Which is better for ai & machine learning: NVIDIA A100 80GB SXM4 or NVIDIA T4?

NVIDIA A100 80GB SXM4 performs better for ai & machine learning, winning 143 of 148 benchmarks in our suite with an average 262.8% advantage.

What are the main hardware differences between NVIDIA A100 80GB SXM4 and NVIDIA T4?

NVIDIA A100 80GB SXM4 has 80GB VRAM and a 400W TDP, while NVIDIA T4 has 16GB VRAM and a 70W TDP.

Does VRAM matter more than speed between NVIDIA A100 80GB SXM4 and NVIDIA T4?

For AI, yes, NVIDIA A100 80GB SXM4 fits 42 more of our 12 workloads than NVIDIA T4. A model that exceeds VRAM doesn't run slower, it doesn't run at all, so the card that fits the model wins that workload outright.

Where is the biggest performance difference between NVIDIA A100 80GB SXM4 and NVIDIA T4?

Stable Diffusion XL: NVIDIA A100 80GB SXM4 leads by roughly 593% (16.36 vs 2.36 images/min) in our testing.

NVIDIA A100 80GB SXM4 vs NVIDIA T4, AI & Machine Learning Comparison

NVIDIA A100 80GB SXM4 wins 143 of 148 benchmarks, averaging 262.8% faster.

Both cards were measured first-party on our bench, same suite, same test rig.

What the numbers say

The gap is widest in Stable Diffusion XL, where NVIDIA A100 80GB SXM4 leads by 593% (16.36 vs 2.36 images/min); the closest fight is Florence-2 Base (18% apart); VRAM decides part of this one: NVIDIA A100 80GB SXM4 runs 155 of our 12 AI workloads while the other card runs 111, models that don't fit score zero.

