NVIDIA B200 vs NVIDIA B300, AI & Machine Learning Comparison
NVIDIA B300 wins 135 of 142 benchmarks, averaging 12.4% faster.
Both cards were measured first-party on our bench, same suite, same test rig.
What the numbers say
The gap is widest in Swin2SR 4x Upscaler, where NVIDIA B300 leads by 387% (11.97 vs 58.29 images/min); the closest fight is Nemotron-3-Nano-30B-A3B (0% apart).
Benchmark results head-to-head
| Benchmark | NVIDIA B200 | NVIDIA B300 | Difference |
|---|---|---|---|
| Qwen3 4B tok/s | 317.93 | 333.34 | -5% |
| Llama 3.1 8B tok/s | 274.41 | 287.23 | -4% |
| Qwen2.5-Coder 14B tok/s | 150.96 | 158.49 | -5% |
| Qwen3 32B tok/s | 78.56 | 83.68 | -6% |
| Llama 3.3 70B tok/s | 44.54 | 47.97 | -7% |
| Stable Diffusion XL images/min | 46.12 | 29.2 | +58% |
| Z-Image Turbo images/min | 32.325 | 39.675 | -19% |
| FLUX.1 dev images/min | 13.136 | 20.807 | -37% |
| FLUX.1 Kontext dev images/min | 5.721 | 10.329 | -45% |
| Qwen-Image-Edit images/min | 4.86 | 8.14 | -40% |
| LTX-Video (distilled) frames/s | 26.8 | 31.87 | -16% |
| Wan 2.2 5B (720p) frames/s | 1.88 | 2.94 | -36% |
| DeepSeek-R1 Distill Llama 8B tok/s | 273.9 | 283.77 | -3% |
| DeepSeek-R1 Distill 1.5B tok/s | 456.7 | 562.14 | -19% |
| DeepSeek-R1 Distill 14B tok/s | 150.69 | 156.33 | -4% |
| DeepSeek-R1 Distill 7B tok/s | 277.02 | 284.77 | -3% |
| Gemma 3 12B tok/s | 151.41 | 161.27 | -6% |
| Gemma 3 4B tok/s | 291.82 | 301.07 | -3% |
| Gemma 4 12B tok/s | 146.36 | 155.96 | -6% |
| Llama 3.2 1B tok/s | 881.78 | 889.19 | -1% |
| Llama 3.2 3B tok/s | 422.09 | 432.24 | -2% |
| Mistral 7B v0.3 tok/s | 287.42 | 298.95 | -4% |
| Mistral Small 24B tok/s | 114.04 | 119.3 | -4% |
| Phi-4 14B tok/s | 163.85 | 177.41 | -8% |
| Phi-4 Mini 3.8B tok/s | 375.74 | 398.16 | -6% |
| Qwen2.5-Coder 7B tok/s | 277.98 | 286.5 | -3% |
| Qwen3 0.6B tok/s | 599.63 | 718.58 | -17% |
| Qwen3 1.7B tok/s | 551.76 | 555.1 | -1% |
| Qwen3 14B tok/s | 158.34 | 165.44 | -4% |
| Qwen3 30B A3B tok/s | 270.84 | 284.66 | -5% |
| Qwen3 8B tok/s | 253.45 | 261.4 | -3% |
| SmolLM3 3B tok/s | 400.73 | 411.25 | -3% |
| Codestral 22B tok/s | 112.16 | 119.81 | -6% |
| DeepSeek-R1 Distill 32B tok/s | 78.55 | 82.9 | -5% |
| Devstral Small 24B tok/s | 113.91 | 121.18 | -6% |
| Dolphin 2.9.1 Yi 1.5 34B tok/s | 79.35 | 85.15 | -7% |
| Dolphin Mistral 24B Venice tok/s | 113.93 | 120.93 | -6% |
| Dolphin X1 8B tok/s | 273.85 | 287.02 | -5% |
| Dolphin 3.0 Llama 3.1 8B tok/s | 274.05 | 285.88 | -4% |
| Dolphin 3.0 R1 Mistral 24B tok/s | 113.95 | 120.95 | -6% |
| Gemma 3 27B tok/s | 86.08 | 93.44 | -8% |
| Qwen2.5-Coder 32B tok/s | 78.53 | 82.9 | -5% |
| Qwen3-Coder 30B A3B tok/s | 277.68 | 284.88 | -3% |
| QwQ 32B tok/s | 78.47 | 82.91 | -5% |
