NVIDIA A10G vs NVIDIA B200, AI & Machine Learning Comparison
NVIDIA B200 wins 142 of 145 benchmarks, averaging 205.4% faster.
Both cards were measured first-party on our bench, same suite, same test rig.
What the numbers say
The gap is widest in Qwen2.5 7B LoRA, where NVIDIA B200 leads by 1008% (1239 vs 13722 train tok/s); the closest fight is SmolLM2-135M (20% apart); VRAM decides part of this one: NVIDIA B200 runs 145 of our 12 AI workloads while the other card runs 137, models that don't fit score zero.
Benchmark results head-to-head
| Benchmark | NVIDIA A10G | NVIDIA B200 | Difference |
|---|---|---|---|
| Qwen3-4B tok/s | 129.87 | 317.93 | -59% |
| Llama-3.1-8B tok/s | 86.62 | 274.41 | -68% |
| Qwen2.5-Coder-14B tok/s | 47.07 | 150.96 | -69% |
| Qwen3-32B tok/s | 22.04 | 78.56 | -72% |
| Llama 3.3 70B tok/s | 0 | 44.54 | n/a |
| Stable Diffusion XL images/min | 6.38 | 46.12 | -86% |
| Z-Image Turbo images/min | 3.45 | 32.325 | -89% |
| FLUX.1 dev images/min | 0 | 13.136 | n/a |
| FLUX.1 Kontext dev images/min | 0 | 5.721 | n/a |
| Qwen-Image-Edit images/min | 0 | 4.86 | n/a |
| Wan 2.2 5B (720p) frames/s | 0.18 | 1.88 | -90% |
| Llama 3.2 1B tok/s | 400.6 | 881.78 | -55% |
| Qwen3 0.6B tok/s | 451.38 | 599.63 | -25% |
| Qwen3 1.7B tok/s | 267.08 | 551.76 | -52% |
| DeepSeek-R1 Distill 1.5B tok/s | 265.75 | 456.7 | -42% |
| Gemma 3 4B tok/s | 124.85 | 291.82 | -57% |
| Llama 3.2 3B tok/s | 170.47 | 422.09 | -60% |
| Mistral 7B v0.3 tok/s | 92.82 | 287.42 | -68% |
| Phi-4 Mini 3.8B tok/s | 144.42 | 375.74 | -62% |
| Qwen2.5-Coder 7B tok/s | 90.05 | 277.98 | -68% |
| SmolLM3 3B tok/s | 172.15 | 400.73 | -57% |
| DeepSeek-R1 Distill Llama 8B tok/s | 86.83 | 273.9 | -68% |
| DeepSeek-R1 Distill 14B tok/s | 46.96 | 150.69 | -69% |
| DeepSeek-R1 Distill 7B tok/s | 89.79 | 277.02 | -68% |
| Gemma 3 12B tok/s | 52.07 | 151.41 | -66% |
| Gemma 4 12B tok/s | 52.67 | 146.36 | -64% |
| Mistral Small 24B tok/s | 30.94 | 114.04 | -73% |
| Phi-4 14B tok/s | 48.18 | 163.85 | -71% |
| Qwen3 14B tok/s | 48.12 | 158.34 | -70% |
| Qwen3 30B A3B tok/s | 140.12 | 270.84 | -48% |
| Qwen3 8B tok/s | 84.09 | 253.45 | -67% |
| Codestral 22B tok/s | 32.09 | 112.16 | -71% |
| DeepSeek-R1 Distill 32B tok/s | 21.85 | 78.55 | -72% |
| Devstral Small 24B tok/s | 30.98 | 113.91 | -73% |
| Dolphin 2.9.1 Yi 1.5 34B tok/s | 21.12 | 79.35 | -73% |
| Dolphin Mistral 24B Venice tok/s | 30.95 | 113.93 | -73% |
| Dolphin X1 8B tok/s | 86.61 | 273.85 | -68% |
| Dolphin 3.0 Llama 3.1 8B tok/s | 86.54 | 274.05 | -68% |
