NVIDIA A100 80GB SXM4 vs NVIDIA B300, AI & Machine Learning Comparison
NVIDIA B300 wins 140 of 142 benchmarks, averaging 93.2% 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 418% (1.993 vs 10.329 images/min); the closest fight is BiRefNet (16% apart).
Benchmark results head-to-head
| Benchmark | NVIDIA A100 80GB SXM4 | NVIDIA B300 | Difference |
|---|---|---|---|
| Qwen3 4B tok/s | 197 | 333.34 | -41% |
| Llama 3.1 8B tok/s | 162.57 | 287.23 | -43% |
| Qwen2.5-Coder 14B tok/s | 89.45 | 158.49 | -44% |
| Qwen3 32B tok/s | 45.53 | 83.68 | -46% |
| Llama 3.3 70B tok/s | 24.4 | 47.97 | -49% |
| Stable Diffusion XL images/min | 16.36 | 29.2 | -44% |
| Z-Image Turbo images/min | 9.975 | 39.675 | -75% |
| FLUX.1 dev images/min | 4.264 | 20.807 | -80% |
| FLUX.1 Kontext dev images/min | 1.993 | 10.329 | -81% |
| Qwen-Image-Edit images/min | 1.64 | 8.14 | -80% |
| LTX-Video (distilled) frames/s | 8.95 | 31.87 | -72% |
| Wan 2.2 5B (720p) frames/s | 0.66 | 2.94 | -78% |
| DeepSeek-R1 Distill Llama 8B tok/s | 159.37 | 283.77 | -44% |
| DeepSeek-R1 Distill 1.5B tok/s | 327.66 | 562.14 | -42% |
| DeepSeek-R1 Distill 14B tok/s | 87 | 156.33 | -44% |
| DeepSeek-R1 Distill 7B tok/s | 162.96 | 284.77 | -43% |
| Gemma 3 12B tok/s | 91.45 | 161.27 | -43% |
| Gemma 3 4B tok/s | 176.94 | 301.07 | -41% |
| Gemma 4 12B tok/s | 92.43 | 155.96 | -41% |
| Llama 3.2 1B tok/s | 525.32 | 889.19 | -41% |
| Llama 3.2 3B tok/s | 264.25 | 432.24 | -39% |
| Mistral 7B v0.3 tok/s | 171.92 | 298.95 | -42% |
| Mistral Small 24B tok/s | 61.38 | 119.3 | -49% |
| Phi-4 14B tok/s | 95.22 | 177.41 | -46% |
| Phi-4 Mini 3.8B tok/s | 239.17 | 398.16 | -40% |
| Qwen2.5-Coder 7B tok/s | 164.6 | 286.5 | -43% |
| Qwen3 0.6B tok/s | 428.39 | 718.58 | -40% |
| Qwen3 1.7B tok/s | 343.58 | 555.1 | -38% |
| Qwen3 14B tok/s | 90.64 | 165.44 | -45% |
| Qwen3 30B A3B tok/s | 177.32 | 284.66 | -38% |
| Qwen3 8B tok/s | 149.96 | 261.4 | -43% |
| SmolLM3 3B tok/s | 249.76 | 411.25 | -39% |
| Codestral 22B tok/s | 63.26 | 119.81 | -47% |
| DeepSeek-R1 Distill 32B tok/s | 43.11 | 82.9 | -48% |
| Devstral Small 24B tok/s | 61.63 | 121.18 | -49% |
| Dolphin 2.9.1 Yi 1.5 34B tok/s | 43.47 | 85.15 | -49% |
| Dolphin Mistral 24B Venice tok/s | 62.72 | 120.93 | -48% |
| Dolphin X1 8B tok/s | 158.62 | 287.02 | -45% |
| Dolphin 3.0 Llama 3.1 8B tok/s | 158.63 | 285.88 | -45% |
| Dolphin 3.0 R1 Mistral 24B tok/s | 61.17 | 120.95 | -49% |
| Gemma 3 27B tok/s | 48.43 | 93.44 | -48% |
| Qwen2.5-Coder 32B tok/s | 43.36 | 82.9 | -48% |
| Qwen3-Coder 30B A3B tok/s | 182.26 | 284.88 | -36% |
| QwQ 32B tok/s | 43.27 | 82.91 | -48% |
| StarCoder2 15B tok/s | 79.22 | 144.05 | -45% |
| FLUX.1 Schnell images/min | 28.39 | 59.54 | -52% |
