NVIDIA B300 vs NVIDIA T4, AI & Machine Learning Comparison
NVIDIA B300 wins 137 of 139 benchmarks, averaging 586.8% faster.
Both cards were measured first-party on our bench, same suite, same test rig.
What the numbers say
The gap is widest in TinyLlama 1.1B LoRA, where NVIDIA B300 leads by 1672% (26938.2 vs 1520.1 train tok/s); the closest fight is SmolLM2-135M (106% apart); VRAM decides part of this one: NVIDIA B300 runs 140 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 B300 | NVIDIA T4 | Difference |
|---|---|---|---|
| Qwen3 4B tok/s | 333.34 | 67.71 | +392% |
| Llama 3.1 8B tok/s | 287.23 | 35.01 | +720% |
| Qwen2.5-Coder 14B tok/s | 158.49 | 19.76 | +702% |
| Qwen3 32B tok/s | 83.68 | 0 | n/a |
| Llama 3.3 70B tok/s | 47.97 | 0 | n/a |
| Stable Diffusion XL images/min | 29.2 | 2.36 | +1137% |
| FLUX.1 dev images/min | 20.807 | 0 | n/a |
| FLUX.1 Kontext dev images/min | 10.329 | 0 | n/a |
| Qwen-Image-Edit images/min | 8.14 | 0 | n/a |
| Wan 2.2 5B (720p) frames/s | 2.94 | 0 | n/a |
| DeepSeek-R1 Distill Llama 8B tok/s | 283.77 | 36.36 | +680% |
| DeepSeek-R1 Distill 1.5B tok/s | 562.14 | 148.72 | +278% |
| DeepSeek-R1 Distill 14B tok/s | 156.33 | 19.21 | +714% |
| DeepSeek-R1 Distill 7B tok/s | 284.77 | 37.58 | +658% |
| Gemma 3 12B tok/s | 161.27 | 23.12 | +598% |
| Gemma 3 4B tok/s | 301.07 | 65.12 | +362% |
| Gemma 4 12B tok/s | 155.96 | 24.03 | +549% |
| Llama 3.2 1B tok/s | 889.19 | 207.71 | +328% |
| Llama 3.2 3B tok/s | 432.24 | 85.94 | +403% |
| Mistral 7B v0.3 tok/s | 298.95 | 40.01 | +647% |
| Mistral Small 24B tok/s | 119.3 | 11.33 | +953% |
| Phi-4 14B tok/s | 177.41 | 17.46 | +916% |
| Phi-4 Mini 3.8B tok/s | 398.16 | 64.15 | +521% |
| Qwen2.5-Coder 7B tok/s | 286.5 | 37.54 | +663% |
| Qwen3 0.6B tok/s | 718.58 | 263.47 | +173% |
| Qwen3 1.7B tok/s | 555.1 | 139.92 | +297% |
| Qwen3 14B tok/s | 165.44 | 19.31 | +757% |
| Qwen3 30B A3B tok/s | 284.66 | 0 | n/a |
| Qwen3 8B tok/s | 261.4 | 36.51 | +616% |
| SmolLM3 3B tok/s | 411.25 | 86.08 | +378% |
| Codestral 22B tok/s | 119.81 | 12.59 | +852% |
| DeepSeek-R1 Distill 32B tok/s | 82.9 | 0 | n/a |
| Devstral Small 24B tok/s | 121.18 | 11.4 | +963% |
| Dolphin 2.9.1 Yi 1.5 34B tok/s | 85.15 | 0 | n/a |
| Dolphin Mistral 24B Venice tok/s | 120.93 | 11.36 | +965% |
| Dolphin X1 8B tok/s | 287.02 | 35.66 | +705% |
| Dolphin 3.0 Llama 3.1 8B tok/s | 285.88 | 35.68 | +701% |
| Dolphin 3.0 R1 Mistral 24B tok/s | 120.95 | 11.32 | +968% |
| Gemma 3 27B tok/s | 93.44 | 0 | n/a |
| Qwen2.5-Coder 32B tok/s | 82.9 | 0 | n/a |
| Qwen3-Coder 30B A3B tok/s | 284.88 | 0 | n/a |
| QwQ 32B tok/s | 82.91 | 0 | n/a |
| StarCoder2 15B tok/s | 144.05 | 15.66 | +820% |
| FLUX.1 Schnell images/min | 59.54 | 0 | n/a |
| BiRefNet images/min | 1064.73 | 183.48 | +480% |
| Depth Anything V2 Large images/min | 1069.95 | 402.34 | +166% |
| Depth Anything V2 Small images/min | 1491.6 | 612.25 | +144% |
| SAM ViT-Base images/min | 1719.59 | 180.73 | +851% |
| SAM ViT-Huge images/min | 399.37 | 34.91 | +1044% |
| Swin2SR 4x Upscaler images/min | 58.29 | 10.53 | +454% |
| Qwen2.5 1.5B LoRA train tok/s | 22351.1 | 1434.4 | +1458% |
