Which is better for ai & machine learning: NVIDIA A10G or NVIDIA T4?

NVIDIA A10G performs better for ai & machine learning, winning 134 of 154 benchmarks in our suite with an average 126.4% advantage.

What are the main hardware differences between NVIDIA A10G and NVIDIA T4?

NVIDIA A10G has 24GB VRAM and a 150W TDP, while NVIDIA T4 has 16GB VRAM and a 70W TDP.

Does VRAM matter more than speed between NVIDIA A10G and NVIDIA T4?

For AI, yes, NVIDIA A10G fits 21 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 A10G and NVIDIA T4?

Sana 1.6B: NVIDIA A10G leads by roughly 1150% (14.13 vs 1.13 images/min) in our testing.

NVIDIA A10G vs NVIDIA T4, AI & Machine Learning Comparison

NVIDIA A10G wins 134 of 154 benchmarks, averaging 126.4% faster.

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

What the numbers say

The gap is widest in Sana 1.6B, where NVIDIA A10G leads by 1150% (14.13 vs 1.13 images/min); the closest fight is Qwen2.5 1.5B + 0.5B draft (2% apart); VRAM decides part of this one: NVIDIA A10G runs 137 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 A10G	NVIDIA T4	Difference
Qwen3-4B tok/s	129.87	67.71	+92%
Llama-3.1-8B tok/s	86.62	35.01	+147%
Qwen2.5-Coder-14B tok/s	47.07	19.76	+138%
Qwen3-32B tok/s	22.04	0	n/a
Llama 3.3 70B tok/s	0	0	n/a
Stable Diffusion XL images/min	6.38	2.36	+170%
FLUX.1 dev images/min	0	0	n/a
FLUX.1 Kontext dev images/min	0	0	n/a
Qwen-Image-Edit images/min	0	0	n/a
Wan 2.2 5B (720p) frames/s	0.18	0	n/a
Llama 3.2 1B tok/s	400.6	207.71	+93%
Qwen3 0.6B tok/s	451.38	263.47	+71%
Qwen3 1.7B tok/s	267.08	139.92	+91%
DeepSeek-R1 Distill 1.5B tok/s	265.75	148.72	+79%
Gemma 3 4B tok/s	124.85	65.12	+92%
Llama 3.2 3B tok/s	170.47	85.94	+98%
Mistral 7B v0.3 tok/s	92.82	40.01	+132%
Phi-4 Mini 3.8B tok/s	144.42	64.15	+125%
Qwen2.5-Coder 7B tok/s	90.05	37.54	+140%
SmolLM3 3B tok/s	172.15	86.08	+100%
DeepSeek-R1 Distill Llama 8B tok/s	86.83	36.36	+139%
DeepSeek-R1 Distill 14B tok/s	46.96	19.21	+144%
DeepSeek-R1 Distill 7B tok/s	89.79	37.58	+139%
Gemma 3 12B tok/s	52.07	23.12	+125%
Gemma 4 12B tok/s	52.67	24.03	+119%
Mistral Small 24B tok/s	30.94	11.33	+173%
Phi-4 14B tok/s	48.18	17.46	+176%
Qwen3 14B tok/s	48.12	19.31	+149%
Qwen3 30B A3B tok/s	140.12	0	n/a
Qwen3 8B tok/s	84.09	36.51	+130%
Codestral 22B tok/s	32.09	12.59	+155%
DeepSeek-R1 Distill 32B tok/s	21.85	0	n/a
Devstral Small 24B tok/s	30.98	11.4	+172%
Dolphin 2.9.1 Yi 1.5 34B tok/s	21.12	0	n/a
Dolphin Mistral 24B Venice tok/s	30.95	11.36	+172%
Dolphin X1 8B tok/s	86.61	35.66	+143%
Dolphin 3.0 Llama 3.1 8B tok/s	86.54	35.68	+143%
Dolphin 3.0 R1 Mistral 24B tok/s	30.9	11.32	+173%
Gemma 3 27B tok/s	24.8	0	n/a
Qwen2.5-Coder 32B tok/s	21.92	0	n/a
Qwen3-Coder 30B A3B tok/s	142.77	0	n/a
QwQ 32B tok/s	21.87	0	n/a
StarCoder2 15B tok/s	42.19	15.66	+169%
BiRefNet images/min	432.33	183.48	+136%
Depth Anything V2 Large images/min	740.74	402.34	+84%
Depth Anything V2 Small images/min	941.18	612.25	+54%
SAM ViT-Base images/min	398.72	180.73	+121%
SAM ViT-Huge images/min	84.22	34.91	+141%
Swin2SR 4x Upscaler images/min	22.31	10.53	+112%
Kokoro TTS 82M x realtime	101.11	42.88	+136%
MusicGen Small x realtime	1.2	0.92	+30%
Whisper large-v3 x realtime	86.07	44.36	+94%
Florence-2 Base images/min	210.79	148.61	+42%
Florence-2 Large images/min	117.94	83.43	+41%
Dolphin X1 Trinity Nano 6B tok/s	171.23	114.21	+50%
gpt-oss-20b tok/s	144.61	63.63	+127%
Olmo-3.1-32B-Think tok/s	22.13	0	n/a
Dolphin-Mistral-24B-Venice-Edition tok/s	31	11.33	+174%
DeepSeek-Coder-V2-Lite tok/s	160.13	83.49	+92%
DeepSeek-R1-0528-Qwen3-8B tok/s	83.22	37.7	+121%
EVA-Qwen2.5-14B-v0.2 tok/s	47	19.92	+136%
gemma-2-2b-it-abliterated tok/s	174.56	91.91	+90%
gemma-2-2b tok/s	175.03	92.13	+90%