| Z-Image Turbo (1024px) images/min | 18.65 | 48.63 | -62% |
| BiRefNet images/min | 916.67 | 1064.73 | -14% |
| Depth Anything V2 Large images/min | 876.29 | 1069.95 | -18% |
| Depth Anything V2 Small images/min | 925.15 | 1491.6 | -38% |
| SAM ViT-Base images/min | 744.79 | 1719.59 | -57% |
| SAM ViT-Huge images/min | 145.7 | 399.37 | -64% |
| Swin2SR 4x Upscaler images/min | 28.72 | 58.29 | -51% |
| Qwen2.5 1.5B LoRA train tok/s | 8252.8 | 22351.1 | -63% |
| Qwen2.5 7B LoRA train tok/s | 3900.6 | 14323.1 | -73% |
| SmolLM2 1.7B LoRA train tok/s | 9750.8 | 26114.3 | -63% |
| TinyLlama 1.1B LoRA train tok/s | 9553.3 | 26938.2 | -65% |
| DeepSeek-R1-Distill-Llama-70B tok/s | 22.99 | 48.11 | -52% |
| DeepSeek-R1-Distill-Qwen-32B-abliterated tok/s | 43.48 | 83.19 | -48% |
| Llama-3.3-70B-Instruct-abliterated tok/s | 22.92 | 48.1 | -52% |
| Meta-Llama-3.1-70B tok/s | 22.92 | 48.12 | -52% |
| Nemotron-3-Nano-30B-A3B tok/s | 204.06 | 345.31 | -41% |
| Qwen2.5-Coder 32B (Q3_K_M) tok/s | 31.99 | 74.28 | -57% |
| AI21-Jamba-Reasoning-3B tok/s | 239.23 | 380.88 | -37% |
| Olmo-3.1-32B-Think tok/s | 46.49 | 87.85 | -47% |
| Codestral 22B (Q3_K_M) tok/s | 49.52 | 107.98 | -54% |
| Dolphin-Mistral-24B-Venice-Edition tok/s | 65.77 | 121.87 | -46% |
| DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored tok/s | 164.46 | 292.55 | -44% |
| DeepSeek-Coder-V2-Lite tok/s | 213.91 | 340.53 | -37% |
| DeepSeek-R1-0528-Qwen3-8B tok/s | 152.76 | 270.35 | -43% |
| DeepSeek-R1 Distill 14B (Q3_K_M) tok/s | 67.13 | 144.74 | -54% |
| dolphin-2.9-llama3-8b tok/s | 164.09 | 290.47 | -44% |
| Dolphin X1 Trinity Nano 6B tok/s | 158.41 | 257.93 | -39% |
| EVA-Qwen2.5-14B-v0.2 tok/s | 90.35 | 159.46 | -43% |
| gemma-2-2b-it-abliterated tok/s | 244.41 | 418.18 | -42% |
| gemma-2-2b tok/s | 244.52 | 417.84 | -41% |
| gemma-2-9b tok/s | 108.68 | 202.22 | -46% |
| Gemma 3 12B (Q3_K_M) tok/s | 71.88 | 149.61 | -52% |
| gemma-3-1b tok/s | 324.08 | 533.32 | -39% |
| gemma-3-270m tok/s | 617.58 | 926.67 | -33% |
| GLM-4.7-Flash-REAP-23B-A3B tok/s | 112.75 | 178.49 | -37% |
| GLM-4.7-Flash tok/s | 124.08 | 196.52 | -37% |
| Josiefied-Qwen3-8B-abliterated-v1 tok/s | 152.29 | 270.09 | -44% |
| gpt-oss-20b tok/s | 212.63 | 355.56 | -40% |
| Hermes-3-Llama-3.2-3B tok/s | 269.13 | 446.78 | -40% |
| Hermes-4-70B tok/s | 24.47 | 48.1 | -49% |
| SmolLM3-3B tok/s | 254.6 | 420.3 | -39% |
| Qwen3-Coder-Next-abliterated tok/s | 119.56 | 199.12 | -40% |
| KAT-Coder-V2.5-Dev tok/s | 146.38 | 239.66 | -39% |
| L3-8B-Stheno-v3.2 tok/s | 164.51 | 293 | -44% |
| Laguna-XS-2.1 tok/s | 0 | 0 | n/a |
| LFM2.5-1.2B tok/s | 576.95 | 955.13 | -40% |
| LFM2.5-8B-A1B tok/s | 369.01 | 596.16 | -38% |
