A100 80GB vs H100 80GB
Specs, price, and performance compared
Specs
▲ indicates the higher value for that spec. TDP: lower is better for density.
A100 80GB vs H100 80GB: Workload Fit
The A100 80GB is built on the GA100 architecture (Ampere, TSMC 7nm), while the H100 80GB uses GH100 (Hopper, TSMC 4N). The H100 80GB delivers 1979 TFLOPS of FP16/BF16 throughput — 6.3× the 312 TFLOPS of the A100 80GB. The H100 80GB adds FP8 support at 3958 TFLOPS — roughly 2.0× its FP16 rate — enabling higher throughput for inference workloads that tolerate reduced precision. The A100 80GB does not support FP8.
Memory capacity and bandwidth are the primary constraints for large language model workloads. The A100 80GB provides 80GB of HBM2e — the same capacity as the H100 80GB, though in HBM3. Memory bandwidth favors the H100 80GB at 3.4 TB/s — 1.7× the 2.0 TB/s of the A100 80GB. Both support NVLink: the A100 80GB at 600 GB/s and the H100 80GB at 900 GB/s bidirectional.
Power consumption shapes rack density and operating cost. The A100 80GB draws 400W TDP versus 700W for the H100 80GB. On a TFLOPS-per-watt basis, the H100 80GB is the more efficient option at 3.6× the compute density of the A100 80GB, delivering 1979 TFLOPS at 700W.
The A100 80GB is best suited for ML training, Large model inference, HPC workloads. The H100 80GB targets LLM training, Large-scale inference, Scientific HPC. For high-density inference racks where power budget is the constraint, the A100 80GB's 400W envelope allows more cards per rack than the H100 80GB's 700W. For throughput-bound inference, the H100 80GB's higher TFLOPS ceiling gives it an advantage when the model fits in either card's VRAM.
Workload Cost Comparison
Total job cost = observed median $/hr × GPU count × hours. Spot pricing typically 40–70% lower.
The H100 80GB costs 1.7× more than the A100 80GB at current observed median prices. Memory bandwidth is 1.7× higher on the H100 80GB (3350 GB/s vs 2000 GB/s), which directly improves token throughput for memory-bound LLM inference. The H100 80GB delivers 6.3× the FP16 compute of the A100 80GB (1979 vs 312 TFLOPS), supporting faster training iteration and higher batch throughput.
Based on observed on-demand median prices. Actual cost depends on provider, region, and availability.
Strengths & Weaknesses
- Lower power draw at 400W vs 700W — fits more cards per rack
- Lower memory bandwidth than the H100 80GB (2000 GB/s vs 3350 GB/s)
- Lower FP16 throughput than the H100 80GB (312 vs 1979 TFLOPS)
- No FP8 support; the H100 80GB offers FP8 at 3958 TFLOPS
- Higher memory bandwidth (3350 GB/s vs 2000 GB/s on the A100 80GB)
- 6.3× the FP16 compute throughput of the A100 80GB (1979 vs 312 TFLOPS)
- FP8 precision supported (3958 TFLOPS) — the A100 80GB does not support FP8
- Newer architecture (GH100, 2022) vs GA100 (2020)
- Higher power draw at 700W vs 400W on the A100 80GB
- ML training
- Large model inference
- HPC workloads
- Older architecture — no FP8 support
- Higher hourly cost than newer alternatives like L40S for inference
- Approaching end-of-life as H100/H200 become standard
- LLM training
- Large-scale inference
- Scientific HPC
- Highest cost per hour of widely available GPUs
- SXM5 form factor requires specialized server infrastructure
- Overkill for inference-only workloads where A100 suffices
For a deeper training-focused analysis, see the full guide:
H100 vs A100: Which GPU for LLM Training?