microsoft/Phi-3-medium-4k-instruct
14B params · est. 8.4 GB Q4
NVIDIA H100 SXM5 80GB: likely fitNVIDIA A100 SXM4 80GB: likely fit
Head-to-head
NVIDIA H100 SXM5 80GB vs NVIDIA A100 SXM4 80GB
Dedicated comparison page for two real hardware profiles. This is built from your benchmark database, trust metadata, and buyer flow instead of generic spec-sheet comparisons.
Device profile
NVIDIA H100 SXM5 80GB
70B FP16 with substantial context, 405B Q4 with headroom. Production multi-tenant serving at batch 8-64 via TensorRT-LLM or vLLM.
Curated Aggregate·2026-04-0980GB / $25k
Best LLM
612 tok/s
VRAM
80 GB
TDP
700 W
Rating
9.4
Device profile
NVIDIA A100 SXM4 80GB
Compared here against NVIDIA H100 SXM5 80GB.
Curated Aggregate·2025-05-0180GB / $15k
Best LLM
215 tok/s
VRAM
80 GB
TDP
400 W
Rating
9.0
Quick verdict
NVIDIA H100 SXM5 80GB scores 9.4 while NVIDIA A100 SXM4 80GB scores 9.0 on MyAIHardware's composite rating.
VRAM capacity is tied.
NVIDIA A100 SXM4 80GB is the lower-power path.
On shared workload evidence, Mistral 7B Q4 is benchmarked at 295 tok/s for NVIDIA H100 SXM5 80GB and 168 tok/s for NVIDIA A100 SXM4 80GB.
| Metric | NVIDIA H100 SXM5 80GB | NVIDIA A100 SXM4 80GB |
|---|---|---|
| MyAI rating | 9.4 | 9.0 |
| Best LLM | 612 tok/s | 215 tok/s |
| VRAM | 80 GB | 80 GB |
| TDP | 700 W | 400 W |
| MSRP | $25k | $15k |
| Workloads | 13 | 7 |
Shared benchmark rows
Mistral 7B Q4
Mistral 7B
NVIDIA H100 SXM5 80GB
295 tok/s
Q4_K_M / 80GB / 2025-05-08
NVIDIA A100 SXM4 80GB
168 tok/s
Q4_K_M / 80GB / 2024-04-15
Gemma 2 9B Q4
Gemma 2 9B
NVIDIA H100 SXM5 80GB
215 tok/s
Q4_K_M / 80GB / 2024-10-14
NVIDIA A100 SXM4 80GB
138 tok/s
Q4_K_M / 80GB / 2024-07-09
Qwen 2.5 14B Q4
Qwen 2.5 14B
NVIDIA H100 SXM5 80GB
168 tok/s
Q4_K_M / 80GB / 2024-10-21
NVIDIA A100 SXM4 80GB
108 tok/s
Q4_K_M / 80GB / 2024-10-30
Llama 3 70B Q4
Llama 3 70B Q4
NVIDIA H100 SXM5 80GB
66.0 tok/s
Q4_K_M / 80GB / 2024-06-24
NVIDIA A100 SXM4 80GB
48.0 tok/s
Q4_K_M / 80GB / 2024-08-28
External quality layer
Best open models likely to fit this class
This shortlist cross-references the external OpenEvals snapshot with an approximate Q4-class VRAM estimate. Use it to sanity-check whether the hardware you are comparing can host strong open models, not just benchmark toy workloads.
91.0
Qwen/Qwen2-72B
73B params · est. 43.6 GB Q4
NVIDIA H100 SXM5 80GB: likely fitNVIDIA A100 SXM4 80GB: likely fit
microsoft/Phi-3.5-mini-instruct
3.8B params · est. 2.3 GB Q4
NVIDIA H100 SXM5 80GB: likely fitNVIDIA A100 SXM4 80GB: likely fit
internlm/internlm2_5-7b-chat
7.7B params · est. 4.6 GB Q4
NVIDIA H100 SXM5 80GB: likely fitNVIDIA A100 SXM4 80GB: likely fit