| StarCoder2 15B tok/s | 138.64 | 144.05 | -4% |
| FLUX.1 Schnell images/min | 81.41 | 59.54 | +37% |
| Z-Image Turbo (1024px) images/min | 62.47 | 48.63 | +28% |
| BiRefNet images/min | 1082.03 | 1064.73 | +2% |
| Depth Anything V2 Large images/min | 1039.47 | 1069.95 | -3% |
| Depth Anything V2 Small images/min | 1137.49 | 1491.6 | -24% |
| SAM ViT-Base images/min | 1662.34 | 1719.59 | -3% |
| SAM ViT-Huge images/min | 388.61 | 399.37 | -3% |
| Swin2SR 4x Upscaler images/min | 11.97 | 58.29 | -79% |
| Qwen2.5 1.5B LoRA train tok/s | 15322 | 22351.1 | -31% |
| Qwen2.5 7B LoRA train tok/s | 13722 | 14323.1 | -4% |
| SmolLM2 1.7B LoRA train tok/s | 18064.7 | 26114.3 | -31% |
| TinyLlama 1.1B LoRA train tok/s | 17993.5 | 26938.2 | -33% |
| AI21-Jamba-Reasoning-3B tok/s | 360.54 | 380.88 | -5% |
| Olmo-3.1-32B-Think tok/s | 79.75 | 87.85 | -9% |
| Codestral 22B (Q3_K_M) tok/s | 89.9 | 107.98 | -17% |
| Dolphin-Mistral-24B-Venice-Edition tok/s | 113.8 | 121.87 | -7% |
| DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored tok/s | 274.3 | 292.55 | -6% |
| DeepSeek-Coder-V2-Lite tok/s | 325.85 | 340.53 | -4% |
| DeepSeek-R1-0528-Qwen3-8B tok/s | 252.98 | 270.35 | -6% |
| DeepSeek-R1-Distill-Llama-70B tok/s | 44.49 | 48.11 | -8% |
| DeepSeek-R1 Distill 14B (Q3_K_M) tok/s | 120.47 | 144.74 | -17% |
| DeepSeek-R1-Distill-Qwen-32B-abliterated tok/s | 78.48 | 83.19 | -6% |
| dolphin-2.9-llama3-8b tok/s | 274.48 | 290.47 | -6% |
| Dolphin X1 Trinity Nano 6B tok/s | 209.31 | 257.93 | -19% |
| EVA-Qwen2.5-14B-v0.2 tok/s | 150.67 | 159.46 | -6% |
| gemma-2-2b-it-abliterated tok/s | 392.32 | 418.18 | -6% |
| gemma-2-2b tok/s | 391.94 | 417.84 | -6% |
| gemma-2-9b tok/s | 187.45 | 202.22 | -7% |
| Gemma 3 12B (Q3_K_M) tok/s | 125.31 | 149.61 | -16% |
| gemma-3-1b tok/s | 519.19 | 533.32 | -3% |
| gemma-3-270m tok/s | 889.52 | 926.67 | -4% |
| GLM-4.7-Flash-REAP-23B-A3B tok/s | 167.76 | 178.49 | -6% |
| GLM-4.7-Flash tok/s | 183.82 | 196.52 | -6% |
| Josiefied-Qwen3-8B-abliterated-v1 tok/s | 253.28 | 270.09 | -6% |
| gpt-oss-20b tok/s | 351.42 | 355.56 | -1% |
| Hermes-3-Llama-3.2-3B tok/s | 420.6 | 446.78 | -6% |
| Hermes-4-70B tok/s | 44.49 | 48.1 | -8% |
| SmolLM3-3B tok/s | 399.05 | 420.3 | -5% |
| Qwen3-Coder-Next-abliterated tok/s | 186.57 | 199.12 | -6% |
| KAT-Coder-V2.5-Dev tok/s | 230.68 | 239.66 | -4% |
| L3-8B-Stheno-v3.2 tok/s | 274.48 | 293 | -6% |
| Laguna-XS-2.1 tok/s | 0 | 0 | n/a |
| LFM2.5-1.2B tok/s | 916.79 | 955.13 | -4% |
| LFM2.5-8B-A1B tok/s | 575.15 | 596.16 | -4% |
| Llama-2-7B tok/s | 292 | 315.9 | -8% |
| Llama-3.2-3B-Instruct-uncensored tok/s | 419.24 | 448.94 | -7% |
| Llama-3.3-70B-Instruct-abliterated tok/s | 44.48 | 48.1 | -8% |