| Qwen2.5 7B LoRA train tok/s | 14323.1 | 0 | n/a |
| SmolLM2 1.7B LoRA train tok/s | 26114.3 | 1561.9 | +1572% |
| TinyLlama 1.1B LoRA train tok/s | 26938.2 | 1520.1 | +1672% |
| AI21-Jamba-Reasoning-3B tok/s | 380.88 | 86.91 | +338% |
| Olmo-3.1-32B-Think tok/s | 87.85 | 0 | n/a |
| Codestral 22B (Q3_K_M) tok/s | 107.98 | 11.3 | +856% |
| Dolphin-Mistral-24B-Venice-Edition tok/s | 121.87 | 11.33 | +976% |
| DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored tok/s | 292.55 | 35.73 | +719% |
| DeepSeek-Coder-V2-Lite tok/s | 340.53 | 83.49 | +308% |
| DeepSeek-R1-0528-Qwen3-8B tok/s | 270.35 | 37.7 | +617% |
| DeepSeek-R1-Distill-Llama-70B tok/s | 48.11 | 0 | n/a |
| DeepSeek-R1 Distill 14B (Q3_K_M) tok/s | 144.74 | 16.86 | +758% |
| DeepSeek-R1-Distill-Qwen-32B-abliterated tok/s | 83.19 | 0 | n/a |
| dolphin-2.9-llama3-8b tok/s | 290.47 | 35.1 | +728% |
| Dolphin X1 Trinity Nano 6B tok/s | 257.93 | 114.21 | +126% |
| EVA-Qwen2.5-14B-v0.2 tok/s | 159.46 | 19.92 | +701% |
| gemma-2-2b-it-abliterated tok/s | 418.18 | 91.91 | +355% |
| gemma-2-2b tok/s | 417.84 | 92.13 | +354% |
| gemma-2-9b tok/s | 202.22 | 28.62 | +607% |
| Gemma 3 12B (Q3_K_M) tok/s | 149.61 | 19.79 | +656% |
| gemma-3-1b tok/s | 533.32 | 156.88 | +240% |
| gemma-3-270m tok/s | 926.67 | 362.8 | +155% |
| GLM-4.7-Flash-REAP-23B-A3B tok/s | 178.49 | 53.83 | +232% |
| GLM-4.7-Flash tok/s | 196.52 | 0 | n/a |
| Josiefied-Qwen3-8B-abliterated-v1 tok/s | 270.09 | 35.62 | +658% |
| gpt-oss-20b tok/s | 355.56 | 63.63 | +459% |
| Hermes-3-Llama-3.2-3B tok/s | 446.78 | 84.99 | +426% |
| Hermes-4-70B tok/s | 48.1 | 0 | n/a |
| SmolLM3-3B tok/s | 420.3 | 86.86 | +384% |
| Qwen3-Coder-Next-abliterated tok/s | 199.12 | 0 | n/a |
| KAT-Coder-V2.5-Dev tok/s | 239.66 | 0 | n/a |
| L3-8B-Stheno-v3.2 tok/s | 293 | 36.28 | +708% |
| Laguna-XS-2.1 tok/s | 0 | 0 | n/a |
| LFM2.5-1.2B tok/s | 955.13 | 221.66 | +331% |
| LFM2.5-8B-A1B tok/s | 596.16 | 139.03 | +329% |
| Llama-2-7B tok/s | 315.9 | 42.98 | +635% |
| Llama-3.2-3B-Instruct-uncensored tok/s | 448.94 | 86.29 | +420% |
| Llama-3.3-70B-Instruct-abliterated tok/s | 48.1 | 0 | n/a |
| Meta-Llama-3.1-70B tok/s | 48.12 | 0 | n/a |
| Meta-Llama-3.1-8B tok/s | 293 | 37.8 | +675% |
| Phi-4-mini tok/s | 409.58 | 66 | +521% |
| Mistral-7B-Instruct-v0.1 tok/s | 306.44 | 40.63 | +654% |
| Mistral-7B-Instruct-v0.2 tok/s | 306.79 | 41.31 | +643% |
| Mistral-7B-Instruct-v0.3 tok/s | 306.46 | 39.87 | +669% |
| Mistral-Nemo-Instruct-2407 tok/s | 196.92 | 22.98 | +757% |
| Mistral Small 24B (Q3_K_M) tok/s | 108.37 | 10.13 | +970% |
| Nanbeige4.2-3B tok/s | 0 | 0 | n/a |
| NemoMix-Unleashed-12B tok/s | 197.06 | 23.19 | +750% |
| Nemotron-3-Nano-30B-A3B tok/s | 345.31 | 0 | n/a |
| Hermes-4-14B tok/s | 169.51 | 19.02 | +791% |
| Ornith-1.0-35B tok/s | 232.36 | 0 | n/a |
| Ornith-1.0-9B tok/s | 240.67 | 32.93 | +631% |
| phi-2 tok/s | 356.65 | 89.6 | +298% |
| Phi-3.5-mini tok/s | 352.25 | 72.07 | +389% |
| Phi-4 14B (Q3_K_M) tok/s | 165.06 | 16.04 | +929% |
| Qwen-AgentWorld-35B-A3B tok/s | 233.04 | 0 | n/a |
| Qwen3-0.6B tok/s | 742.64 | 259.91 | +186% |
