU sitting at 40% utilization on a cheap instance is more expensive per useful token than a GPU at 85% utilization on a premium one.

This framing becomes even more important when you're comparing three generations of silicon with radically different memory bandwidth profiles.

Full Pricing and Spec Comparison

The table below combines verified hardware specs with current cloud pricing. Benchmark sources are cited inline. Check gmicloud.ai/pricing for current rates, as spot and reserved pricing changes frequently.

GPU	VRAM	Memory BW	FP16 TFLOPS	INT8 TOPS	Approx. On-Demand Price
H200 SXM	141 GB HBM3e	4.8 TB/s	989	3,958	~$2.60/GPU-hr
H100 SXM	80 GB HBM3	3.35 TB/s	989	3,958	~$2.00/GPU-hr
A100 80GB	80 GB HBM2e	2.0 TB/s	312	624	~$1.50–1.80/GPU-hr
L4	24 GB GDDR6	300 GB/s	121	485	~$0.50–0.80/GPU-hr

Sources: NVIDIA H100 Tensor Core GPU Datasheet (2023); NVIDIA H200 Tensor Core GPU Product Brief (2024); NVIDIA A100 Tensor Core GPU Datasheet; NVIDIA L4 Tensor Core GPU Datasheet. Cloud pricing is approximate; check gmicloud.ai/pricing for current rates.

A few things jump out immediately. The H200 and H100 share identical compute specs — 989 FP16 TFLOPS and 3,958 INT8 TOPS — but the H200 packs 141 GB of HBM3e versus 80 GB of HBM3, and delivers 4.8 TB/s of memory bandwidth versus 3.35 TB/s.

That bandwidth difference is why the H200 achieves up to 1.9x inference speedup on Llama 2 70B compared to the H100 (NVIDIA official benchmark, TensorRT-LLM, FP8, batch 64, 128/2048 tokens — NVIDIA H200 Tensor Core GPU Product Brief, 2024).

Both GPUs support NVLink 4.0 at 900 GB/s bidirectional aggregate per GPU on HGX/DGX platforms.

Performance-Per-Dollar Analysis

Raw hourly pricing doesn't map to cost efficiency until you bring throughput into the equation. Here's a simplified $/TFLOPS breakdown based on published specs and approximate on-demand rates.

GPU	FP16 TFLOPS	Approx. $/GPU-hr	$/TFLOPS (FP16)
H200 SXM	989	~$2.60	~$0.00263
H100 SXM	989	~$2.00	~$0.00202
A100 80GB	312	~$1.65 avg	~$0.00529
L4	121	~$0.65 avg	~$0.00537

Check gmicloud.ai/pricing for current rates. The H100 wins on raw $/TFLOPS. The H200 costs more per TFLOP of compute, but that framing misses the point: the H200's advantage is memory capacity and bandwidth, not raw compute. For large-model inference, the bottleneck is almost always memory, not FLOPS.

For a practical $/output-token estimate on Llama 2 70B in FP16, a single H100 at 80 GB VRAM can just barely fit the model with minimal KV-cache headroom. An H200's 141 GB gives you comfortable room for longer contexts and larger batch sizes, which drives up tokens-per-second and reduces effective cost per output token.

The H200's 1.9x inference speedup directly translates to roughly 1.9x more tokens delivered per GPU-hour at equivalent quality.

When to Upgrade: A100 to H100, or H100 to H200

If you're still on A100s and wondering whether to move, the math is straightforward. The A100 has no native FP8 support and delivers 624 INT8 TOPS (Source: NVIDIA A100 Tensor Core GPU Datasheet).

The H100 delivers 3,958 INT8 TOPS with native FP8 at 1,979 TFLOPS — roughly 6x more throughput for inference-optimized workloads. If your batches are large and your models are quantized, you'll see the gap immediately.