| Meta-Llama-3.1-70B tok/s | 44.48 | 48.12 | -8% |
| Meta-Llama-3.1-8B tok/s | 274.17 | 293 | -6% |
| Phi-4-mini tok/s | 375.06 | 409.58 | -8% |
| Mistral-7B-Instruct-v0.1 tok/s | 286.89 | 306.44 | -6% |
| Mistral-7B-Instruct-v0.2 tok/s | 286.37 | 306.79 | -7% |
| Mistral-7B-Instruct-v0.3 tok/s | 286.48 | 306.46 | -7% |
| Mistral-Nemo-Instruct-2407 tok/s | 183.92 | 196.92 | -7% |
| Mistral Small 24B (Q3_K_M) tok/s | 87.72 | 108.37 | -19% |
| Nanbeige4.2-3B tok/s | 0 | 0 | n/a |
| NemoMix-Unleashed-12B tok/s | 183.93 | 197.06 | -7% |
| Nemotron-3-Nano-30B-A3B tok/s | 345.93 | 345.31 | 0% |
| Hermes-4-14B tok/s | 158.22 | 169.51 | -7% |
| Ornith-1.0-35B tok/s | 221.13 | 232.36 | -5% |
| Ornith-1.0-9B tok/s | 226.2 | 240.67 | -6% |
| phi-2 tok/s | 348.72 | 356.65 | -2% |
| Phi-3.5-mini tok/s | 325 | 352.25 | -8% |
| Phi-4 14B (Q3_K_M) tok/s | 135.21 | 165.06 | -18% |
| Qwen-AgentWorld-35B-A3B tok/s | 221.78 | 233.04 | -5% |
| Qwen3-0.6B tok/s | 596.73 | 742.64 | -20% |
| Qwen3-1.7B tok/s | 551.93 | 575.97 | -4% |
| Qwen3-14B tok/s | 158.19 | 169.52 | -7% |
| Qwen3-30B-A3B tok/s | 271.69 | 291.82 | -7% |
| Qwen3-4B-Instruct-2507 tok/s | 316.66 | 339.78 | -7% |
| Qwen3-8B tok/s | 253.14 | 270.35 | -6% |
| Qwen3-Coder-Next tok/s | 186.39 | 196.34 | -5% |
| Qwen3-Next-80B-A3B-Thinking tok/s | 186.03 | 194.98 | -5% |
| Qwen1.5-0.5B tok/s | 699.73 | 894.84 | -22% |
| Qwen2-1.5B tok/s | 454.55 | 583.79 | -22% |
| Qwen2.5-0.5B tok/s | 818.27 | 843.4 | -3% |
| Qwen2.5-1.5B tok/s | 454.69 | 584.96 | -22% |
| Uncensored tok/s | 150.51 | 159.48 | -6% |
| Qwen2.5-14B tok/s | 150.57 | 159.33 | -5% |
| Qwen2.5-32B tok/s | 78.55 | 83.41 | -6% |
| Qwen2.5-3B tok/s | 388.14 | 409.56 | -5% |
| Qwen2.5-72B tok/s | 45.34 | 48.28 | -6% |
| Qwen2.5-7B tok/s | 277.17 | 293.63 | -6% |
| Qwen2.5-Coder-0.5B tok/s | 828.48 | 843.44 | -2% |
| Qwen2.5-Coder-1.5B tok/s | 455.35 | 584.5 | -22% |
| Qwen2.5-Coder-14B-Instruct-abliterated tok/s | 150.67 | 159.5 | -6% |
| Qwen2.5-Coder 32B (Q3_K_M) tok/s | 60.24 | 74.28 | -19% |
| Qwen2.5-Coder-3B tok/s | 388.17 | 410.06 | -5% |
| Qwen2.5-Coder-7B-Instruct-abliterated tok/s | 276.57 | 292.21 | -5% |
| Qwen3 30B A3B (Q3_K_M) tok/s | 229.58 | 268.71 | -15% |
| Qwen3-4B-Instruct-2507 tok/s | 316.7 | 339.72 | -7% |
| Qwen3-4B-Thinking-2507 tok/s | 317.03 | 339.74 | -7% |
| Qwen3-Coder-Next tok/s | 189.76 | 199.39 | -5% |
| Qwen3-Next-80B-A3B-Thinking tok/s | 188.78 | 200.55 | -6% |
| Qwen3-Next-80B-A3B tok/s | 179.71 | 191.32 | -6% |
| SmolLM2-135M tok/s | 815.3 | 855.87 | -5% |
| Cydonia-24B-v4.3 tok/s | 113.81 | 121.9 | -7% |
Whole-job comparison
How long each card takes to finish a complete pipeline, not just one model. NVIDIA B200 is faster on 2 of 14; NVIDIA B300 on 12.
