NVIDIA B200 vs NVIDIA T4, AI & Machine Learning Comparison
NVIDIA B200 wins 141 of 143 benchmarks, averaging 533.1% 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 B200 leads by 1854% (46.12 vs 2.36 images/min); the closest fight is Swin2SR 4x Upscaler (14% apart); VRAM decides part of this one: NVIDIA B200 runs 145 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 B200 | NVIDIA T4 | Difference |
|---|---|---|---|
| Qwen3 4B tok/s | 317.93 | 67.71 | +370% |
| Llama 3.1 8B tok/s | 274.41 | 35.01 | +684% |
| Qwen2.5-Coder 14B tok/s | 150.96 | 19.76 | +664% |
| Qwen3 32B tok/s | 78.56 | 0 | n/a |
| Llama 3.3 70B tok/s | 44.54 | 0 | n/a |
| Stable Diffusion XL images/min | 46.12 | 2.36 | +1854% |
| FLUX.1 dev images/min | 13.136 | 0 | n/a |
| FLUX.1 Kontext dev images/min | 5.721 | 0 | n/a |
| Qwen-Image-Edit images/min | 4.86 | 0 | n/a |
| Wan 2.2 5B (720p) frames/s | 1.88 | 0 | n/a |
| DeepSeek-R1 Distill Llama 8B tok/s | 273.9 | 36.36 | +653% |
| DeepSeek-R1 Distill 1.5B tok/s | 456.7 | 148.72 | +207% |
| DeepSeek-R1 Distill 14B tok/s | 150.69 | 19.21 | +684% |
| DeepSeek-R1 Distill 7B tok/s | 277.02 | 37.58 | +637% |
| Gemma 3 12B tok/s | 151.41 | 23.12 | +555% |
| Gemma 3 4B tok/s | 291.82 | 65.12 | +348% |
| Gemma 4 12B tok/s | 146.36 | 24.03 | +509% |
| Llama 3.2 1B tok/s | 881.78 | 207.71 | +325% |
| Llama 3.2 3B tok/s | 422.09 | 85.94 | +391% |
| Mistral 7B v0.3 tok/s | 287.42 | 40.01 | +618% |
| Mistral Small 24B tok/s | 114.04 | 11.33 | +907% |
| Phi-4 14B tok/s | 163.85 | 17.46 | +838% |
| Phi-4 Mini 3.8B tok/s | 375.74 | 64.15 | +486% |
| Qwen2.5-Coder 7B tok/s | 277.98 | 37.54 | +640% |
| Qwen3 0.6B tok/s | 599.63 | 263.47 | +128% |
| Qwen3 1.7B tok/s | 551.76 | 139.92 | +294% |
| Qwen3 14B tok/s | 158.34 | 19.31 | +720% |
| Qwen3 30B A3B tok/s | 270.84 | 0 | n/a |
| Qwen3 8B tok/s | 253.45 | 36.51 | +594% |
| SmolLM3 3B tok/s | 400.73 | 86.08 | +366% |
| Codestral 22B tok/s | 112.16 | 12.59 | +791% |
| DeepSeek-R1 Distill 32B tok/s | 78.55 | 0 | n/a |
| Devstral Small 24B tok/s | 113.91 | 11.4 | +899% |
| Dolphin 2.9.1 Yi 1.5 34B tok/s | 79.35 | 0 | n/a |
| Dolphin Mistral 24B Venice tok/s | 113.93 | 11.36 | +903% |
| Dolphin X1 8B tok/s | 273.85 | 35.66 | +668% |
| Dolphin 3.0 Llama 3.1 8B tok/s | 274.05 | 35.68 | +668% |
| Dolphin 3.0 R1 Mistral 24B tok/s | 113.95 | 11.32 | +907% |
| Gemma 3 27B tok/s | 86.08 | 0 | n/a |
| Qwen2.5-Coder 32B tok/s | 78.53 | 0 | n/a |
| Qwen3-Coder 30B A3B tok/s | 277.68 | 0 | n/a |
| QwQ 32B tok/s | 78.47 | 0 | n/a |
| StarCoder2 15B tok/s | 138.64 | 15.66 | +785% |
| FLUX.1 Schnell images/min | 81.41 | 0 | n/a |
| BiRefNet images/min | 1082.03 | 183.48 | +490% |
| Depth Anything V2 Large images/min | 1039.47 | 402.34 | +158% |
| Depth Anything V2 Small images/min | 1137.49 | 612.25 | +86% |
| SAM ViT-Base images/min | 1662.34 | 180.73 | +820% |
| SAM ViT-Huge images/min | 388.61 | 34.91 | +1013% |
| Swin2SR 4x Upscaler images/min | 11.97 | 10.53 | +14% |
| Qwen2.5 1.5B LoRA train tok/s | 15322 | 1434.4 | +968% |
| Qwen2.5 7B LoRA train tok/s | 13722 | 0 | n/a |
| SmolLM2 1.7B LoRA train tok/s | 18064.7 | 1561.9 | +1057% |