You should upgrade from A100 to H100 when: - You're running 13B to 70B parameter models - You're serving multiple concurrent users with dynamic batching - Your A100 utilization is consistently above 70% (signal: you're memory-constrained) - You're paying for multiple A100 GPUs to fit a model that one H100 could handle

The jump from H100 to H200 is warranted in different scenarios. The compute specs are identical, so don't upgrade for FLOP-count reasons. Upgrade for memory.

If you're running 70B+ models, need sequences longer than 8K tokens, or want to increase concurrent request batching without model sharding, the H200's 141 GB gives you the headroom to do it. The 4.8 TB/s bandwidth vs. 3.35 TB/s on the H100 means decode-bound workloads run meaningfully faster.

The L4 is a different category entirely. It's a per-inference cost optimizer for smaller models — sub-7B, low-latency, single-user scenarios — not a scaled production GPU for large LLMs.

TCO Considerations: Utilization, On-Demand vs. Reserved

Your total cost of ownership isn't just the GPU hourly rate. It's GPU utilization × cost × time, plus the hidden costs of idle capacity, cold-start latency, and ops overhead. A GPU sitting at 30% utilization is wasting 70% of your spend.

On-demand pricing gives you flexibility — pay only when you run workloads, no commitments. Reserved pricing typically cuts hourly rates by 30–50% in exchange for a time commitment (monthly or yearly).

If you have predictable inference workloads running more than 12–16 hours per day, reserved instances often deliver better TCO than on-demand even after accounting for utilization variance.

Here's the thing: multi-GPU scaling also affects your TCO calculation. On a single 8-GPU H100 node, NVLink 4.0 runs at 900 GB/s bidirectional aggregate per GPU on HGX/DGX platforms.

For models requiring tensor parallelism across GPUs (70B+ in full precision), that bandwidth determines how efficiently GPUs communicate — and whether you're paying for 8 GPUs or just using them like 8 isolated cards.

Inter-node communication on InfiniBand matters for training or multi-node inference, but intra-node NVLink is what makes large inference practical.

GMI Cloud Pricing Anchor

GMI Cloud currently offers H100 SXM at approximately $2.00/GPU-hour and H200 SXM at approximately $2.60/GPU-hour, both on-demand and reserved.

Each node ships with 8 GPUs, NVLink 4.0 at 900 GB/s bidirectional aggregate per GPU (HGX/DGX), and 3.2 Tbps InfiniBand for inter-node connectivity. The environment comes pre-configured with CUDA 12.x, cuDNN, NCCL, TensorRT-LLM, vLLM, and Triton Inference Server.

Check gmicloud.ai/pricing for current rates before committing to a plan.

FAQ

Is the H200 worth the premium over the H100? For models 70B and above, or workloads with long context windows, yes. The H200's 141 GB HBM3e and 4.8 TB/s bandwidth make it 1.9x faster on Llama 2 70B inference (NVIDIA H200 Tensor Core GPU Product Brief, 2024).

For sub-30B models, the H100 at a lower price point usually wins on $/output-token.

Can I run Llama 2 70B on a single H100? Barely, in FP16 — 80 GB fits the weights, but leaves almost no room for KV-cache. In practice, you'd want FP8 quantization or an H200 for comfortable headroom and longer sequences.

Is the A100 still worth using in 2026? For smaller models under 13B, or cost-sensitive batch jobs where latency doesn't matter, yes. For production inference on modern LLMs, the H100 or H200 typically delivers better cost-per-token despite the higher hourly rate.

What's the difference between on-demand and reserved GPU pricing? On-demand lets you spin up and tear down without commitment. Reserved pricing locks in capacity for a defined term in exchange for a lower hourly rate, often 30–50% cheaper. Check gmicloud.ai/pricing for current rates on both tiers.

Does NVLink matter for inference? It matters for large-model inference that requires tensor parallelism across multiple GPUs. NVLink 4.0 at 900 GB/s bidirectional aggregate per GPU (HGX/DGX) allows GPUs to share model shards efficiently.

For single-GPU inference on models that fit in VRAM, NVLink isn't a factor.