NVIDIA B300 vs NVIDIA H100 80GB HBM3, AI & Machine Learning Comparison
NVIDIA B300 wins 132 of 142 benchmarks, averaging 15.2% faster.
Both cards were measured first-party on our bench, same suite, same test rig.
What the numbers say
The gap is widest in FLUX.1 Kontext dev, where NVIDIA B300 leads by 142% (10.329 vs 4.264 images/min); the closest fight is Qwen3 1.7B (0% apart).
Benchmark results head-to-head
| Benchmark | NVIDIA B300 | NVIDIA H100 80GB HBM3 | Difference |
|---|---|---|---|
| Qwen3 4B tok/s | 333.34 | 310.26 | +7% |
| Llama 3.1 8B tok/s | 287.23 | 261.83 | +10% |
| Qwen2.5-Coder 14B tok/s | 158.49 | 144.84 | +9% |
| Qwen3 32B tok/s | 83.68 | 74.07 | +13% |
| Llama 3.3 70B tok/s | 47.97 | 41 | +17% |
| Stable Diffusion XL images/min | 29.2 | 34.58 | -16% |
| Z-Image Turbo images/min | 39.675 | 22.125 | +79% |
| FLUX.1 dev images/min | 20.807 | 9.064 | +130% |
| FLUX.1 Kontext dev images/min | 10.329 | 4.264 | +142% |
| Qwen-Image-Edit images/min | 8.14 | 3.68 | +121% |
| LTX-Video (distilled) frames/s | 31.87 | 17.47 | +82% |
| Wan 2.2 5B (720p) frames/s | 2.94 | 1.33 | +121% |
| DeepSeek-R1 Distill Llama 8B tok/s | 283.77 | 261.15 | +9% |
| DeepSeek-R1 Distill 1.5B tok/s | 562.14 | 537.32 | +5% |
| DeepSeek-R1 Distill 14B tok/s | 156.33 | 144.82 | +8% |
| DeepSeek-R1 Distill 7B tok/s | 284.77 | 264.42 | +8% |
| Gemma 3 12B tok/s | 161.27 | 150.58 | +7% |
| Gemma 3 4B tok/s | 301.07 | 289.61 | +4% |
| Gemma 4 12B tok/s | 155.96 | 148.17 | +5% |
| Llama 3.2 1B tok/s | 889.19 | 880.64 | +1% |
| Llama 3.2 3B tok/s | 432.24 | 422.87 | +2% |
| Mistral 7B v0.3 tok/s | 298.95 | 275.85 | +8% |
| Mistral Small 24B tok/s | 119.3 | 105.62 | +13% |
| Phi-4 14B tok/s | 177.41 | 165.17 | +7% |
| Phi-4 Mini 3.8B tok/s | 398.16 | 388.86 | +2% |
| Qwen2.5-Coder 7B tok/s | 286.5 | 263.91 | +9% |
| Qwen3 0.6B tok/s | 718.58 | 713.53 | +1% |
| Qwen3 1.7B tok/s | 555.1 | 556.48 | -0% |
| Qwen3 14B tok/s | 165.44 | 151.16 | +9% |
| Qwen3 30B A3B tok/s | 284.66 | 283.78 | 0% |
| Qwen3 8B tok/s | 261.4 | 244.22 | +7% |
| SmolLM3 3B tok/s | 411.25 | 397.99 | +3% |
| Codestral 22B tok/s | 119.81 | 110.05 | +9% |
| DeepSeek-R1 Distill 32B tok/s | 82.9 | 75.8 | +9% |
| Devstral Small 24B tok/s | 121.18 | 107.79 | +12% |
| Dolphin 2.9.1 Yi 1.5 34B tok/s | 85.15 | 76.76 | +11% |
| Dolphin Mistral 24B Venice tok/s | 120.93 | 109.13 | +11% |
| Dolphin X1 8B tok/s | 287.02 | 266 | +8% |
| Dolphin 3.0 Llama 3.1 8B tok/s | 285.88 | 266.19 | +7% |
| Dolphin 3.0 R1 Mistral 24B tok/s | 120.95 | 107.83 | +12% |
| Gemma 3 27B tok/s | 93.44 | 84.77 | +10% |
| Qwen2.5-Coder 32B tok/s | 82.9 | 75.81 | +9% |
| Qwen3-Coder 30B A3B tok/s | 284.88 | 296.22 | -4% |
| QwQ 32B tok/s | 82.91 | 75.82 | +9% |
