Which is better for ai & machine learning: NVIDIA A100 40GB SXM4 or NVIDIA T4?

NVIDIA A100 40GB SXM4 performs better for ai & machine learning, winning 131 of 147 benchmarks in our suite with an average 248.5% advantage.

What are the main hardware differences between NVIDIA A100 40GB SXM4 and NVIDIA T4?

NVIDIA A100 40GB SXM4 has 40GB VRAM and a 400W TDP, while NVIDIA T4 has 16GB VRAM and a 70W TDP.

Does VRAM matter more than speed between NVIDIA A100 40GB SXM4 and NVIDIA T4?

For AI, yes, NVIDIA A100 40GB SXM4 fits 29 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 40GB SXM4 and NVIDIA T4?

Stable Diffusion XL: NVIDIA A100 40GB SXM4 leads by roughly 687% (18.576 vs 2.36 images/min) in our testing.

NVIDIA A100 40GB SXM4 vs NVIDIA T4, AI & Machine Learning Comparison

NVIDIA A100 40GB SXM4 wins 131 of 147 benchmarks, averaging 248.5% faster.

NVIDIA A100 40GB SXM4's numbers are anchored estimates calibrated against our measured cards, pending first-party measurement. Treat small gaps as ties.

What the numbers say

The gap is widest in Stable Diffusion XL, where NVIDIA A100 40GB SXM4 leads by 687% (18.576 vs 2.36 images/min); the closest fight is Depth Anything V2 Small (0% apart); VRAM decides part of this one: NVIDIA A100 40GB SXM4 runs 135 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 40GB SXM4	NVIDIA T4	Difference
Qwen3-4B tok/s	193.14	67.71	+185%
Llama-3.1-8B tok/s	157.26	35.01	+349%
Qwen2.5-Coder-14B tok/s	86.2	19.76	+336%
Qwen3-32B tok/s	43.8	0	n/a
Llama-3.3-70B tok/s	0	0	n/a
Stable Diffusion XL images/min	18.576	2.36	+687%
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	0	0	n/a
Wan 2.2 5B (720p) frames/s	0.66	0	n/a
DeepSeek-R1 Distill Llama 8B tok/s	157.16	36.36	+332%
DeepSeek-R1 Distill 1.5B tok/s	320.78	148.72	+116%
DeepSeek-R1 Distill 14B tok/s	86.39	19.21	+350%
DeepSeek-R1 Distill 7B tok/s	159.09	37.58	+323%
Gemma 3 12B tok/s	88.97	23.12	+285%
Gemma 3 4B tok/s	174.14	65.12	+167%
Gemma 4 12B tok/s	87.14	24.03	+263%
Llama 3.2 1B tok/s	532.08	207.71	+156%
Llama 3.2 3B tok/s	261.24	85.94	+204%
Mistral 7B v0.3 tok/s	165.9	40.01	+315%
Mistral Small 24B tok/s	62.42	11.33	+451%
Phi-4 14B tok/s	98.65	17.46	+465%
Phi-4 Mini 3.8B tok/s	236.71	64.15	+269%
Qwen2.5-Coder 7B tok/s	159.9	37.54	+326%
Qwen3 0.6B tok/s	417.88	263.47	+59%
Qwen3 1.7B tok/s	340.76	139.92	+144%
Qwen3 14B tok/s	90.01	19.31	+366%
Qwen3 30B A3B tok/s	169.1	0	n/a
Qwen3 8B tok/s	146.21	36.51	+300%
SmolLM3 3B tok/s	246.25	86.08	+186%
Codestral 22B tok/s	63.74	12.59	+406%
DeepSeek-R1 Distill 32B tok/s	43.13	0	n/a
Devstral Small 24B tok/s	62.4	11.4	+447%
Dolphin 2.9.1 Yi 1.5 34B tok/s	42.97	0	n/a
Dolphin Mistral 24B Venice tok/s	62.46	11.36	+450%
Dolphin X1 8B tok/s	156.3	35.66	+338%
Dolphin 3.0 Llama 3.1 8B tok/s	156.55	35.68	+339%
Dolphin 3.0 R1 Mistral 24B tok/s	62.4	11.32	+451%
Gemma 3 27B tok/s	47.47	0	n/a
Qwen2.5-Coder 32B tok/s	43.14	0	n/a
Qwen3-Coder 30B A3B tok/s	173.47	0	n/a
QwQ 32B tok/s	43.02	0	n/a
StarCoder2 15B tok/s	80.25	15.66	+412%
FLUX.1 Schnell images/min	28.44	0	n/a
BiRefNet images/min	611.56	183.48	+233%
Depth Anything V2 Large images/min	536.74	402.34	+33%
Depth Anything V2 Small images/min	613.42	612.25	0%
SAM ViT-Base images/min	696.77	180.73	+286%
SAM ViT-Huge images/min	146.81	34.91	+321%
Swin2SR 4x Upscaler images/min	17.25	10.53	+64%
Kokoro TTS 82M x realtime	142.03	42.88	+231%
MusicGen Small x realtime	1.08	0.92	+17%
Whisper large-v3 x realtime	102.47	44.36	+131%
Dolphin X1 Trinity Nano 6B tok/s	153.27	114.21	+34%
gpt-oss-20b tok/s	205.1	63.63	+222%
Olmo-3.1-32B-Think tok/s	43.67	0	n/a
Dolphin-Mistral-24B-Venice-Edition tok/s	62.56	11.33	+452%
DeepSeek-Coder-V2-Lite tok/s	205.42	83.49	+146%
DeepSeek-R1-0528-Qwen3-8B tok/s	145.84	37.7	+287%
