NVIDIA A100 40GB SXM4 vs NVIDIA B300, AI & Machine Learning Comparison
NVIDIA B300 wins 140 of 142 benchmarks, averaging 101.6% faster.
NVIDIA A100 40GB SXM4's numbers are anchored estimates calibrated against our measured cards, pending first-party measurement. Treat small gaps as ties.
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 Qwen2.5-Coder-0.5B (56% apart); VRAM decides part of this one: NVIDIA B300 runs 140 of our 12 AI workloads while the other card runs 135, models that don't fit score zero.
Benchmark results head-to-head
| Benchmark | NVIDIA A100 40GB SXM4 | NVIDIA B300 | Difference |
|---|---|---|---|
| Qwen3-4B tok/s | 193.14 | 333.34 | -42% |
| Llama-3.1-8B tok/s | 157.26 | 287.23 | -45% |
| Qwen2.5-Coder-14B tok/s | 86.2 | 158.49 | -46% |
| Qwen3-32B tok/s | 43.8 | 83.68 | -48% |
| Llama-3.3-70B tok/s | 0 | 47.97 | n/a |
| Stable Diffusion XL images/min | 18.576 | 29.2 | -36% |
| 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 | 0 | 8.14 | n/a |
| 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 | 157.16 | 283.77 | -45% |
| DeepSeek-R1 Distill 1.5B tok/s | 320.78 | 562.14 | -43% |
| DeepSeek-R1 Distill 14B tok/s | 86.39 | 156.33 | -45% |
| DeepSeek-R1 Distill 7B tok/s | 159.09 | 284.77 | -44% |
| Gemma 3 12B tok/s | 88.97 | 161.27 | -45% |
| Gemma 3 4B tok/s | 174.14 | 301.07 | -42% |
| Gemma 4 12B tok/s | 87.14 | 155.96 | -44% |
| Llama 3.2 1B tok/s | 532.08 | 889.19 | -40% |
| Llama 3.2 3B tok/s | 261.24 | 432.24 | -40% |
| Mistral 7B v0.3 tok/s | 165.9 | 298.95 | -45% |
| Mistral Small 24B tok/s | 62.42 | 119.3 | -48% |
| Phi-4 14B tok/s | 98.65 | 177.41 | -44% |
| Phi-4 Mini 3.8B tok/s | 236.71 | 398.16 | -41% |
| Qwen2.5-Coder 7B tok/s | 159.9 | 286.5 | -44% |
| Qwen3 0.6B tok/s | 417.88 | 718.58 | -42% |
| Qwen3 1.7B tok/s | 340.76 | 555.1 | -39% |
| Qwen3 14B tok/s | 90.01 | 165.44 | -46% |
| Qwen3 30B A3B tok/s | 169.1 | 284.66 | -41% |
| Qwen3 8B tok/s | 146.21 | 261.4 | -44% |
| SmolLM3 3B tok/s | 246.25 | 411.25 | -40% |
| Codestral 22B tok/s | 63.74 | 119.81 | -47% |
| DeepSeek-R1 Distill 32B tok/s | 43.13 | 82.9 | -48% |
| Devstral Small 24B tok/s | 62.4 | 121.18 | -49% |
| Dolphin 2.9.1 Yi 1.5 34B tok/s | 42.97 | 85.15 | -50% |
| Dolphin Mistral 24B Venice tok/s | 62.46 | 120.93 | -48% |
| Dolphin X1 8B tok/s | 156.3 | 287.02 | -46% |
| Dolphin 3.0 Llama 3.1 8B tok/s | 156.55 | 285.88 | -45% |
| Dolphin 3.0 R1 Mistral 24B tok/s | 62.4 | 120.95 | -48% |
| Gemma 3 27B tok/s | 47.47 | 93.44 | -49% |
| Qwen2.5-Coder 32B tok/s | 43.14 | 82.9 | -48% |
| Qwen3-Coder 30B A3B tok/s | 173.47 | 284.88 | -39% |
| QwQ 32B tok/s | 43.02 | 82.91 | -48% |
| StarCoder2 15B tok/s | 80.25 | 144.05 | -44% |