| StarCoder2 15B tok/s | 144.05 | 134.92 | +7% |
| FLUX.1 Schnell images/min | 59.54 | 58.36 | +2% |
| Z-Image Turbo (1024px) images/min | 48.63 | 39.42 | +23% |
| BiRefNet images/min | 1064.73 | 1310.97 | -19% |
| Depth Anything V2 Large images/min | 1069.95 | 918.71 | +16% |
| Depth Anything V2 Small images/min | 1491.6 | 1081.54 | +38% |
| SAM ViT-Base images/min | 1719.59 | 1431.27 | +20% |
| SAM ViT-Huge images/min | 399.37 | 320.64 | +25% |
| Swin2SR 4x Upscaler images/min | 58.29 | 25.48 | +129% |
| Qwen2.5 1.5B LoRA train tok/s | 22351.1 | 16034.9 | +39% |
| Qwen2.5 7B LoRA train tok/s | 14323.1 | 8514.3 | +68% |
| SmolLM2 1.7B LoRA train tok/s | 26114.3 | 18910.3 | +38% |
| TinyLlama 1.1B LoRA train tok/s | 26938.2 | 17522 | +54% |
| AI21-Jamba-Reasoning-3B tok/s | 380.88 | 363.09 | +5% |
| Olmo-3.1-32B-Think tok/s | 87.85 | 75.53 | +16% |
| Codestral 22B (Q3_K_M) tok/s | 107.98 | 87.21 | +24% |
| Dolphin-Mistral-24B-Venice-Edition tok/s | 121.87 | 105.63 | +15% |
| DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored tok/s | 292.55 | 262.76 | +11% |
| DeepSeek-Coder-V2-Lite tok/s | 340.53 | 309.14 | +10% |
| DeepSeek-R1-0528-Qwen3-8B tok/s | 270.35 | 245.26 | +10% |
| DeepSeek-R1-Distill-Llama-70B tok/s | 48.11 | 41.17 | +17% |
| DeepSeek-R1 Distill 14B (Q3_K_M) tok/s | 144.74 | 118.43 | +22% |
| DeepSeek-R1-Distill-Qwen-32B-abliterated tok/s | 83.19 | 73.48 | +13% |
| dolphin-2.9-llama3-8b tok/s | 290.47 | 262.47 | +11% |
| Dolphin X1 Trinity Nano 6B tok/s | 257.93 | 247.94 | +4% |
| EVA-Qwen2.5-14B-v0.2 tok/s | 159.46 | 145.15 | +10% |
| gemma-2-2b-it-abliterated tok/s | 418.18 | 392.65 | +7% |
| gemma-2-2b tok/s | 417.84 | 392.26 | +7% |
| gemma-2-9b tok/s | 202.22 | 174.7 | +16% |
| Gemma 3 12B (Q3_K_M) tok/s | 149.61 | 125.92 | +19% |
| gemma-3-1b tok/s | 533.32 | 504.17 | +6% |
| gemma-3-270m tok/s | 926.67 | 961.29 | -4% |
| GLM-4.7-Flash-REAP-23B-A3B tok/s | 178.49 | 168.41 | +6% |
| GLM-4.7-Flash tok/s | 196.52 | 186.05 | +6% |
| Josiefied-Qwen3-8B-abliterated-v1 tok/s | 270.09 | 245 | +10% |
| gpt-oss-20b tok/s | 355.56 | 346.8 | +3% |
| Hermes-3-Llama-3.2-3B tok/s | 446.78 | 423.72 | +5% |
| Hermes-4-70B tok/s | 48.1 | 41.2 | +17% |
| SmolLM3-3B tok/s | 420.3 | 399.14 | +5% |
| Qwen3-Coder-Next-abliterated tok/s | 199.12 | 190.32 | +5% |
| KAT-Coder-V2.5-Dev tok/s | 239.66 | 231.83 | +3% |
| L3-8B-Stheno-v3.2 tok/s | 293 | 261.83 | +12% |
| Laguna-XS-2.1 tok/s | 0 | 0 | n/a |
| LFM2.5-1.2B tok/s | 955.13 | 926.61 | +3% |
| LFM2.5-8B-A1B tok/s | 596.16 | 581.98 | +2% |
| Llama-2-7B tok/s | 315.9 | 284.62 | +11% |
| Llama-3.2-3B-Instruct-uncensored tok/s | 448.94 | 424.25 | +6% |
| Llama-3.3-70B-Instruct-abliterated tok/s | 48.1 | 41.15 | +17% |
