CDNA 4
AMD Instinct MI355X
wins
3
The AMD Instinct MI355X is AMD's CDNA 4 flagship.
Hopper
NVIDIA H100 NVL
wins
2
The H100 NVL is a PCIe form-factor Hopper GPU with 94GB HBM3 and NVLink support.
Performance comparison
Visual bars — winner highlighted
AMD Instinct MI355X
Metric
NVIDIA H100 NVL
FP16 TFLOPS
Bandwidth
VRAM
TDP (lower=better)
TFLOPS/watt
GB/s per watt
Full specification table
| Spec | AMD Instinct MI355X | NVIDIA H100 NVL |
|---|---|---|
| Architecture | CDNA 4 | Hopper |
| VRAM | 288 GB HBM3e | 94 GB HBM3 |
| VRAM type | HBM3e | HBM3 |
| Memory bandwidth | 8,000 GB/s | 3,938 GB/s |
| FP16 TFLOPS | 2,615 TFLOPS | 1,979 TFLOPS |
| TDP | 750 W | 400 W |
| TFLOPS/watt | 3.49 T/W | 4.95 T/W |
| GB/s per watt | 10.67 GB/s/W | 9.85 GB/s/W |
| NVLink | No | Yes |
| Release year | 2025 | 2023 |
Live cloud pricing
On-demand pricing across providers — updated in real time
AMD Instinct MI355X
No live pricing data available.
NVIDIA H100 NVL
Power efficiency analysis
TFLOPS/watt and GB/s/watt — critical for data center TCO
AMD Instinct MI355X
NVIDIA H100 NVL
TFLOPS/watt measures compute efficiency — how much AI throughput you get per watt of power consumed. For data centers with PUE of 1.2–1.5, a 10% improvement in TFLOPS/watt translates directly to lower electricity costs and cooling requirements. GB/s/watt measures memory bandwidth efficiency, which is the binding constraint for memory-bound LLM inference workloads.
Performance-per-dollar
TFLOPS/$ and GB/s/$ at current best cloud prices
AMD Instinct MI355X
NVIDIA H100 NVL
TFLOPS/$ = FP16 TFLOPS ÷ best on-demand $/hr. Higher is better — more compute per dollar. GB/s/$ = memory bandwidth ÷ best on-demand $/hr. For memory-bound inference workloads (most LLM serving), GB/s/$ is often a better predictor of cost-efficiency than TFLOPS/$. Prices are live from the Compute Comparison API and update in real time.
Workload Cost Comparison
Total job cost = best on-demand $/hr × GPU count × hours
| Workload | Scale | AMD Instinct MI355X | NVIDIA H100 NVL |
|---|---|---|---|
| 7B LLM fine-tune | 8× GPU, 24 h | — | $632 |
| 70B LLM fine-tune | 64× GPU, 72 h | — | $15k |
| Inference server | 1× GPU, 730 h | — | $2.4k |
| Image gen batch | 4× GPU, 8 h | — | $105 |
Based on current best on-demand prices. Spot pricing typically 40–70% lower. Actual cost depends on provider, region, and availability.
When to choose each GPU
Choose AMD Instinct MI355X for:
- Large LLM inference
- Agent workloads
- Memory-bound AI
Weaknesses / Limitations
- ROCm ecosystem less mature than CUDA — some frameworks require porting
- Very limited cloud availability compared to NVIDIA equivalents
- 750W TDP — high power draw comparable to H100 SXM
Choose NVIDIA H100 NVL for:
- LLM inference
- PCIe server deployments
- Dual-GPU NVLink pairs
Weaknesses / Limitations
- Lower FP16 TFLOPS (1,979 per dual-GPU) than SXM5 per-card
- Requires paired NVLink configuration — not a single-card solution
- Limited availability compared to standard H100 SXM
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