gemma-2-9b tok/s	57.37	28.62	+100%
gemma-3-1b tok/s	284.78	156.88	+82%
gemma-3-270m tok/s	597.49	362.8	+65%
GLM-4.7-Flash tok/s	103.24	0	n/a
SmolLM3-3B tok/s	171.66	86.86	+98%
KAT-Coder-V2.5-Dev tok/s	113.6	0	n/a
L3-8B-Stheno-v3.2 tok/s	86.8	36.28	+139%
LFM2.5-1.2B tok/s	429.96	221.66	+94%
Llama-2-7B tok/s	96.69	42.98	+125%
Llama-3.2-3B-Instruct-uncensored tok/s	167.63	86.29	+94%
Meta-Llama-3.1-8B tok/s	86.69	37.8	+129%
Phi-4-mini tok/s	144.27	66	+119%
Mistral-7B-Instruct-v0.1 tok/s	92.45	40.63	+128%
Mistral-7B-Instruct-v0.2 tok/s	92.74	41.31	+124%
Mistral-7B-Instruct-v0.3 tok/s	92.45	39.87	+132%
Ornith-1.0-35B tok/s	104.9	0	n/a
Ornith-1.0-9B tok/s	73.78	32.93	+124%
Phi-3.5-mini tok/s	145.97	72.07	+103%
Qwen-AgentWorld-35B-A3B tok/s	104.36	0	n/a
Qwen3-0.6B tok/s	446.74	259.91	+72%
Qwen3-1.7B tok/s	262.49	135.21	+94%
Qwen3-14B tok/s	47.8	19.44	+146%
Qwen3-8B tok/s	83.04	37.37	+122%
Qwen2.5-0.5B tok/s	508.78	295.47	+72%
Qwen2.5-1.5B tok/s	265.7	148.14	+79%
Qwen2.5-14B tok/s	47.02	19.84	+137%
Qwen2.5-32B tok/s	21.98	0	n/a
Qwen2.5-3B tok/s	168.23	86.04	+96%
Qwen2.5-7B tok/s	89.68	39.01	+130%
Qwen2.5-Coder-1.5B tok/s	264.79	148.51	+78%
Qwen2.5-Coder-3B tok/s	168	85.74	+96%
GLM-4.7-Flash-REAP-23B-A3B tok/s	97.48	53.83	+81%
Josiefied-Qwen3-8B-abliterated-v1 tok/s	83.26	35.62	+134%
LFM2.5-8B-A1B tok/s	268.04	139.03	+93%
Mistral-Nemo-Instruct-2407 tok/s	57	22.98	+148%
Hermes-4-14B tok/s	47.8	19.02	+151%
phi-2 tok/s	172.45	89.6	+92%
Qwen3-30B-A3B tok/s	140.14	0	n/a
Qwen2.5-Coder-14B-Instruct-abliterated tok/s	46.91	18.86	+149%
Qwen3-4B-Instruct-2507 tok/s	129.9	67.45	+93%
Qwen3-4B-Thinking-2507 tok/s	129.99	67	+94%
SmolLM2-135M tok/s	680.37	416.24	+63%
Cydonia-24B-v4.3 tok/s	31.04	11.21	+177%
Qwen2.5 1.5B LoRA train tok/s	4214.4	1434.4	+194%
Qwen2.5 7B LoRA train tok/s	1239	0	n/a
SmolLM2 1.7B LoRA train tok/s	3668.3	1561.9	+135%
TinyLlama 1.1B LoRA train tok/s	5499.6	1520.1	+262%
Qwen2.5 1.5B served serve tok/s	2702.5	1337.1	+102%
Qwen2.5 7B served serve tok/s	858.7	0	n/a
SmolLM2 1.7B served serve tok/s	2242.7	1222.5	+83%
TinyLlama 1.1B served serve tok/s	3558.2	1897.8	+87%
AuraFlow v0.3 images/min	1.39	0	n/a
FLUX.1 Schnell images/min	0	0	n/a
PixArt-Sigma XL images/min	9.93	0	n/a
Sana 1.6B images/min	14.13	1.13	+1150%
Stable Diffusion 3.5 Large images/min	0	0	n/a
Stable Diffusion 3.5 Medium images/min	4.11	0	n/a
Qwen2.5 1.5B + 0.5B draft x vs solo	0.919	0.9	+2%
AI21-Jamba-Reasoning-3B tok/s	166.29	86.91	+91%
Codestral 22B (Q3_K_M) tok/s	28.01	11.3	+148%
DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored tok/s	87.84	35.73	+146%
DeepSeek-R1-Distill-Llama-70B tok/s	0	0	n/a
DeepSeek-R1 Distill 14B (Q3_K_M) tok/s	41.72	16.86	+147%
DeepSeek-R1-Distill-Qwen-32B-abliterated tok/s	22.14	0	n/a
dolphin-2.9-llama3-8b tok/s	87.98	35.1	+151%
Gemma 3 12B (Q3_K_M) tok/s	46.29	19.79	+134%
Hermes-3-Llama-3.2-3B tok/s	169.23	84.99	+99%
Hermes-4-70B tok/s	0	0	n/a
Qwen3-Coder-Next-abliterated tok/s	0	0	n/a
Laguna-XS-2.1 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
Mistral Small 24B (Q3_K_M) tok/s	26.8	10.13	+165%
Nanbeige4.2-3B tok/s	0	0	n/a
NemoMix-Unleashed-12B tok/s	57.52	23.19	+148%
Nemotron-3-Nano-30B-A3B tok/s	0	0	n/a
Phi-4 14B (Q3_K_M) tok/s	43.56	16.04	+172%
Qwen3-4B-Instruct-2507 tok/s	130.83	66.89	+96%
Qwen3-Coder-Next tok/s	0	0	n/a
Qwen3-Next-80B-A3B-Thinking tok/s	0	0	n/a
Qwen1.5-0.5B tok/s	542.19	328.29	+65%
Qwen2-1.5B tok/s	265.45	147.68	+80%
Uncensored tok/s	47.45	18.94	+151%
Qwen2.5-72B tok/s	0	0	n/a
Qwen2.5-Coder-0.5B tok/s	513.36	294.96	+74%
Qwen2.5-Coder 32B (Q3_K_M) tok/s	18.78	0	n/a
Qwen2.5-Coder-7B-Instruct-abliterated tok/s	91.41	37.31	+145%
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
Qwen3 30B A3B (Q3_K_M) tok/s	120.38	61.82	+95%