| Qwen3-1.7B tok/s | 575.97 | 135.21 | +326% |
| Qwen3-14B tok/s | 169.52 | 19.44 | +772% |
| Qwen3-30B-A3B tok/s | 291.82 | 0 | n/a |
| Qwen3-4B-Instruct-2507 tok/s | 339.78 | 66.89 | +408% |
| Qwen3-8B tok/s | 270.35 | 37.37 | +623% |
| Qwen3-Coder-Next tok/s | 196.34 | 0 | n/a |
| Qwen3-Next-80B-A3B-Thinking tok/s | 194.98 | 0 | n/a |
| Qwen1.5-0.5B tok/s | 894.84 | 328.29 | +173% |
| Qwen2-1.5B tok/s | 583.79 | 147.68 | +295% |
| Qwen2.5-0.5B tok/s | 843.4 | 295.47 | +185% |
| Qwen2.5-1.5B tok/s | 584.96 | 148.14 | +295% |
| Uncensored tok/s | 159.48 | 18.94 | +742% |
| Qwen2.5-14B tok/s | 159.33 | 19.84 | +703% |
| Qwen2.5-32B tok/s | 83.41 | 0 | n/a |
| Qwen2.5-3B tok/s | 409.56 | 86.04 | +376% |
| Qwen2.5-72B tok/s | 48.28 | 0 | n/a |
| Qwen2.5-7B tok/s | 293.63 | 39.01 | +653% |
| Qwen2.5-Coder-0.5B tok/s | 843.44 | 294.96 | +186% |
| Qwen2.5-Coder-1.5B tok/s | 584.5 | 148.51 | +294% |
| Qwen2.5-Coder-14B-Instruct-abliterated tok/s | 159.5 | 18.86 | +746% |
| Qwen2.5-Coder 32B (Q3_K_M) tok/s | 74.28 | 0 | n/a |
| Qwen2.5-Coder-3B tok/s | 410.06 | 85.74 | +378% |
| Qwen2.5-Coder-7B-Instruct-abliterated tok/s | 292.21 | 37.31 | +683% |
| Qwen3 30B A3B (Q3_K_M) tok/s | 268.71 | 61.82 | +335% |
| Qwen3-4B-Instruct-2507 tok/s | 339.72 | 67.45 | +404% |
| Qwen3-4B-Thinking-2507 tok/s | 339.74 | 67 | +407% |
| Qwen3-Coder-Next tok/s | 199.39 | 0 | n/a |
| Qwen3-Next-80B-A3B-Thinking tok/s | 200.55 | 0 | n/a |
| Qwen3-Next-80B-A3B tok/s | 191.32 | 0 | n/a |
| SmolLM2-135M tok/s | 855.87 | 416.24 | +106% |
| Cydonia-24B-v4.3 tok/s | 121.9 | 11.21 | +987% |
Whole-job comparison
How long each card takes to finish a complete pipeline, not just one model. NVIDIA B300 is faster on 4 of 4; NVIDIA T4 on 0.
| Workflow | NVIDIA B300 | NVIDIA T4 | Difference |
|---|---|---|---|
| 50-image depth pass | 5 s | 13 s | NVIDIA B300 2.85x faster |
| 500-image masking run | 78 s | 14.4 min | NVIDIA B300 11.14x faster |
| 200-product catalogue cutout | 3.7 min | 20.3 min | NVIDIA B300 5.45x faster |
| Full codebase review | 6.3 min | 50.7 min | NVIDIA B300 8.03x faster |
Specifications compared
| NVIDIA B300 | NVIDIA T4 | |
|---|---|---|
| VRAM | 288GB | 16GB |
| Architecture | Blackwell Ultra | Turing |
| Memory bandwidth | 8 TB/s | 320 GB/s |
| Boost clock | n/a | 1,590 MHz |
| TDP | 1400 W | 70 W |
| Launch MSRP | $40,000 | $2,299 |
| Release | 2025-11-01 | 2018-09-13 |
FAQ
- Which is better for ai & machine learning: NVIDIA B300 or NVIDIA T4?
- NVIDIA B300 performs better for ai & machine learning, winning 137 of 139 benchmarks in our suite with an average 586.8% advantage.
- What are the main hardware differences between NVIDIA B300 and NVIDIA T4?
- NVIDIA B300 has 288GB VRAM and a 1400W TDP, while NVIDIA T4 has 16GB VRAM and a 70W TDP.
- Does VRAM matter more than speed between NVIDIA B300 and NVIDIA T4?
- For AI, yes, NVIDIA B300 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 B300 and NVIDIA T4?
- TinyLlama 1.1B LoRA: NVIDIA B300 leads by roughly 1672% (26938.2 vs 1520.1 train tok/s) in our testing.
NVIDIA B300 full review · NVIDIA T4 full review · All AI & Machine Learning rankings