Benchmark results head-to-head

Benchmark	NVIDIA A100 80GB SXM4	NVIDIA T4	Difference
Qwen3 4B tok/s	197	67.71	+191%
Llama 3.1 8B tok/s	162.57	35.01	+364%
Qwen2.5-Coder 14B tok/s	89.45	19.76	+353%
Qwen3 32B tok/s	45.53	0	n/a
Llama 3.3 70B tok/s	24.4	0	n/a
Stable Diffusion XL images/min	16.36	2.36	+593%
FLUX.1 dev images/min	4.264	0	n/a
FLUX.1 Kontext dev images/min	1.993	0	n/a
Qwen-Image-Edit images/min	1.64	0	n/a
Wan 2.2 5B (720p) frames/s	0.66	0	n/a
DeepSeek-R1 Distill Llama 8B tok/s	159.37	36.36	+338%
DeepSeek-R1 Distill 1.5B tok/s	327.66	148.72	+120%
DeepSeek-R1 Distill 14B tok/s	87	19.21	+353%
DeepSeek-R1 Distill 7B tok/s	162.96	37.58	+334%
Gemma 3 12B tok/s	91.45	23.12	+296%
Gemma 3 4B tok/s	176.94	65.12	+172%
Gemma 4 12B tok/s	92.43	24.03	+285%
Llama 3.2 1B tok/s	525.32	207.71	+153%
Llama 3.2 3B tok/s	264.25	85.94	+207%
Mistral 7B v0.3 tok/s	171.92	40.01	+330%
Mistral Small 24B tok/s	61.38	11.33	+442%
Phi-4 14B tok/s	95.22	17.46	+445%
Phi-4 Mini 3.8B tok/s	239.17	64.15	+273%
Qwen2.5-Coder 7B tok/s	164.6	37.54	+338%
Qwen3 0.6B tok/s	428.39	263.47	+63%
Qwen3 1.7B tok/s	343.58	139.92	+146%
Qwen3 14B tok/s	90.64	19.31	+369%
Qwen3 30B A3B tok/s	177.32	0	n/a
Qwen3 8B tok/s	149.96	36.51	+311%
SmolLM3 3B tok/s	249.76	86.08	+190%
Codestral 22B tok/s	63.26	12.59	+402%
DeepSeek-R1 Distill 32B tok/s	43.11	0	n/a
Devstral Small 24B tok/s	61.63	11.4	+441%
Dolphin 2.9.1 Yi 1.5 34B tok/s	43.47	0	n/a
Dolphin Mistral 24B Venice tok/s	62.72	11.36	+452%
Dolphin X1 8B tok/s	158.62	35.66	+345%
Dolphin 3.0 Llama 3.1 8B tok/s	158.63	35.68	+345%
Dolphin 3.0 R1 Mistral 24B tok/s	61.17	11.32	+440%
Gemma 3 27B tok/s	48.43	0	n/a
Qwen2.5-Coder 32B tok/s	43.36	0	n/a
Qwen3-Coder 30B A3B tok/s	182.26	0	n/a
QwQ 32B tok/s	43.27	0	n/a
StarCoder2 15B tok/s	79.22	15.66	+406%
FLUX.1 Schnell images/min	28.39	0	n/a
BiRefNet images/min	916.67	183.48	+400%
Depth Anything V2 Large images/min	876.29	402.34	+118%
Depth Anything V2 Small images/min	925.15	612.25	+51%
SAM ViT-Base images/min	744.79	180.73	+312%
SAM ViT-Huge images/min	145.7	34.91	+317%
Swin2SR 4x Upscaler images/min	28.72	10.53	+173%
Florence-2 Base images/min	126.4	148.61	-15%
Florence-2 Large images/min	70.94	83.43	-15%
Qwen2.5 1.5B LoRA train tok/s	8252.8	1434.4	+475%
Qwen2.5 7B LoRA train tok/s	3900.6	0	n/a
SmolLM2 1.7B LoRA train tok/s	9750.8	1561.9	+524%
TinyLlama 1.1B LoRA train tok/s	9553.3	1520.1	+528%
Qwen2.5 1.5B served serve tok/s	4564.7	1337.1	+241%
Qwen2.5 7B served serve tok/s	2187.2	0	n/a
SmolLM2 1.7B served serve tok/s	4351.4	1222.5	+256%
TinyLlama 1.1B served serve tok/s	5701.5	1897.8	+200%
DeepSeek-R1-Distill-Llama-70B tok/s	22.99	0	n/a
DeepSeek-R1-Distill-Qwen-32B-abliterated tok/s	43.48	0	n/a
Kokoro TTS 82M x realtime	110.17	42.88	+157%
Llama-3.3-70B-Instruct-abliterated tok/s	22.92	0	n/a
Meta-Llama-3.1-70B tok/s	22.92	0	n/a
MusicGen Small x realtime	0.74	0.92	-20%
Nemotron-3-Nano-30B-A3B tok/s	204.06	0	n/a
Whisper large-v3 x realtime	73.83	44.36	+66%
Qwen2.5-Coder 32B (Q3_K_M) tok/s	31.99	0	n/a
AI21-Jamba-Reasoning-3B tok/s	239.23	86.91	+175%
Olmo-3.1-32B-Think tok/s	46.49	0	n/a
Codestral 22B (Q3_K_M) tok/s	49.52	11.3	+338%
Dolphin-Mistral-24B-Venice-Edition tok/s	65.77	11.33	+480%
DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored tok/s	164.46	35.73	+360%
DeepSeek-Coder-V2-Lite tok/s	213.91	83.49	+156%
DeepSeek-R1-0528-Qwen3-8B tok/s	152.76	37.7	+305%
DeepSeek-R1 Distill 14B (Q3_K_M) tok/s	67.13	16.86	+298%
dolphin-2.9-llama3-8b tok/s	164.09	35.1	+367%
Dolphin X1 Trinity Nano 6B tok/s	158.41	114.21	+39%
EVA-Qwen2.5-14B-v0.2 tok/s	90.35	19.92	+354%
gemma-2-2b-it-abliterated tok/s	244.41	91.91	+166%
gemma-2-2b tok/s	244.52	92.13	+165%
gemma-2-9b tok/s	108.68	28.62	+280%
Gemma 3 12B (Q3_K_M) tok/s	71.88	19.79	+263%
gemma-3-1b tok/s	324.08	156.88	+107%
gemma-3-270m tok/s	617.58	362.8	+70%
GLM-4.7-Flash-REAP-23B-A3B tok/s	112.75	53.83	+109%
GLM-4.7-Flash tok/s	124.08	0	n/a
Josiefied-Qwen3-8B-abliterated-v1 tok/s	152.29	35.62	+328%
gpt-oss-20b tok/s	212.63	63.63	+234%
Hermes-3-Llama-3.2-3B tok/s	269.13	84.99	+217%
Hermes-4-70B tok/s	24.47	0	n/a
SmolLM3-3B tok/s	254.6	86.86	+193%
Qwen3-Coder-Next-abliterated tok/s	119.56	0	n/a
KAT-Coder-V2.5-Dev tok/s	146.38	0	n/a
L3-8B-Stheno-v3.2 tok/s	164.51	36.28	+353%
Laguna-XS-2.1 tok/s	0	0	n/a
LFM2.5-1.2B tok/s	576.95	221.66	+160%
LFM2.5-8B-A1B tok/s	369.01	139.03	+165%
Llama-2-7B tok/s	180.09	42.98	+319%
Llama-3.2-3B-Instruct-uncensored tok/s	269.13	86.29	+212%
Meta-Llama-3.1-8B tok/s	164.24	37.8	+334%
Phi-4-mini tok/s	243.38	66	+269%
Mistral-7B-Instruct-v0.1 tok/s	173.87	40.63	+328%
Mistral-7B-Instruct-v0.2 tok/s	173.8	41.31	+321%
Mistral-7B-Instruct-v0.3 tok/s	173.27	39.87	+335%
Mistral-Nemo-Instruct-2407 tok/s	111.07	22.98	+383%
Mistral Small 24B (Q3_K_M) tok/s	46.63	10.13	+360%
Nanbeige4.2-3B tok/s	0	0	n/a
NemoMix-Unleashed-12B tok/s	110.99	23.19	+379%
Hermes-4-14B tok/s	94.48	19.02	+397%
Ornith-1.0-35B tok/s	138.8	0	n/a
Ornith-1.0-9B tok/s	134.67	32.93	+309%
phi-2 tok/s	235.55	89.6	+163%
Phi-3.5-mini tok/s	229.46	72.07	+218%
Phi-4 14B (Q3_K_M) tok/s	82.68	16.04	+415%
Qwen-AgentWorld-35B-A3B tok/s	139.69	0	n/a
Qwen3-0.6B tok/s	432.66	259.91	+66%
Qwen3-1.7B tok/s	350.4	135.21	+159%
Qwen3-14B tok/s	94.35	19.44	+385%
Qwen3-30B-A3B tok/s	178.14	0	n/a
Qwen3-4B-Instruct-2507 tok/s	199.39	66.89	+198%
Qwen3-8B tok/s	152.8	37.37	+309%
Qwen3-Coder-Next tok/s	118.62	0	n/a
Qwen3-Next-80B-A3B-Thinking tok/s	118.93	0	n/a
Qwen1.5-0.5B tok/s	540.32	328.29	+65%
Qwen2-1.5B tok/s	333.79	147.68	+126%
Qwen2.5-0.5B tok/s	571.49	295.47	+93%
Qwen2.5-1.5B tok/s	335.29	148.14	+126%
Uncensored tok/s	89.43	18.94	+372%
Qwen2.5-14B tok/s	90.46	19.84	+356%
Qwen2.5-32B tok/s	45.6	0	n/a
Qwen2.5-3B tok/s	248.47	86.04	+189%
Qwen2.5-72B tok/s	23.95	0	n/a
Qwen2.5-7B tok/s	166.92	39.01	+328%
Qwen2.5-Coder-0.5B tok/s	562.59	294.96	+91%
Qwen2.5-Coder-1.5B tok/s	333.87	148.51	+125%
Qwen2.5-Coder-14B-Instruct-abliterated tok/s	90.28	18.86	+379%
Qwen2.5-Coder-3B tok/s	248.7	85.74	+190%
Qwen2.5-Coder-7B-Instruct-abliterated tok/s	166.84	37.31	+347%
Qwen3 30B A3B (Q3_K_M) tok/s	146.72	61.82	+137%
Qwen3-4B-Instruct-2507 tok/s	199.65	67.45	+196%
Qwen3-4B-Thinking-2507 tok/s	199.71	67	+198%
Qwen3-Coder-Next tok/s	120.71	0	n/a
Qwen3-Next-80B-A3B-Thinking tok/s	120.98	0	n/a
Qwen3-Next-80B-A3B tok/s	117.44	0	n/a
SmolLM2-135M tok/s	554.44	416.24	+33%
Cydonia-24B-v4.3 tok/s	65.82	11.21	+487%