| Dolphin 3.0 R1 Mistral 24B tok/s | 30.9 | 113.95 | -73% |
| Gemma 3 27B tok/s | 24.8 | 86.08 | -71% |
| Qwen2.5-Coder 32B tok/s | 21.92 | 78.53 | -72% |
| Qwen3-Coder 30B A3B tok/s | 142.77 | 277.68 | -49% |
| QwQ 32B tok/s | 21.87 | 78.47 | -72% |
| StarCoder2 15B tok/s | 42.19 | 138.64 | -70% |
| Z-Image Turbo (1024px) images/min | 6.32 | 62.47 | -90% |
| BiRefNet images/min | 432.33 | 1082.03 | -60% |
| Depth Anything V2 Large images/min | 740.74 | 1039.47 | -29% |
| Depth Anything V2 Small images/min | 941.18 | 1137.49 | -17% |
| SAM ViT-Base images/min | 398.72 | 1662.34 | -76% |
| SAM ViT-Huge images/min | 84.22 | 388.61 | -78% |
| Swin2SR 4x Upscaler images/min | 22.31 | 11.97 | +86% |
| Dolphin X1 Trinity Nano 6B tok/s | 171.23 | 209.31 | -18% |
| gpt-oss-20b tok/s | 144.61 | 351.42 | -59% |
| Olmo-3.1-32B-Think tok/s | 22.13 | 79.75 | -72% |
| Dolphin-Mistral-24B-Venice-Edition tok/s | 31 | 113.8 | -73% |
| DeepSeek-Coder-V2-Lite tok/s | 160.13 | 325.85 | -51% |
| DeepSeek-R1-0528-Qwen3-8B tok/s | 83.22 | 252.98 | -67% |
| EVA-Qwen2.5-14B-v0.2 tok/s | 47 | 150.67 | -69% |
| gemma-2-2b-it-abliterated tok/s | 174.56 | 392.32 | -56% |
| gemma-2-2b tok/s | 175.03 | 391.94 | -55% |
| gemma-2-9b tok/s | 57.37 | 187.45 | -69% |
| gemma-3-1b tok/s | 284.78 | 519.19 | -45% |
| gemma-3-270m tok/s | 597.49 | 889.52 | -33% |
| GLM-4.7-Flash tok/s | 103.24 | 183.82 | -44% |
| SmolLM3-3B tok/s | 171.66 | 399.05 | -57% |
| KAT-Coder-V2.5-Dev tok/s | 113.6 | 230.68 | -51% |
| L3-8B-Stheno-v3.2 tok/s | 86.8 | 274.48 | -68% |
| LFM2.5-1.2B tok/s | 429.96 | 916.79 | -53% |
| Llama-2-7B tok/s | 96.69 | 292 | -67% |
| Llama-3.2-3B-Instruct-uncensored tok/s | 167.63 | 419.24 | -60% |
| Meta-Llama-3.1-8B tok/s | 86.69 | 274.17 | -68% |
| Phi-4-mini tok/s | 144.27 | 375.06 | -62% |
| Mistral-7B-Instruct-v0.1 tok/s | 92.45 | 286.89 | -68% |
| Mistral-7B-Instruct-v0.2 tok/s | 92.74 | 286.37 | -68% |
| Mistral-7B-Instruct-v0.3 tok/s | 92.45 | 286.48 | -68% |
| Ornith-1.0-35B tok/s | 104.9 | 221.13 | -53% |
| Ornith-1.0-9B tok/s | 73.78 | 226.2 | -67% |
| Phi-3.5-mini tok/s | 145.97 | 325 | -55% |
| Qwen-AgentWorld-35B-A3B tok/s | 104.36 | 221.78 | -53% |
| Qwen3-0.6B tok/s | 446.74 | 596.73 | -25% |
| Qwen3-1.7B tok/s | 262.49 | 551.93 | -52% |
| Qwen3-14B tok/s | 47.8 | 158.19 | -70% |
| Qwen3-8B tok/s | 83.04 | 253.14 | -67% |
| Qwen2.5-0.5B tok/s | 508.78 | 818.27 | -38% |
| Qwen2.5-1.5B tok/s | 265.7 | 454.69 | -42% |
| Qwen2.5-14B tok/s | 47.02 | 150.57 | -69% |