Whole-job comparison

How long each card takes to finish a complete pipeline, not just one model. NVIDIA A10G is faster on 4 of 4; NVIDIA T4 on 0.

Workflow	NVIDIA A10G	NVIDIA T4	Difference
50-image depth pass	7 s	13 s	NVIDIA A10G 2.02x faster
500-image masking run	6 min	14.4 min	NVIDIA A10G 2.40x faster
200-product catalogue cutout	9.6 min	20.3 min	NVIDIA A10G 2.12x faster
Full codebase review	21.3 min	50.7 min	NVIDIA A10G 2.38x faster

Specifications compared

	NVIDIA A10G	NVIDIA T4
VRAM	24GB	16GB
Architecture	Ampere	Turing
Memory bandwidth	600 GB/s	320 GB/s
Boost clock	1,710 MHz	1,590 MHz
TDP	150 W	70 W
Launch MSRP	$2,800	$2,299
Release	2021-11-01	2018-09-13

FAQ

Which is better for ai & machine learning: NVIDIA A10G or NVIDIA T4?: NVIDIA A10G performs better for ai & machine learning, winning 134 of 154 benchmarks in our suite with an average 126.4% advantage.
What are the main hardware differences between NVIDIA A10G and NVIDIA T4?: NVIDIA A10G has 24GB VRAM and a 150W TDP, while NVIDIA T4 has 16GB VRAM and a 70W TDP.
Does VRAM matter more than speed between NVIDIA A10G and NVIDIA T4?: For AI, yes, NVIDIA A10G fits 21 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 A10G and NVIDIA T4?: Sana 1.6B: NVIDIA A10G leads by roughly 1150% (14.13 vs 1.13 images/min) in our testing.

NVIDIA A10G full review · NVIDIA T4 full review · All AI & Machine Learning rankings