| Llama-2-7B tok/s | 180.09 | 315.9 | -43% |
| Llama-3.2-3B-Instruct-uncensored tok/s | 269.13 | 448.94 | -40% |
| Meta-Llama-3.1-8B tok/s | 164.24 | 293 | -44% |
| Phi-4-mini tok/s | 243.38 | 409.58 | -41% |
| Mistral-7B-Instruct-v0.1 tok/s | 173.87 | 306.44 | -43% |
| Mistral-7B-Instruct-v0.2 tok/s | 173.8 | 306.79 | -43% |
| Mistral-7B-Instruct-v0.3 tok/s | 173.27 | 306.46 | -43% |
| Mistral-Nemo-Instruct-2407 tok/s | 111.07 | 196.92 | -44% |
| Mistral Small 24B (Q3_K_M) tok/s | 46.63 | 108.37 | -57% |
| Nanbeige4.2-3B tok/s | 0 | 0 | n/a |
| NemoMix-Unleashed-12B tok/s | 110.99 | 197.06 | -44% |
| Hermes-4-14B tok/s | 94.48 | 169.51 | -44% |
| Ornith-1.0-35B tok/s | 138.8 | 232.36 | -40% |
| Ornith-1.0-9B tok/s | 134.67 | 240.67 | -44% |
| phi-2 tok/s | 235.55 | 356.65 | -34% |
| Phi-3.5-mini tok/s | 229.46 | 352.25 | -35% |
| Phi-4 14B (Q3_K_M) tok/s | 82.68 | 165.06 | -50% |
| Qwen-AgentWorld-35B-A3B tok/s | 139.69 | 233.04 | -40% |
| Qwen3-0.6B tok/s | 432.66 | 742.64 | -42% |
| Qwen3-1.7B tok/s | 350.4 | 575.97 | -39% |
| Qwen3-14B tok/s | 94.35 | 169.52 | -44% |
| Qwen3-30B-A3B tok/s | 178.14 | 291.82 | -39% |
| Qwen3-4B-Instruct-2507 tok/s | 199.39 | 339.78 | -41% |
| Qwen3-8B tok/s | 152.8 | 270.35 | -43% |
| Qwen3-Coder-Next tok/s | 118.62 | 196.34 | -40% |
| Qwen3-Next-80B-A3B-Thinking tok/s | 118.93 | 194.98 | -39% |
| Qwen1.5-0.5B tok/s | 540.32 | 894.84 | -40% |
| Qwen2-1.5B tok/s | 333.79 | 583.79 | -43% |
| Qwen2.5-0.5B tok/s | 571.49 | 843.4 | -32% |
| Qwen2.5-1.5B tok/s | 335.29 | 584.96 | -43% |
| Uncensored tok/s | 89.43 | 159.48 | -44% |
| Qwen2.5-14B tok/s | 90.46 | 159.33 | -43% |
| Qwen2.5-32B tok/s | 45.6 | 83.41 | -45% |
| Qwen2.5-3B tok/s | 248.47 | 409.56 | -39% |
| Qwen2.5-72B tok/s | 23.95 | 48.28 | -50% |
| Qwen2.5-7B tok/s | 166.92 | 293.63 | -43% |
| Qwen2.5-Coder-0.5B tok/s | 562.59 | 843.44 | -33% |
| Qwen2.5-Coder-1.5B tok/s | 333.87 | 584.5 | -43% |
| Qwen2.5-Coder-14B-Instruct-abliterated tok/s | 90.28 | 159.5 | -43% |
| Qwen2.5-Coder-3B tok/s | 248.7 | 410.06 | -39% |
| Qwen2.5-Coder-7B-Instruct-abliterated tok/s | 166.84 | 292.21 | -43% |
| Qwen3 30B A3B (Q3_K_M) tok/s | 146.72 | 268.71 | -45% |
| Qwen3-4B-Instruct-2507 tok/s | 199.65 | 339.72 | -41% |
| Qwen3-4B-Thinking-2507 tok/s | 199.71 | 339.74 | -41% |
| Qwen3-Coder-Next tok/s | 120.71 | 199.39 | -39% |
| Qwen3-Next-80B-A3B-Thinking tok/s | 120.98 | 200.55 | -40% |
| Qwen3-Next-80B-A3B tok/s | 117.44 | 191.32 | -39% |
| SmolLM2-135M tok/s | 554.44 | 855.87 | -35% |
| Cydonia-24B-v4.3 tok/s | 65.82 | 121.9 | -46% |
Whole-job comparison
How long each card takes to finish a complete pipeline, not just one model. NVIDIA A100 80GB SXM4 is faster on 0 of 14; NVIDIA B300 on 14.
