The trade-off between A100 vs H100 price to performance inference is not a simple multiplier on spec sheets. For engineers running LLMs in production, the real cost per token depends on whether your workload can exploit H100’s FP8 and higher memory bandwidth, or if it is memory-bound on both GPUs anyway.
Capabilities
Compute architecture
A100 (Ampere, sm_80) launched in 2020 with up to 624 TFLOPS FP16 tensor. H100 (Hopper, sm_90) shipped in 2022 with revised tensor cores and a dedicated Transformer Engine. The headline difference is precision: H100 adds native FP8 E4M3/E5M2 paths that roughly double matrix throughput for supported layers. A100 tops out at FP16/BF16 and INT8.
Memory subsystem
The 80GB A100 uses HBM2e at ~2 TB/s. H100 80GB uses HBM3 at ~3 TB/s. That 50% bandwidth increase matters for decode-bound serving, where each token generation streams weights from memory. H100 SXM also moves to PCIe Gen5 host links and faster NVLink, reducing all-reduce overhead in tensor-parallel setups.
Power and form factor
A100 SXM draws 400W; H100 SXM hits 700W. Both come in PCIe variants with lower TDP. Older clusters overwhelmingly have A100; H100 requires newer HGX baseboards and CUDA 12+ drivers. If you are on a three-year-old Kubernetes fleet, A100 is what you already own.
Price and Cost Model
Cloud on-demand rates put H100 at a clear premium over A100 80GB. The exact ratio shifts with region and commitment, but H100 consistently costs multiples more per GPU-hour. For a fixed inference budget, the A100 vs H100 price to performance inference equation only favors H100 if the throughput gain exceeds the cost ratio.
On-prem, A100 capex is largely amortized in many orgs. H100 nodes carry higher upfront cost and power overhead. If you meter per token at the application layer, hardware choice is invisible until you hit saturation:
# per-token cost is independent of GPU if the gateway abstracts it
from openai import OpenAI
client = OpenAI(base_url="https://api.n4n.ai/v1", api_key="KEY")
resp = client.chat.completions.create(
model="meta-llama/llama-3-70b-instruct",
messages=[{"role": "user", "content": "Explain attention."}]
)
# client pays per token, not per GPUA gateway that forwards provider health and honors routing directives lets you shift traffic when H100 pools are constrained.
Latency and Throughput
LLM inference splits into prefill (compute-bound) and decode (memory-bound). H100 wins prefill on raw FLOPS and FP8. For decode, the bandwidth gap yields modest single-stream latency improvements—often 10–20% faster time-to-first-token under identical batch size.
The real throughput story is batching. At batch 64+ with FP8 weights, H100 sustains far more concurrent sequences per GPU than A100. For a 70B model at BF16, two A100 80GB cards fit with tensor parallelism 2; two H100s do the same with headroom for larger KV caches. For 180B+ models, A100 needs 4–8 way TP; H100 drops that to 2–4, cutting inter-GPU sync.
Ergonomics
Both expose standard CUDA interfaces. Code changes are minimal if your stack already uses PyTorch or vLLM:
# check compute capability before building custom kernels
nvidia-smi --query-gpu=name,compute_cap --format=csv
# A100 -> 8.0, H100 -> 9.0A100 runs comfortably on CUDA 11.8 or 12.x. H100 demands CUDA 12.0+ and newer container bases. Driver drift is the most common friction when mixing fleets. MIG on A100 is mature; H100 supports MIG but few LLM serving stacks use it because single large models need the full die.
Ecosystem
A100 has universal support: DeepSpeed, Megatron, TensorRT-LLM, vLLM, and every custom CUDA extension. H100 support is now solid in flagship frameworks but lags in niche kernels. FlashAttention-2 runs on both; FlashAttention-3 extracts extra H100 performance but is newer.
If you depend on a research-grade kernel or an older Triton version, A100 is lower risk. H100 pays off when your serving path is optimized for Transformer Engine and FP8 quant.
Limits
A100’s 80GB ceiling forces aggressive tensor parallelism for 180B models or 128k contexts. H100’s same 80GB cap still bites, but higher bandwidth delays the cliff. Neither GPU solves multi-hundred-GB model hosting alone; both require pipeline parallelism or offloading.
Thermal and power density limit H100 density per rack. A100’s lower TDP makes it easier to pack in existing colo. Both are constrained by PCIe topology in non-SXM nodes—NVLink bridges are mandatory for high TP efficiency.
Head-to-Head Comparison
| Dimension | A100 80GB | H100 80GB |
|---|---|---|
| Memory bandwidth | ~2 TB/s HBM2e | ~3 TB/s HBM3 |
| Native precisions | FP16, BF16, INT8 | + FP8, Transformer Engine |
| Typical cloud cost | Lower per GPU-hour | 2–3x premium |
| 70B BF16 fit | 2-GPU TP | 2-GPU TP, more KV headroom |
| Framework maturity | Universal | Catching up, FP8 paths young |
| Power draw (SXM) | 400W | 700W |
| Best workload | Steady BF16, cost-sensitive | Batched FP8, high-throughput |
Which to Choose
Cost-sensitive startup or spiky traffic
Use A100. The A100 vs H100 price to performance inference gap favors the older card when you cannot hold large batches or your models are sub-70B at BF16. Amortized on-prem A100 fleets are effectively free marginal cost.
Paid API with steady high QPS
Pick H100. If you can drive batch sizes above 32 and adopt FP8, the token-per-dollar curve crosses A100 despite the hourly premium. The Transformer Engine is the lever.
Long-context or 180B+ serving
H100 reduces tensor-parallel degree and improves prefill. If you must serve a 180B model at 128k context with low TTFT, H100’s bandwidth and FP8 are pragmatic despite cost.
Mixed fleet or capacity uncertainty
Run both. Default to A100, overflow to H100 on rate limits. An inference gateway that honors client routing and forwards cache-control hints makes this transparent without client changes. For teams using n4n.ai, the same OpenAI-compatible call reaches either pool.
Prototyping and research
A100 is easier to find on spot markets and older notebooks. H100 is worth requesting when you specifically benchmark FP8 kernels; otherwise the ergonomic difference is negligible until production scale.