| Meta-Llama-3.1-70B tok/s | 48.12 | 41.19 | +17% |
| Meta-Llama-3.1-8B tok/s | 293 | 261.7 | +12% |
| Phi-4-mini tok/s | 409.58 | 381.52 | +7% |
| Mistral-7B-Instruct-v0.1 tok/s | 306.44 | 276.29 | +11% |
| Mistral-7B-Instruct-v0.2 tok/s | 306.79 | 276.73 | +11% |
| Mistral-7B-Instruct-v0.3 tok/s | 306.46 | 276.61 | +11% |
| Mistral-Nemo-Instruct-2407 tok/s | 196.92 | 177.38 | +11% |
| Mistral Small 24B (Q3_K_M) tok/s | 108.37 | 84 | +29% |
| Nanbeige4.2-3B tok/s | 0 | 0 | n/a |
| NemoMix-Unleashed-12B tok/s | 197.06 | 177.28 | +11% |
| Nemotron-3-Nano-30B-A3B tok/s | 345.31 | 316.51 | +9% |
| Hermes-4-14B tok/s | 169.51 | 152 | +12% |
| Ornith-1.0-35B tok/s | 232.36 | 221.65 | +5% |
| Ornith-1.0-9B tok/s | 240.67 | 216.89 | +11% |
| phi-2 tok/s | 356.65 | 340.09 | +5% |
| Phi-3.5-mini tok/s | 352.25 | 345.07 | +2% |
| Phi-4 14B (Q3_K_M) tok/s | 165.06 | 138.87 | +19% |
| Qwen-AgentWorld-35B-A3B tok/s | 233.04 | 219.1 | +6% |
| Qwen3-0.6B tok/s | 742.64 | 710.97 | +4% |
| Qwen3-1.7B tok/s | 575.97 | 557.09 | +3% |
| Qwen3-14B tok/s | 169.52 | 152.2 | +11% |
| Qwen3-30B-A3B tok/s | 291.82 | 285.44 | +2% |
| Qwen3-4B-Instruct-2507 tok/s | 339.78 | 310.58 | +9% |
| Qwen3-8B tok/s | 270.35 | 244.2 | +11% |
| Qwen3-Coder-Next tok/s | 196.34 | 188.21 | +4% |
| Qwen3-Next-80B-A3B-Thinking tok/s | 194.98 | 188.14 | +4% |
| Qwen1.5-0.5B tok/s | 894.84 | 847.02 | +6% |
| Qwen2-1.5B tok/s | 583.79 | 536.52 | +9% |
| Qwen2.5-0.5B tok/s | 843.4 | 890 | -5% |
| Qwen2.5-1.5B tok/s | 584.96 | 537.24 | +9% |
| Uncensored tok/s | 159.48 | 144.92 | +10% |
| Qwen2.5-14B tok/s | 159.33 | 144.7 | +10% |
| Qwen2.5-32B tok/s | 83.41 | 73.5 | +13% |
| Qwen2.5-3B tok/s | 409.56 | 395.52 | +4% |
| Qwen2.5-72B tok/s | 48.28 | 40.78 | +18% |
| Qwen2.5-7B tok/s | 293.63 | 264.17 | +11% |
| Qwen2.5-Coder-0.5B tok/s | 843.44 | 892.81 | -6% |
| Qwen2.5-Coder-1.5B tok/s | 584.5 | 538.44 | +9% |
| Qwen2.5-Coder-14B-Instruct-abliterated tok/s | 159.5 | 145.01 | +10% |
| Qwen2.5-Coder 32B (Q3_K_M) tok/s | 74.28 | 58.05 | +28% |
| Qwen2.5-Coder-3B tok/s | 410.06 | 394 | +4% |
| Qwen2.5-Coder-7B-Instruct-abliterated tok/s | 292.21 | 264.24 | +11% |
| Qwen3 30B A3B (Q3_K_M) tok/s | 268.71 | 242.6 | +11% |
| Qwen3-4B-Instruct-2507 tok/s | 339.72 | 311.47 | +9% |
| Qwen3-4B-Thinking-2507 tok/s | 339.74 | 310.84 | +9% |
| Qwen3-Coder-Next tok/s | 199.39 | 193.79 | +3% |
| Qwen3-Next-80B-A3B-Thinking tok/s | 200.55 | 191.08 | +5% |
| Qwen3-Next-80B-A3B tok/s | 191.32 | 186.22 | +3% |
| SmolLM2-135M tok/s | 855.87 | 898.53 | -5% |
| Cydonia-24B-v4.3 tok/s | 121.9 | 105.49 | +16% |
Whole-job comparison
How long each card takes to finish a complete pipeline, not just one model. NVIDIA B300 is faster on 14 of 14; NVIDIA H100 80GB HBM3 on 0.