| FLUX.1 Schnell images/min | 28.44 | 59.54 | -52% |
| Z-Image Turbo (1024px) images/min | 18.7 | 48.63 | -62% |
| BiRefNet images/min | 611.56 | 1064.73 | -43% |
| Depth Anything V2 Large images/min | 536.74 | 1069.95 | -50% |
| Depth Anything V2 Small images/min | 613.42 | 1491.6 | -59% |
| SAM ViT-Base images/min | 696.77 | 1719.59 | -59% |
| SAM ViT-Huge images/min | 146.81 | 399.37 | -63% |
| Swin2SR 4x Upscaler images/min | 17.25 | 58.29 | -70% |
| Dolphin X1 Trinity Nano 6B tok/s | 153.27 | 257.93 | -41% |
| gpt-oss-20b tok/s | 205.1 | 355.56 | -42% |
| Olmo-3.1-32B-Think tok/s | 43.67 | 87.85 | -50% |
| Dolphin-Mistral-24B-Venice-Edition tok/s | 62.56 | 121.87 | -49% |
| DeepSeek-Coder-V2-Lite tok/s | 205.42 | 340.53 | -40% |
| DeepSeek-R1-0528-Qwen3-8B tok/s | 145.84 | 270.35 | -46% |
| EVA-Qwen2.5-14B-v0.2 tok/s | 86.48 | 159.46 | -46% |
| Qwen2.5 1.5B LoRA train tok/s | 6349.7 | 22351.1 | -72% |
| Qwen2.5 7B LoRA train tok/s | 3670.8 | 14323.1 | -74% |
| SmolLM2 1.7B LoRA train tok/s | 6938.8 | 26114.3 | -73% |
| TinyLlama 1.1B LoRA train tok/s | 7471 | 26938.2 | -72% |
| gemma-2-2b-it-abliterated tok/s | 232.27 | 418.18 | -44% |
| gemma-2-2b tok/s | 234.68 | 417.84 | -44% |
| gemma-2-9b tok/s | 101.97 | 202.22 | -50% |
| gemma-3-1b tok/s | 310.69 | 533.32 | -42% |
| gemma-3-270m tok/s | 574.24 | 926.67 | -38% |
| GLM-4.7-Flash tok/s | 119.1 | 196.52 | -39% |
| SmolLM3-3B tok/s | 247.66 | 420.3 | -41% |
| KAT-Coder-V2.5-Dev tok/s | 137.31 | 239.66 | -43% |
| LFM2.5-1.2B tok/s | 572.69 | 955.13 | -40% |
| Llama-2-7B tok/s | 172.53 | 315.9 | -45% |
| Llama-3.2-3B-Instruct-uncensored tok/s | 260.83 | 448.94 | -42% |
| Meta-Llama-3.1-8B tok/s | 156.68 | 293 | -47% |
| Phi-4-mini tok/s | 237.38 | 409.58 | -42% |
| Mistral-7B-Instruct-v0.1 tok/s | 166.39 | 306.44 | -46% |
| Mistral-7B-Instruct-v0.2 tok/s | 166.8 | 306.79 | -46% |
| Mistral-7B-Instruct-v0.3 tok/s | 167.06 | 306.46 | -45% |
| Ornith-1.0-35B tok/s | 131.77 | 232.36 | -43% |
| Ornith-1.0-9B tok/s | 126.79 | 240.67 | -47% |
| Phi-3.5-mini tok/s | 222.53 | 352.25 | -37% |
| Qwen-AgentWorld-35B-A3B tok/s | 131.16 | 233.04 | -44% |
| Qwen3-0.6B tok/s | 408.39 | 742.64 | -45% |
| Qwen3-1.7B tok/s | 341.85 | 575.97 | -41% |
| Qwen3-14B tok/s | 90.07 | 169.52 | -47% |
| Qwen3-8B tok/s | 145.96 | 270.35 | -46% |
| Qwen2.5-0.5B tok/s | 517.09 | 843.4 | -39% |
| Qwen2.5-1.5B tok/s | 315.59 | 584.96 | -46% |
| Qwen2.5-14B tok/s | 86.07 | 159.33 | -46% |
| Qwen2.5-32B tok/s | 43.23 | 83.41 | -48% |
| Qwen2.5-3B tok/s | 240.48 | 409.56 | -41% |
| Qwen2.5-7B tok/s | 159.81 | 293.63 | -46% |
| Qwen2.5-Coder-1.5B tok/s | 322.96 | 584.5 | -45% |
| Qwen2.5-Coder-3B tok/s | 241.51 | 410.06 | -41% |
