RTX 4090 vs A100: Real-World Benchmark for AI Workloads
Benchmarking the RTX 4090 against the A100 80GB on Llama inference, FLUX image generation, and SDXL throughput — with cost-per-output math.
The RTX 4090 is a $1,600 consumer card. The A100 80GB is a $15,000 datacenter GPU. On paper the A100 should crush the 4090 — but in real AI workloads, the gap is much smaller than the price suggests.
Spec comparison
| RTX 4090 | A100 80GB | |
|---|---|---|
| Architecture | Ada Lovelace | Ampere |
| VRAM | 24 GB GDDR6X | 80 GB HBM2e |
| Memory bandwidth | 1.0 TB/s | 2.0 TB/s |
| FP16 Tensor TFLOPS | 330 | 312 |
| FP8 support | Yes (Ada) | No |
| TDP | 450 W | 400 W |
| Typical rent | $0.34–0.69/hr | $1.19–1.89/hr |
Llama 3.1 8B inference (INT8, batch 1, 512 in / 512 out)
| GPU | Tokens/sec | Latency (full 512 out) |
|---|---|---|
| RTX 4090 | 142 t/s | 3.6s |
| A100 80GB | 168 t/s | 3.0s |
A100 is 18% faster, but ~3.5× the price.
Llama 3.1 8B inference (INT8, batch 32)
| GPU | Aggregate tok/s |
|---|---|
| RTX 4090 | ~2,950 |
| A100 80GB | ~5,400 |
At higher batch sizes the A100's bandwidth advantage shows. A100 ~1.8× the throughput, ~3.5× the cost — 4090 still wins on tokens-per-dollar.
FLUX.1 [dev], 1024×1024, 20 steps
| GPU | Seconds/image |
|---|---|
| RTX 4090 | 6.5s |
| A100 80GB | 5.0s |
A100 ~30% faster — same cost story.
SDXL 1.0, 1024×1024, 30 steps
| GPU | Seconds/image |
|---|---|
| RTX 4090 | 4.1s |
| A100 80GB | 3.5s |
Verdict
The RTX 4090 is the cost-per-output king across nearly every consumer-AI workload that fits in 24 GB. The A100's only structural wins are:
- Models that don't fit in 24 GB (70B+ LLMs without aggressive quantization).
- Multi-GPU jobs that need NVLink.
- Production environments where datacenter SLA matters more than $/hr.
Otherwise: rent 4090s.