| Workflow | NVIDIA B300 | NVIDIA H100 80GB HBM3 | Difference | Cost per run |
|---|---|---|---|---|
| 50-image depth pass | 5 s | 5 s | NVIDIA B300 1.17x faster | $0.009 vs $0.002 |
| 500-image masking run | 78 s | 1.6 min | NVIDIA B300 1.25x faster | $0.150 vs $0.036 |
| 24-frame storyboard | 1.5 min | 1.8 min | NVIDIA B300 1.21x faster | $0.174 vs $0.040 |
| 6-panel comic page | 2 min | 3 min | NVIDIA B300 1.47x faster | $0.234 vs $0.066 |
| 60-second AI short film | 2.2 min | 2.5 min | NVIDIA B300 1.14x faster | $0.251 vs $0.055 |
| Character sheet, 12 poses | 2.2 min | 3.7 min | NVIDIA B300 1.65x faster | $0.257 vs $0.082 |
| 200-product catalogue cutout | 3.7 min | 8.1 min | NVIDIA B300 2.19x faster | $0.430 vs $0.181 |
| 10 short social clips | 4.7 min | 8 min | NVIDIA B300 1.68x faster | $0.548 vs $0.178 |
| 40-product photo shoot | 6.1 min | 11.1 min | NVIDIA B300 1.84x faster | $0.701 vs $0.248 |
| Full codebase review | 6.3 min | 6.9 min | NVIDIA B300 1.09x faster | $0.730 vs $0.154 |
| 40-product shoot, start to finish | 6.9 min | 12.9 min | NVIDIA B300 1.87x faster | $0.797 vs $0.287 |
| 20 long-form articles | 9.7 min | 11.4 min | NVIDIA B300 1.17x faster | $1.125 vs $0.253 |
| 100-photo restoration batch | 10.2 min | 23.8 min | NVIDIA B300 2.34x faster | $1.178 vs $0.531 |
| 100-photo restore and enlarge | 11.9 min | 27.8 min | NVIDIA B300 2.33x faster | $1.378 vs $0.618 |
Renting by the hour, NVIDIA H100 80GB HBM3 finishes 14 of 14 cheaper. The quicker card is not automatically the cheaper way to get the work done.
Cost to rent
| Card | Per hour |
|---|---|
| NVIDIA B300 | $6.940 |
| NVIDIA H100 80GB HBM3 | $1.336 |
NVIDIA H100 80GB HBM3 is 5.19x cheaper per hour. Cheapest on-demand rate we see across RunPod and Vast.
Specifications compared
| NVIDIA B300 | NVIDIA H100 80GB HBM3 | |
|---|---|---|
| VRAM | 288GB | 80GB |
| Architecture | Blackwell Ultra | Hopper |
| Memory bandwidth | 8 TB/s | 3350 GB/s |
| Boost clock | n/a | 1,980 MHz |
| TDP | 1400 W | 700 W |
| Launch MSRP | $40,000 | $30,000 |
| Release | 2025-11-01 | 2022-09-20 |
FAQ
- Which is better for ai & machine learning: NVIDIA B300 or NVIDIA H100 80GB HBM3?
- NVIDIA B300 performs better for ai & machine learning, winning 132 of 142 benchmarks in our suite with an average 15.2% advantage.
- What are the main hardware differences between NVIDIA B300 and NVIDIA H100 80GB HBM3?
- NVIDIA B300 has 288GB VRAM and a 1400W TDP, while NVIDIA H100 80GB HBM3 has 80GB VRAM and a 700W TDP.
- Where is the biggest performance difference between NVIDIA B300 and NVIDIA H100 80GB HBM3?
- FLUX.1 Kontext dev: NVIDIA B300 leads by roughly 142% (10.329 vs 4.264 images/min) in our testing.
NVIDIA B300 full review · NVIDIA H100 80GB HBM3 full review · All AI & Machine Learning rankings