| AI21-Jamba-Reasoning-3B tok/s | 231.15 | 380.88 | -39% |
| Codestral 22B (Q3_K_M) tok/s | 48.37 | 107.98 | -55% |
| DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored tok/s | 156.62 | 292.55 | -46% |
| DeepSeek-R1 Distill 14B (Q3_K_M) tok/s | 65.33 | 144.74 | -55% |
| DeepSeek-R1-Distill-Qwen-32B-abliterated tok/s | 43.07 | 83.19 | -48% |
| dolphin-2.9-llama3-8b tok/s | 153.34 | 290.47 | -47% |
| Gemma 3 12B (Q3_K_M) tok/s | 69.7 | 149.61 | -53% |
| GLM-4.7-Flash-REAP-23B-A3B tok/s | 109.1 | 178.49 | -39% |
| Josiefied-Qwen3-8B-abliterated-v1 tok/s | 146.8 | 270.09 | -46% |
| Hermes-3-Llama-3.2-3B tok/s | 261 | 446.78 | -42% |
| L3-8B-Stheno-v3.2 tok/s | 156.55 | 293 | -47% |
| LFM2.5-8B-A1B tok/s | 351.47 | 596.16 | -41% |
| Mistral-Nemo-Instruct-2407 tok/s | 106.33 | 196.92 | -46% |
| Mistral Small 24B (Q3_K_M) tok/s | 45.34 | 108.37 | -58% |
| NemoMix-Unleashed-12B tok/s | 105.05 | 197.06 | -47% |
| Nemotron-3-Nano-30B-A3B tok/s | 192.44 | 345.31 | -44% |
| Hermes-4-14B tok/s | 89.97 | 169.51 | -47% |
| phi-2 tok/s | 228.1 | 356.65 | -36% |
| Phi-4 14B (Q3_K_M) tok/s | 80.71 | 165.06 | -51% |
| Qwen3-30B-A3B tok/s | 167.1 | 291.82 | -43% |
| Qwen3-4B-Instruct-2507 tok/s | 192.89 | 339.78 | -43% |
| Qwen1.5-0.5B tok/s | 497.73 | 894.84 | -44% |
| Qwen2-1.5B tok/s | 322.09 | 583.79 | -45% |
| Uncensored tok/s | 85.82 | 159.48 | -46% |
| Qwen2.5-Coder-0.5B tok/s | 542.36 | 843.44 | -36% |
| Qwen2.5-Coder-14B-Instruct-abliterated tok/s | 86.34 | 159.5 | -46% |
| Qwen2.5-Coder 32B (Q3_K_M) tok/s | 31.04 | 74.28 | -58% |
| Qwen2.5-Coder-7B-Instruct-abliterated tok/s | 159.76 | 292.21 | -45% |
| Qwen3 30B A3B (Q3_K_M) tok/s | 142.78 | 268.71 | -47% |
| Qwen3-4B-Instruct-2507 tok/s | 193.39 | 339.72 | -43% |
| Qwen3-4B-Thinking-2507 tok/s | 189.89 | 339.74 | -44% |
| SmolLM2-135M tok/s | 544.06 | 855.87 | -36% |
| Cydonia-24B-v4.3 tok/s | 62.41 | 121.9 | -49% |
| Qwen3-Coder-Next-abliterated tok/s | 0 | 199.12 | n/a |
| Laguna-XS-2.1 tok/s | 0 | 0 | n/a |
| Nanbeige4.2-3B tok/s | 0 | 0 | n/a |
| Qwen3-Coder-Next tok/s | 0 | 196.34 | n/a |
| Qwen3-Next-80B-A3B-Thinking tok/s | 0 | 194.98 | n/a |
| Qwen2.5-72B tok/s | 0 | 48.28 | n/a |
| Qwen3-Coder-Next tok/s | 0 | 199.39 | n/a |
| Qwen3-Next-80B-A3B-Thinking tok/s | 0 | 200.55 | n/a |
| Qwen3-Next-80B-A3B tok/s | 0 | 191.32 | n/a |
| DeepSeek-R1-Distill-Llama-70B tok/s | 0 | 48.11 | n/a |
| Hermes-4-70B tok/s | 0 | 48.1 | n/a |
| Llama-3.3-70B-Instruct-abliterated tok/s | 0 | 48.1 | n/a |
| Meta-Llama-3.1-70B tok/s | 0 | 48.12 | n/a |
Whole-job comparison
How long each card takes to finish a complete pipeline, not just one model. NVIDIA A100 40GB SXM4 is faster on 0 of 13; NVIDIA B300 on 13.