EVA-Qwen2.5-14B-v0.2 tok/s	86.48	19.92	+334%
Qwen2.5 1.5B LoRA train tok/s	6349.7	1434.4	+343%
Qwen2.5 7B LoRA train tok/s	3670.8	0	n/a
SmolLM2 1.7B LoRA train tok/s	6938.8	1561.9	+344%
TinyLlama 1.1B LoRA train tok/s	7471	1520.1	+391%
gemma-2-2b-it-abliterated tok/s	232.27	91.91	+153%
gemma-2-2b tok/s	234.68	92.13	+155%
gemma-2-9b tok/s	101.97	28.62	+256%
gemma-3-1b tok/s	310.69	156.88	+98%
gemma-3-270m tok/s	574.24	362.8	+58%
GLM-4.7-Flash tok/s	119.1	0	n/a
SmolLM3-3B tok/s	247.66	86.86	+185%
KAT-Coder-V2.5-Dev tok/s	137.31	0	n/a
LFM2.5-1.2B tok/s	572.69	221.66	+158%
Llama-2-7B tok/s	172.53	42.98	+301%
Llama-3.2-3B-Instruct-uncensored tok/s	260.83	86.29	+202%
Meta-Llama-3.1-8B tok/s	156.68	37.8	+314%
Phi-4-mini tok/s	237.38	66	+260%
Mistral-7B-Instruct-v0.1 tok/s	166.39	40.63	+310%
Mistral-7B-Instruct-v0.2 tok/s	166.8	41.31	+304%
Mistral-7B-Instruct-v0.3 tok/s	167.06	39.87	+319%
Ornith-1.0-35B tok/s	131.77	0	n/a
Ornith-1.0-9B tok/s	126.79	32.93	+285%
Phi-3.5-mini tok/s	222.53	72.07	+209%
Qwen-AgentWorld-35B-A3B tok/s	131.16	0	n/a
Qwen3-0.6B tok/s	408.39	259.91	+57%
Qwen3-1.7B tok/s	341.85	135.21	+153%
Qwen3-14B tok/s	90.07	19.44	+363%
Qwen3-8B tok/s	145.96	37.37	+291%
Qwen2.5-0.5B tok/s	517.09	295.47	+75%
Qwen2.5-1.5B tok/s	315.59	148.14	+113%
Qwen2.5-14B tok/s	86.07	19.84	+334%
Qwen2.5-32B tok/s	43.23	0	n/a
Qwen2.5-3B tok/s	240.48	86.04	+179%
Qwen2.5-7B tok/s	159.81	39.01	+310%
Qwen2.5-Coder-1.5B tok/s	322.96	148.51	+117%
Qwen2.5-Coder-3B tok/s	241.51	85.74	+182%
AI21-Jamba-Reasoning-3B tok/s	231.15	86.91	+166%
Codestral 22B (Q3_K_M) tok/s	48.37	11.3	+328%
DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored tok/s	156.62	35.73	+338%
DeepSeek-R1 Distill 14B (Q3_K_M) tok/s	65.33	16.86	+287%
DeepSeek-R1-Distill-Qwen-32B-abliterated tok/s	43.07	0	n/a
dolphin-2.9-llama3-8b tok/s	153.34	35.1	+337%
Gemma 3 12B (Q3_K_M) tok/s	69.7	19.79	+252%
GLM-4.7-Flash-REAP-23B-A3B tok/s	109.1	53.83	+103%
Josiefied-Qwen3-8B-abliterated-v1 tok/s	146.8	35.62	+312%
Hermes-3-Llama-3.2-3B tok/s	261	84.99	+207%
L3-8B-Stheno-v3.2 tok/s	156.55	36.28	+332%
LFM2.5-8B-A1B tok/s	351.47	139.03	+153%
Mistral-Nemo-Instruct-2407 tok/s	106.33	22.98	+363%
Mistral Small 24B (Q3_K_M) tok/s	45.34	10.13	+348%
NemoMix-Unleashed-12B tok/s	105.05	23.19	+353%
Nemotron-3-Nano-30B-A3B tok/s	192.44	0	n/a
Hermes-4-14B tok/s	89.97	19.02	+373%
phi-2 tok/s	228.1	89.6	+155%
Phi-4 14B (Q3_K_M) tok/s	80.71	16.04	+403%
Qwen3-30B-A3B tok/s	167.1	0	n/a
Qwen3-4B-Instruct-2507 tok/s	192.89	66.89	+188%
Qwen1.5-0.5B tok/s	497.73	328.29	+52%
Qwen2-1.5B tok/s	322.09	147.68	+118%
Uncensored tok/s	85.82	18.94	+353%
Qwen2.5-Coder-0.5B tok/s	542.36	294.96	+84%
Qwen2.5-Coder-14B-Instruct-abliterated tok/s	86.34	18.86	+358%
Qwen2.5-Coder 32B (Q3_K_M) tok/s	31.04	0	n/a
Qwen2.5-Coder-7B-Instruct-abliterated tok/s	159.76	37.31	+328%
Qwen3 30B A3B (Q3_K_M) tok/s	142.78	61.82	+131%
Qwen3-4B-Instruct-2507 tok/s	193.39	67.45	+187%
Qwen3-4B-Thinking-2507 tok/s	189.89	67	+183%
SmolLM2-135M tok/s	544.06	416.24	+31%
Cydonia-24B-v4.3 tok/s	62.41	11.21	+457%
Qwen2.5 1.5B served serve tok/s	4111.1	1337.1	+207%
Qwen2.5 7B served serve tok/s	1769.6	0	n/a
SmolLM2 1.7B served serve tok/s	3865.2	1222.5	+216%
TinyLlama 1.1B served serve tok/s	3221.3	1897.8	+70%
Qwen2.5 1.5B + 0.5B draft x vs solo	0.694	0.9	-23%
Qwen3-Coder-Next-abliterated tok/s	0	0	n/a
Laguna-XS-2.1 tok/s	0	0	n/a
Nanbeige4.2-3B tok/s	0	0	n/a
Qwen3-Coder-Next tok/s	0	0	n/a
Qwen3-Next-80B-A3B-Thinking tok/s	0	0	n/a
Qwen2.5-72B tok/s	0	0	n/a
Qwen3-Coder-Next tok/s	0	0	n/a
Qwen3-Next-80B-A3B-Thinking tok/s	0	0	n/a
Qwen3-Next-80B-A3B tok/s	0	0	n/a
DeepSeek-R1-Distill-Llama-70B tok/s	0	0	n/a
Hermes-4-70B tok/s	0	0	n/a
Llama-3.3-70B-Instruct-abliterated tok/s	0	0	n/a
Meta-Llama-3.1-70B tok/s	0	0	n/a