Whole-job comparison

How long each card takes to finish a complete pipeline, not just one model. NVIDIA A100 80GB SXM4 is faster on 4 of 4; NVIDIA T4 on 0.

Workflow	NVIDIA A100 80GB SXM4	NVIDIA T4	Difference
50-image depth pass	5 s	13 s	NVIDIA A100 80GB SXM4 2.67x faster
500-image masking run	3.5 min	14.4 min	NVIDIA A100 80GB SXM4 4.14x faster
200-product catalogue cutout	7.3 min	20.3 min	NVIDIA A100 80GB SXM4 2.78x faster
Full codebase review	11.2 min	50.7 min	NVIDIA A100 80GB SXM4 4.53x faster

Specifications compared

	NVIDIA A100 80GB SXM4	NVIDIA T4
VRAM	80GB	16GB
Architecture	Ampere	Turing
Memory bandwidth	2039 GB/s	320 GB/s
Boost clock	1,410 MHz	1,590 MHz
TDP	400 W	70 W
Launch MSRP	$17,000	$2,299
Release	2020-11-16	2018-09-13

FAQ

Which is better for ai & machine learning: NVIDIA A100 80GB SXM4 or NVIDIA T4?: NVIDIA A100 80GB SXM4 performs better for ai & machine learning, winning 143 of 148 benchmarks in our suite with an average 262.8% advantage.
What are the main hardware differences between NVIDIA A100 80GB SXM4 and NVIDIA T4?: NVIDIA A100 80GB SXM4 has 80GB VRAM and a 400W TDP, while NVIDIA T4 has 16GB VRAM and a 70W TDP.
Does VRAM matter more than speed between NVIDIA A100 80GB SXM4 and NVIDIA T4?: For AI, yes, NVIDIA A100 80GB SXM4 fits 42 more of our 12 workloads than NVIDIA T4. A model that exceeds VRAM doesn't run slower, it doesn't run at all, so the card that fits the model wins that workload outright.
Where is the biggest performance difference between NVIDIA A100 80GB SXM4 and NVIDIA T4?: Stable Diffusion XL: NVIDIA A100 80GB SXM4 leads by roughly 593% (16.36 vs 2.36 images/min) in our testing.

NVIDIA A100 80GB SXM4 full review · NVIDIA T4 full review · All AI & Machine Learning rankings