| Workflow | NVIDIA A100 40GB SXM4 | NVIDIA B300 | Difference | Cost per run |
|---|---|---|---|---|
| 50-image depth pass | 10 s | 5 s | NVIDIA B300 2.07x faster | $0.003 vs $0.009 |
| 24-frame storyboard | 3 min | 1.5 min | NVIDIA B300 2.03x faster | $0.051 vs $0.174 |
| 500-image masking run | 3.5 min | 78 s | NVIDIA B300 2.70x faster | $0.058 vs $0.150 |
| 60-second AI short film | 4 min | 2.2 min | NVIDIA B300 1.83x faster | $0.066 vs $0.251 |
| 6-panel comic page | 4.8 min | 2 min | NVIDIA B300 2.37x faster | $0.080 vs $0.234 |
| Character sheet, 12 poses | 6.3 min | 2.2 min | NVIDIA B300 2.81x faster | $0.104 vs $0.257 |
| Full codebase review | 11.7 min | 6.3 min | NVIDIA B300 1.85x faster | $0.195 vs $0.730 |
| 200-product catalogue cutout | 12.2 min | 3.7 min | NVIDIA B300 3.27x faster | $0.203 vs $0.430 |
| 10 short social clips | 13.7 min | 4.7 min | NVIDIA B300 2.89x faster | $0.228 vs $0.548 |
| 40-product photo shoot | 22.2 min | 6.1 min | NVIDIA B300 3.67x faster | $0.370 vs $0.701 |
| 40-product shoot, start to finish | 24.8 min | 6.9 min | NVIDIA B300 3.61x faster | $0.414 vs $0.797 |
| 100-photo restoration batch | 50.2 min | 10.2 min | NVIDIA B300 4.93x faster | $0.836 vs $1.178 |
| 100-photo restore and enlarge | 56 min | 11.9 min | NVIDIA B300 4.70x faster | $0.934 vs $1.378 |
Renting by the hour, NVIDIA A100 40GB SXM4 finishes 13 of 13 cheaper. The quicker card is not automatically the cheaper way to get the work done.
Cost to rent
| Card | Per hour |
|---|---|
| NVIDIA A100 40GB SXM4 | $1.000 |
| NVIDIA B300 | $6.940 |
NVIDIA A100 40GB SXM4 is 6.94x cheaper per hour. Cheapest on-demand rate we see across RunPod and Vast.
Specifications compared
| NVIDIA A100 40GB SXM4 | NVIDIA B300 | |
|---|---|---|
| VRAM | 40GB | 288GB |
| Architecture | Ampere | Blackwell Ultra |
| Memory bandwidth | 1555 GB/s | 8 TB/s |
| Boost clock | 1,410 MHz | n/a |
| TDP | 400 W | 1400 W |
| Launch MSRP | $12,000 | $40,000 |
| Release | 2020-05-14 | 2025-11-01 |
FAQ
- Which is better for ai & machine learning: NVIDIA A100 40GB SXM4 or NVIDIA B300?
- NVIDIA B300 performs better for ai & machine learning, winning 140 of 142 benchmarks in our suite with an average 101.6% advantage.
- What are the main hardware differences between NVIDIA A100 40GB SXM4 and NVIDIA B300?
- NVIDIA A100 40GB SXM4 has 40GB VRAM and a 400W TDP, while NVIDIA B300 has 288GB VRAM and a 1400W TDP.
- Does VRAM matter more than speed between NVIDIA A100 40GB SXM4 and NVIDIA B300?
- For AI, yes, NVIDIA B300 fits 13 more of our 12 workloads than NVIDIA A100 40GB SXM4. 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 A100 40GB 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 40GB SXM4 full review · NVIDIA B300 full review · All AI & Machine Learning rankings