H100 SXM5 vs T4
NVIDIA H100 SXM5 (Hopper, 80 GB) against NVIDIA T4 (Turing, 16 GB): memory, compute, power and rental price, compared for LLM inference and training.
Pick two GPUs to compare
Side-by-Side Specifications
| Spec | H100 SXM5 | T4 |
|---|---|---|
| Architecture | Hopper | Turing |
| Memory | 80 GB HBM3 | 16 GB GDDR6 |
| Memory bandwidth | 3,350 GB/s | 320 GB/s |
| FP16 tensor compute | 1,979 TFLOPS | 65 TFLOPS |
| INT8 tensor compute | 3,958 TOPS | 130 TOPS |
| Interconnect | NVLink 4.0 · 900 GB/s | PCIe 3.0 · 32 GB/s |
| TDP | 700 W | 70 W |
| Est. on-demand price | ~$8.00/h | ~$0.50/h |
| FP16 TFLOPS per $/h | 247 | 130 |
Highlighted values indicate the stronger spec. Hourly rates are indicative on-demand estimates.
Verdict
Raw performance: The H100 SXM5 leads on FP16 tensor compute (30.4x advantage), which translates directly into higher token throughput for inference and shorter training steps.
Memory: With 80 GB per card, the H100 SXM5 fits larger models on fewer GPUs — fewer cards means less inter-GPU communication and simpler deployments.
Value: At current on-demand rates, the H100 SXM5 delivers more compute per dollar (247 vs 130 FP16 TFLOPS per $/h). If your model fits in its VRAM budget, it is usually the more economical choice.
GPUs Needed for Popular LLMs
Cards required to serve each model at 8-bit quantization (with 20% overhead for activations and KV cache).
| Model | VRAM (8-bit) | H100 SXM5 | T4 |
|---|---|---|---|
| GPT-5.6 Sol | 2682 GB | 34x | 168x |
| DeepSeek V4 Pro (671B) | 750 GB | 10x | 47x |
| Muse Spark 1.1 | 335 GB | 5x | 21x |
| Claude 5 Sonnet (175B) | 196 GB | 3x | 13x |
| Nova Premier (80B) | 89 GB | 2x | 6x |
| Nova Core (34B) | 38 GB | 1x | 3x |
| Nova Lite (12B) | 13 GB | 1x | 1x |
| Phi 3.5 (3.8B) | 4 GB | 1x | 1x |
Frequently Asked Questions
Which is better for LLM inference: H100 SXM5 or T4?
The H100 SXM5 delivers more raw FP16 compute (1,979 TFLOPS) and the H100 SXM5 offers the most memory per card (80 GB). For cost-efficiency, the H100 SXM5 currently gives more FP16 TFLOPS per dollar of on-demand rental (247 vs 130 TFLOPS per $/h).
How much more memory does the H100 SXM5 have?
The H100 SXM5 has 80 GB of HBM3 versus 16 GB of GDDR6 for the T4 — a ratio of 5.00x in favor of the H100 SXM5. More VRAM per card means fewer GPUs to fit a given model.
Is the H100 SXM5 or the T4 cheaper to rent?
Estimated on-demand rates are ~$8.00/h for the H100 SXM5 and ~$0.50/h for the T4. Raw hourly price is only part of the story: normalize by throughput (TFLOPS per $/h) and by how many cards you need for your model's VRAM.
How do the H100 SXM5 and T4 compare on power?
The H100 SXM5 has a TDP of 700W versus 70W for the T4. FP16 compute per watt: 2.8 vs 0.9 TFLOPS/W.
Deploy on a GPU cloud
Rent the H100 SXM5 or T4 by the hour instead of buying hardware.