| TinyLlama 1.1B LoRA train tok/s | 17993.5 | 1520.1 | +1084% |
| TinyLlama 1.1B served serve tok/s | 11562.2 | 1897.8 | +509% |
| Qwen2.5 1.5B served serve tok/s | 9186.4 | 1337.1 | +587% |
| Qwen2.5 7B served serve tok/s | 5799.1 | 0 | n/a |
| SmolLM2 1.7B served serve tok/s | 9672.9 | 1222.5 | +691% |
| AI21-Jamba-Reasoning-3B tok/s | 360.54 | 86.91 | +315% |
| Olmo-3.1-32B-Think tok/s | 79.75 | 0 | n/a |
| Codestral 22B (Q3_K_M) tok/s | 89.9 | 11.3 | +696% |
| Dolphin-Mistral-24B-Venice-Edition tok/s | 113.8 | 11.33 | +904% |
| DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored tok/s | 274.3 | 35.73 | +668% |
| DeepSeek-Coder-V2-Lite tok/s | 325.85 | 83.49 | +290% |
| DeepSeek-R1-0528-Qwen3-8B tok/s | 252.98 | 37.7 | +571% |
| DeepSeek-R1-Distill-Llama-70B tok/s | 44.49 | 0 | n/a |
| DeepSeek-R1 Distill 14B (Q3_K_M) tok/s | 120.47 | 16.86 | +615% |
| DeepSeek-R1-Distill-Qwen-32B-abliterated tok/s | 78.48 | 0 | n/a |
| dolphin-2.9-llama3-8b tok/s | 274.48 | 35.1 | +682% |
| Dolphin X1 Trinity Nano 6B tok/s | 209.31 | 114.21 | +83% |
| EVA-Qwen2.5-14B-v0.2 tok/s | 150.67 | 19.92 | +656% |
| gemma-2-2b-it-abliterated tok/s | 392.32 | 91.91 | +327% |
| gemma-2-2b tok/s | 391.94 | 92.13 | +325% |
| gemma-2-9b tok/s | 187.45 | 28.62 | +555% |
| Gemma 3 12B (Q3_K_M) tok/s | 125.31 | 19.79 | +533% |
| gemma-3-1b tok/s | 519.19 | 156.88 | +231% |
| gemma-3-270m tok/s | 889.52 | 362.8 | +145% |
| GLM-4.7-Flash-REAP-23B-A3B tok/s | 167.76 | 53.83 | +212% |
| GLM-4.7-Flash tok/s | 183.82 | 0 | n/a |
| Josiefied-Qwen3-8B-abliterated-v1 tok/s | 253.28 | 35.62 | +611% |
| gpt-oss-20b tok/s | 351.42 | 63.63 | +452% |
| Hermes-3-Llama-3.2-3B tok/s | 420.6 | 84.99 | +395% |
| Hermes-4-70B tok/s | 44.49 | 0 | n/a |
| SmolLM3-3B tok/s | 399.05 | 86.86 | +359% |
| Qwen3-Coder-Next-abliterated tok/s | 186.57 | 0 | n/a |
| KAT-Coder-V2.5-Dev tok/s | 230.68 | 0 | n/a |
| L3-8B-Stheno-v3.2 tok/s | 274.48 | 36.28 | +657% |
| Laguna-XS-2.1 tok/s | 0 | 0 | n/a |
| LFM2.5-1.2B tok/s | 916.79 | 221.66 | +314% |
| LFM2.5-8B-A1B tok/s | 575.15 | 139.03 | +314% |
| Llama-2-7B tok/s | 292 | 42.98 | +579% |
| Llama-3.2-3B-Instruct-uncensored tok/s | 419.24 | 86.29 | +386% |
| Llama-3.3-70B-Instruct-abliterated tok/s | 44.48 | 0 | n/a |
| Meta-Llama-3.1-70B tok/s | 44.48 | 0 | n/a |
| Meta-Llama-3.1-8B tok/s | 274.17 | 37.8 | +625% |
| Phi-4-mini tok/s | 375.06 | 66 | +468% |
| Mistral-7B-Instruct-v0.1 tok/s | 286.89 | 40.63 | +606% |
| Mistral-7B-Instruct-v0.2 tok/s | 286.37 | 41.31 | +593% |
| Mistral-7B-Instruct-v0.3 tok/s | 286.48 | 39.87 | +619% |
| Mistral-Nemo-Instruct-2407 tok/s | 183.92 | 22.98 | +700% |
| Mistral Small 24B (Q3_K_M) tok/s | 87.72 | 10.13 | +766% |
| Nanbeige4.2-3B tok/s | 0 | 0 | n/a |
| NemoMix-Unleashed-12B tok/s | 183.93 | 23.19 | +693% |
| Nemotron-3-Nano-30B-A3B tok/s | 345.93 | 0 | n/a |
| Hermes-4-14B tok/s | 158.22 | 19.02 | +732% |
| Ornith-1.0-35B tok/s | 221.13 | 0 | n/a |
| Ornith-1.0-9B tok/s | 226.2 | 32.93 | +587% |
| phi-2 tok/s | 348.72 | 89.6 | +289% |
| Phi-3.5-mini tok/s | 325 | 72.07 | +351% |
| Phi-4 14B (Q3_K_M) tok/s | 135.21 | 16.04 | +743% |
| Qwen-AgentWorld-35B-A3B tok/s | 221.78 | 0 | n/a |