| Qwen2.5-32B tok/s | 21.98 | 78.55 | -72% |
| Qwen2.5-3B tok/s | 168.23 | 388.14 | -57% |
| Qwen2.5-7B tok/s | 89.68 | 277.17 | -68% |
| Qwen2.5-Coder-1.5B tok/s | 264.79 | 455.35 | -42% |
| Qwen2.5-Coder-3B tok/s | 168 | 388.17 | -57% |
| GLM-4.7-Flash-REAP-23B-A3B tok/s | 97.48 | 167.76 | -42% |
| Josiefied-Qwen3-8B-abliterated-v1 tok/s | 83.26 | 253.28 | -67% |
| LFM2.5-8B-A1B tok/s | 268.04 | 575.15 | -53% |
| Mistral-Nemo-Instruct-2407 tok/s | 57 | 183.92 | -69% |
| Hermes-4-14B tok/s | 47.8 | 158.22 | -70% |
| phi-2 tok/s | 172.45 | 348.72 | -51% |
| Qwen3-30B-A3B tok/s | 140.14 | 271.69 | -48% |
| Qwen2.5-Coder-14B-Instruct-abliterated tok/s | 46.91 | 150.67 | -69% |
| Qwen3-4B-Instruct-2507 tok/s | 129.9 | 316.7 | -59% |
| Qwen3-4B-Thinking-2507 tok/s | 129.99 | 317.03 | -59% |
| SmolLM2-135M tok/s | 680.37 | 815.3 | -17% |
| Cydonia-24B-v4.3 tok/s | 31.04 | 113.81 | -73% |
| Qwen2.5 1.5B LoRA train tok/s | 4214.4 | 15322 | -72% |
| Qwen2.5 7B LoRA train tok/s | 1239 | 13722 | -91% |
| SmolLM2 1.7B LoRA train tok/s | 3668.3 | 18064.7 | -80% |
| TinyLlama 1.1B LoRA train tok/s | 5499.6 | 17993.5 | -69% |
| Qwen2.5 1.5B served serve tok/s | 2702.5 | 9186.4 | -71% |
| Qwen2.5 7B served serve tok/s | 858.7 | 5799.1 | -85% |
| SmolLM2 1.7B served serve tok/s | 2242.7 | 9672.9 | -77% |
| TinyLlama 1.1B served serve tok/s | 3558.2 | 11562.2 | -69% |
| FLUX.1 Schnell images/min | 0 | 81.41 | n/a |
| AI21-Jamba-Reasoning-3B tok/s | 166.29 | 360.54 | -54% |
| Codestral 22B (Q3_K_M) tok/s | 28.01 | 89.9 | -69% |
| DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored tok/s | 87.84 | 274.3 | -68% |
| DeepSeek-R1-Distill-Llama-70B tok/s | 0 | 44.49 | n/a |
| DeepSeek-R1 Distill 14B (Q3_K_M) tok/s | 41.72 | 120.47 | -65% |
| DeepSeek-R1-Distill-Qwen-32B-abliterated tok/s | 22.14 | 78.48 | -72% |
| dolphin-2.9-llama3-8b tok/s | 87.98 | 274.48 | -68% |
| Gemma 3 12B (Q3_K_M) tok/s | 46.29 | 125.31 | -63% |
| Hermes-3-Llama-3.2-3B tok/s | 169.23 | 420.6 | -60% |
| Hermes-4-70B tok/s | 0 | 44.49 | n/a |
| Qwen3-Coder-Next-abliterated tok/s | 0 | 186.57 | n/a |
| Laguna-XS-2.1 tok/s | 0 | 0 | n/a |
| Llama-3.3-70B-Instruct-abliterated tok/s | 0 | 44.48 | n/a |
| Meta-Llama-3.1-70B tok/s | 0 | 44.48 | n/a |
| Mistral Small 24B (Q3_K_M) tok/s | 26.8 | 87.72 | -69% |
| Nanbeige4.2-3B tok/s | 0 | 0 | n/a |
| NemoMix-Unleashed-12B tok/s | 57.52 | 183.93 | -69% |
| Nemotron-3-Nano-30B-A3B tok/s | 0 | 345.93 | n/a |
| Phi-4 14B (Q3_K_M) tok/s | 43.56 | 135.21 | -68% |
