H100 80GB vs RTX 4090
Specs, price, and performance compared
Specs
▲ indicates the higher value for that spec. TDP: lower is better for density.
H100 80GB vs RTX 4090: Workload Fit
The H100 80GB is built on the GH100 architecture (Hopper, TSMC 4N), while the RTX 4090 uses AD102 (Ada Lovelace, TSMC 4N). The H100 80GB delivers 1979 TFLOPS of FP16/BF16 throughput — 12.0× the 165.2 TFLOPS of the RTX 4090. 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 RTX 4090 does not support FP8.
Memory capacity and bandwidth are the primary constraints for large language model workloads. The H100 80GB provides 80GB of HBM3 versus 24GB of GDDR6X on the RTX 4090. Memory bandwidth favors the H100 80GB at 3.4 TB/s — 3.3× the 1.0 TB/s of the RTX 4090. The H100 80GB supports NVLink at 900 GB/s bidirectional, enabling multi-GPU tensor parallelism; the RTX 4090 is PCIe-only with no VRAM pooling across cards.
Power consumption shapes rack density and operating cost. The RTX 4090 draws 450W TDP versus 700W for the H100 80GB. On a TFLOPS-per-watt basis, the H100 80GB is the more efficient option at 7.7× the compute density of the RTX 4090, delivering 1979 TFLOPS at 700W.
The H100 80GB is best suited for LLM training, Large-scale inference, Scientific HPC. The RTX 4090 targets Fine-tuning small models, Inference up to 13B, Cost-sensitive workloads. For models that exceed the smaller card's VRAM, the H100 80GB's 80GB is the deciding factor — a 80GB card can hold roughly 40B parameters at FP16 or 160B at INT4.
Workload Cost Comparison
Total job cost = observed median $/hr × GPU count × hours. Spot pricing typically 40–70% lower.
The H100 80GB costs 5.1× more than the RTX 4090 at current observed median prices. The H100 80GB's 80GB VRAM (vs 24GB on the RTX 4090) allows larger models and batch sizes without offloading, which commands a premium in the cloud market. Memory bandwidth is 3.3× higher on the H100 80GB (3350 GB/s vs 1008 GB/s), which directly improves token throughput for memory-bound LLM inference.
Based on observed on-demand median prices. Actual cost depends on provider, region, and availability.
Strengths & Weaknesses
- 3× the VRAM of the RTX 4090 (80GB vs 24GB)
- Higher memory bandwidth (3350 GB/s vs 1008 GB/s on the RTX 4090)
- 12.0× the FP16 compute throughput of the RTX 4090 (1979 vs 165.2 TFLOPS)
- FP8 precision supported (3958 TFLOPS) — the RTX 4090 does not support FP8
- NVLink support (900 GB/s bidirectional) enables multi-GPU tensor parallelism; the RTX 4090 is PCIe-only
- Higher power draw at 700W vs 450W on the RTX 4090
- Lower power draw at 450W vs 700W — fits more cards per rack
- Less VRAM than the H100 80GB (24GB vs 80GB)
- Lower memory bandwidth than the H100 80GB (1008 GB/s vs 3350 GB/s)
- Lower FP16 throughput than the H100 80GB (165.2 vs 1979 TFLOPS)
- No FP8 support; the H100 80GB offers FP8 at 3958 TFLOPS
- PCIe-only — no NVLink; the H100 80GB supports 900 GB/s NVLink
- 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
- Fine-tuning small models
- Inference up to 13B
- Cost-sensitive workloads
- Consumer GPU — no ECC memory, not suited for production data centers
- No NVLink — cannot pool VRAM across cards
- Only 24GB VRAM — limits to ~13B models at FP16