| Qwen3-0.6B tok/s | 596.73 | 259.91 | +130% |
| Qwen3-1.7B tok/s | 551.93 | 135.21 | +308% |
| Qwen3-14B tok/s | 158.19 | 19.44 | +714% |
| Qwen3-30B-A3B tok/s | 271.69 | 0 | n/a |
| Qwen3-4B-Instruct-2507 tok/s | 316.66 | 66.89 | +373% |
| Qwen3-8B tok/s | 253.14 | 37.37 | +577% |
| Qwen3-Coder-Next tok/s | 186.39 | 0 | n/a |
| Qwen3-Next-80B-A3B-Thinking tok/s | 186.03 | 0 | n/a |
| Qwen1.5-0.5B tok/s | 699.73 | 328.29 | +113% |
| Qwen2-1.5B tok/s | 454.55 | 147.68 | +208% |
| Qwen2.5-0.5B tok/s | 818.27 | 295.47 | +177% |
| Qwen2.5-1.5B tok/s | 454.69 | 148.14 | +207% |
| Uncensored tok/s | 150.51 | 18.94 | +695% |
| Qwen2.5-14B tok/s | 150.57 | 19.84 | +659% |
| Qwen2.5-32B tok/s | 78.55 | 0 | n/a |
| Qwen2.5-3B tok/s | 388.14 | 86.04 | +351% |
| Qwen2.5-72B tok/s | 45.34 | 0 | n/a |
| Qwen2.5-7B tok/s | 277.17 | 39.01 | +611% |
| Qwen2.5-Coder-0.5B tok/s | 828.48 | 294.96 | +181% |
| Qwen2.5-Coder-1.5B tok/s | 455.35 | 148.51 | +207% |
| Qwen2.5-Coder-14B-Instruct-abliterated tok/s | 150.67 | 18.86 | +699% |
| Qwen2.5-Coder 32B (Q3_K_M) tok/s | 60.24 | 0 | n/a |
| Qwen2.5-Coder-3B tok/s | 388.17 | 85.74 | +353% |
| Qwen2.5-Coder-7B-Instruct-abliterated tok/s | 276.57 | 37.31 | +641% |
| Qwen3 30B A3B (Q3_K_M) tok/s | 229.58 | 61.82 | +271% |
| Qwen3-4B-Instruct-2507 tok/s | 316.7 | 67.45 | +370% |
| Qwen3-4B-Thinking-2507 tok/s | 317.03 | 67 | +373% |
| Qwen3-Coder-Next tok/s | 189.76 | 0 | n/a |
| Qwen3-Next-80B-A3B-Thinking tok/s | 188.78 | 0 | n/a |
| Qwen3-Next-80B-A3B tok/s | 179.71 | 0 | n/a |
| SmolLM2-135M tok/s | 815.3 | 416.24 | +96% |
| Cydonia-24B-v4.3 tok/s | 113.81 | 11.21 | +915% |
Whole-job comparison
How long each card takes to finish a complete pipeline, not just one model. NVIDIA B200 is faster on 4 of 4; NVIDIA T4 on 0.
| Workflow | NVIDIA B200 | NVIDIA T4 | Difference |
|---|---|---|---|
| 50-image depth pass | 6 s | 13 s | NVIDIA B200 2.34x faster |
| 500-image masking run | 85 s | 14.4 min | NVIDIA B200 10.16x faster |
| Full codebase review | 6.6 min | 50.7 min | NVIDIA B200 7.65x faster |
| 200-product catalogue cutout | 17.2 min | 20.3 min | NVIDIA B200 1.18x faster |
Specifications compared
| NVIDIA B200 | NVIDIA T4 | |
|---|---|---|
| VRAM | 192GB | 16GB |
| Architecture | Blackwell | Turing |
| Memory bandwidth | 8 TB/s | 320 GB/s |
| Boost clock | n/a | 1,590 MHz |
| TDP | 1000 W | 70 W |
| Launch MSRP | $40,000 | $2,299 |
| Release | 2025-02-01 | 2018-09-13 |
FAQ
- Which is better for ai & machine learning: NVIDIA B200 or NVIDIA T4?
- NVIDIA B200 performs better for ai & machine learning, winning 141 of 143 benchmarks in our suite with an average 533.1% advantage.
- What are the main hardware differences between NVIDIA B200 and NVIDIA T4?
- NVIDIA B200 has 192GB VRAM and a 1000W TDP, while NVIDIA T4 has 16GB VRAM and a 70W TDP.
- Does VRAM matter more than speed between NVIDIA B200 and NVIDIA T4?
- For AI, yes, NVIDIA B200 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 B200 and NVIDIA T4?
- Stable Diffusion XL: NVIDIA B200 leads by roughly 1854% (46.12 vs 2.36 images/min) in our testing.
NVIDIA B200 full review · NVIDIA T4 full review · All AI & Machine Learning rankings