| Workflow | NVIDIA A100 80GB SXM4 | NVIDIA B300 | Difference | Cost per run |
|---|---|---|---|---|
| 50-image depth pass | 5 s | 5 s | NVIDIA B300 1.07x faster | $0.002 vs $0.009 |
| 500-image masking run | 3.5 min | 78 s | NVIDIA B300 2.69x faster | $0.081 vs $0.150 |
| 24-frame storyboard | 3.7 min | 1.5 min | NVIDIA B300 2.45x faster | $0.085 vs $0.174 |
| 60-second AI short film | 4.7 min | 2.2 min | NVIDIA B300 2.15x faster | $0.108 vs $0.251 |
| 6-panel comic page | 6 min | 2 min | NVIDIA B300 2.96x faster | $0.139 vs $0.234 |
| 200-product catalogue cutout | 7.3 min | 3.7 min | NVIDIA B300 1.96x faster | $0.169 vs $0.430 |
| Character sheet, 12 poses | 7.6 min | 2.2 min | NVIDIA B300 3.40x faster | $0.175 vs $0.257 |
| Full codebase review | 11.2 min | 6.3 min | NVIDIA B300 1.77x faster | $0.259 vs $0.730 |
| 10 short social clips | 15.1 min | 4.7 min | NVIDIA B300 3.19x faster | $0.350 vs $0.548 |
| 20 long-form articles | 19.1 min | 9.7 min | NVIDIA B300 1.97x faster | $0.443 vs $1.125 |
| 40-product photo shoot | 23.5 min | 6.1 min | NVIDIA B300 3.87x faster | $0.543 vs $0.701 |
| 40-product shoot, start to finish | 25 min | 6.9 min | NVIDIA B300 3.63x faster | $0.579 vs $0.797 |
| 100-photo restoration batch | 50.8 min | 10.2 min | NVIDIA B300 4.99x faster | $1.177 vs $1.178 |
| 100-photo restore and enlarge | 54.3 min | 11.9 min | NVIDIA B300 4.56x faster | $1.258 vs $1.378 |
Renting by the hour, NVIDIA A100 80GB SXM4 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 A100 80GB SXM4 | $1.390 |
| NVIDIA B300 | $6.940 |
NVIDIA A100 80GB SXM4 is 4.99x cheaper per hour. Cheapest on-demand rate we see across RunPod and Vast.
Specifications compared
| NVIDIA A100 80GB SXM4 | NVIDIA B300 | |
|---|---|---|
| VRAM | 80GB | 288GB |
| Architecture | Ampere | Blackwell Ultra |
| Memory bandwidth | 2039 GB/s | 8 TB/s |
| Boost clock | 1,410 MHz | n/a |
| TDP | 400 W | 1400 W |
| Launch MSRP | $17,000 | $40,000 |
| Release | 2020-11-16 | 2025-11-01 |
FAQ
- Which is better for ai & machine learning: NVIDIA A100 80GB SXM4 or NVIDIA B300?
- NVIDIA B300 performs better for ai & machine learning, winning 140 of 142 benchmarks in our suite with an average 93.2% advantage.
- What are the main hardware differences between NVIDIA A100 80GB SXM4 and NVIDIA B300?
- NVIDIA A100 80GB SXM4 has 80GB VRAM and a 400W TDP, while NVIDIA B300 has 288GB VRAM and a 1400W TDP.
- Where is the biggest performance difference between NVIDIA A100 80GB SXM4 and NVIDIA B300?
- FLUX.1 Kontext dev: NVIDIA B300 leads by roughly 418% (1.993 vs 10.329 images/min) in our testing.
NVIDIA A100 80GB SXM4 full review · NVIDIA B300 full review · All AI & Machine Learning rankings