| Workflow | NVIDIA B200 | NVIDIA B300 | Difference | Cost per run |
|---|---|---|---|---|
| 50-image depth pass | 6 s | 5 s | NVIDIA B300 1.22x faster | $0.009 vs $0.009 |
| 500-image masking run | 85 s | 78 s | NVIDIA B300 1.10x faster | $0.141 vs $0.150 |
| 24-frame storyboard | 89 s | 1.5 min | NVIDIA B200 1.01x faster | $0.149 vs $0.174 |
| 60-second AI short film | 1.9 min | 2.2 min | NVIDIA B200 1.17x faster | $0.186 vs $0.251 |
| 6-panel comic page | 2.3 min | 2 min | NVIDIA B300 1.16x faster | $0.233 vs $0.234 |
| Character sheet, 12 poses | 2.9 min | 2.2 min | NVIDIA B300 1.28x faster | $0.285 vs $0.257 |
| 10 short social clips | 5.6 min | 4.7 min | NVIDIA B300 1.18x faster | $0.558 vs $0.548 |
| Full codebase review | 6.6 min | 6.3 min | NVIDIA B300 1.05x faster | $0.660 vs $0.730 |
| 40-product photo shoot | 8.4 min | 6.1 min | NVIDIA B300 1.38x faster | $0.836 vs $0.701 |
| 20 long-form articles | 10.5 min | 9.7 min | NVIDIA B300 1.08x faster | $1.044 vs $1.125 |
| 40-product shoot, start to finish | 12.1 min | 6.9 min | NVIDIA B300 1.75x faster | $1.201 vs $0.797 |
| 200-product catalogue cutout | 17.2 min | 3.7 min | NVIDIA B300 4.62x faster | $1.712 vs $0.430 |
| 100-photo restoration batch | 17.8 min | 10.2 min | NVIDIA B300 1.75x faster | $1.773 vs $1.178 |
| 100-photo restore and enlarge | 26.4 min | 11.9 min | NVIDIA B300 2.21x faster | $2.628 vs $1.378 |
Renting by the hour, NVIDIA B300 finishes 8 of 14 cheaper. The quicker card is not automatically the cheaper way to get the work done.
Cost to rent
| Card | Per hour |
|---|---|
| NVIDIA B200 | $5.980 |
| NVIDIA B300 | $6.940 |
NVIDIA B200 is 1.16x cheaper per hour. Cheapest on-demand rate we see across RunPod and Vast.
Specifications compared
| NVIDIA B200 | NVIDIA B300 | |
|---|---|---|
| VRAM | 192GB | 288GB |
| Architecture | Blackwell | Blackwell Ultra |
| Memory bandwidth | 8 TB/s | 8 TB/s |
| TDP | 1000 W | 1400 W |
| Launch MSRP | $40,000 | $40,000 |
| Release | 2025-02-01 | 2025-11-01 |
FAQ
- Which is better for ai & machine learning: NVIDIA B200 or NVIDIA B300?
- NVIDIA B300 performs better for ai & machine learning, winning 135 of 142 benchmarks in our suite with an average 12.4% advantage.
- What are the main hardware differences between NVIDIA B200 and NVIDIA B300?
- NVIDIA B200 has 192GB VRAM and a 1000W TDP, while NVIDIA B300 has 288GB VRAM and a 1400W TDP.
- Where is the biggest performance difference between NVIDIA B200 and NVIDIA B300?
- Swin2SR 4x Upscaler: NVIDIA B300 leads by roughly 387% (11.97 vs 58.29 images/min) in our testing.
NVIDIA B200 full review · NVIDIA B300 full review · All AI & Machine Learning rankings