Whole-job comparison

How long each card takes to finish a complete pipeline, not just one model. NVIDIA A100 40GB SXM4 is faster on 4 of 4; NVIDIA T4 on 0.

Workflow	NVIDIA A100 40GB SXM4	NVIDIA T4	Difference
50-image depth pass	10 s	13 s	NVIDIA A100 40GB SXM4 1.38x faster
500-image masking run	3.5 min	14.4 min	NVIDIA A100 40GB SXM4 4.13x faster
Full codebase review	11.7 min	50.7 min	NVIDIA A100 40GB SXM4 4.34x faster
200-product catalogue cutout	12.2 min	20.3 min	NVIDIA A100 40GB SXM4 1.67x faster

Specifications compared

	NVIDIA A100 40GB SXM4	NVIDIA T4
VRAM	40GB	16GB
Architecture	Ampere	Turing
Memory bandwidth	1555 GB/s	320 GB/s
Boost clock	1,410 MHz	1,590 MHz
TDP	400 W	70 W
Launch MSRP	$12,000	$2,299
Release	2020-05-14	2018-09-13

FAQ

Which is better for ai & machine learning: NVIDIA A100 40GB SXM4 or NVIDIA T4?: NVIDIA A100 40GB SXM4 performs better for ai & machine learning, winning 131 of 147 benchmarks in our suite with an average 248.5% advantage.
What are the main hardware differences between NVIDIA A100 40GB SXM4 and NVIDIA T4?: NVIDIA A100 40GB SXM4 has 40GB VRAM and a 400W TDP, while NVIDIA T4 has 16GB VRAM and a 70W TDP.
Does VRAM matter more than speed between NVIDIA A100 40GB SXM4 and NVIDIA T4?: For AI, yes, NVIDIA A100 40GB SXM4 fits 29 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 40GB SXM4 and NVIDIA T4?: Stable Diffusion XL: NVIDIA A100 40GB SXM4 leads by roughly 687% (18.576 vs 2.36 images/min) in our testing.

NVIDIA A100 40GB SXM4 full review · NVIDIA T4 full review · All AI & Machine Learning rankings