| Qwen3-4B-Instruct-2507 tok/s | 130.83 | 316.66 | -59% |
| Qwen3-Coder-Next tok/s | 0 | 186.39 | n/a |
| Qwen3-Next-80B-A3B-Thinking tok/s | 0 | 186.03 | n/a |
| Qwen1.5-0.5B tok/s | 542.19 | 699.73 | -23% |
| Qwen2-1.5B tok/s | 265.45 | 454.55 | -42% |
| Uncensored tok/s | 47.45 | 150.51 | -68% |
| Qwen2.5-72B tok/s | 0 | 45.34 | n/a |
| Qwen2.5-Coder-0.5B tok/s | 513.36 | 828.48 | -38% |
| Qwen2.5-Coder 32B (Q3_K_M) tok/s | 18.78 | 60.24 | -69% |
| Qwen2.5-Coder-7B-Instruct-abliterated tok/s | 91.41 | 276.57 | -67% |
| Qwen3-Coder-Next tok/s | 0 | 189.76 | n/a |
| Qwen3-Next-80B-A3B-Thinking tok/s | 0 | 188.78 | n/a |
| Qwen3-Next-80B-A3B tok/s | 0 | 179.71 | n/a |
| Qwen3 30B A3B (Q3_K_M) tok/s | 120.38 | 229.58 | -48% |
Whole-job comparison
How long each card takes to finish a complete pipeline, not just one model. NVIDIA A10G is faster on 1 of 6; NVIDIA B200 on 5.
| Workflow | NVIDIA A10G | NVIDIA B200 | Difference |
|---|---|---|---|
| 50-image depth pass | 7 s | 6 s | NVIDIA B200 1.16x faster |
| 500-image masking run | 6 min | 85 s | NVIDIA B200 4.23x faster |
| 24-frame storyboard | 9 min | 89 s | NVIDIA B200 6.02x faster |
| 200-product catalogue cutout | 9.6 min | 17.2 min | NVIDIA A10G 1.80x faster |
| Full codebase review | 21.3 min | 6.6 min | NVIDIA B200 3.22x faster |
| 10 short social clips | 50.7 min | 5.6 min | NVIDIA B200 9.05x faster |
Specifications compared
| NVIDIA A10G | NVIDIA B200 | |
|---|---|---|
| VRAM | 24GB | 192GB |
| Architecture | Ampere | Blackwell |
| Memory bandwidth | 600 GB/s | 8 TB/s |
| Boost clock | 1,710 MHz | n/a |
| TDP | 150 W | 1000 W |
| Launch MSRP | $2,800 | $40,000 |
| Release | 2021-11-01 | 2025-02-01 |
FAQ
- Which is better for ai & machine learning: NVIDIA A10G or NVIDIA B200?
- NVIDIA B200 performs better for ai & machine learning, winning 142 of 145 benchmarks in our suite with an average 205.4% advantage.
- What are the main hardware differences between NVIDIA A10G and NVIDIA B200?
- NVIDIA A10G has 24GB VRAM and a 150W TDP, while NVIDIA B200 has 192GB VRAM and a 1000W TDP.
- Does VRAM matter more than speed between NVIDIA A10G and NVIDIA B200?
- For AI, yes, NVIDIA B200 fits 21 more of our 12 workloads than NVIDIA A10G. 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 A10G and NVIDIA B200?
- Qwen2.5 7B LoRA: NVIDIA B200 leads by roughly 1008% (1239 vs 13722 train tok/s) in our testing.
NVIDIA A10G full review · NVIDIA B200 full review · All AI & Machine Learning rankings