NVIDIA A10G vs NVIDIA B300, AI & Machine Learning Comparison
NVIDIA B300 wins 139 of 141 benchmarks, averaging 228.9% faster.
Both cards were measured first-party on our bench, same suite, same test rig.
What the numbers say
The gap is widest in Wan 2.2 5B (720p), where NVIDIA B300 leads by 1533% (0.18 vs 2.94 frames/s); the closest fight is SmolLM2-135M (26% apart); VRAM decides part of this one: NVIDIA B300 runs 140 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 B300 | Difference |
|---|---|---|---|
| Qwen3-4B tok/s | 129.87 | 333.34 | -61% |
| Llama-3.1-8B tok/s | 86.62 | 287.23 | -70% |
| Qwen2.5-Coder-14B tok/s | 47.07 | 158.49 | -70% |
| Qwen3-32B tok/s | 22.04 | 83.68 | -74% |
| Llama 3.3 70B tok/s | 0 | 47.97 | n/a |
| Stable Diffusion XL images/min | 6.38 | 29.2 | -78% |
| Z-Image Turbo images/min | 3.45 | 39.675 | -91% |
| FLUX.1 dev images/min | 0 | 20.807 | n/a |
| FLUX.1 Kontext dev images/min | 0 | 10.329 | n/a |
| Qwen-Image-Edit images/min | 0 | 8.14 | n/a |
| Wan 2.2 5B (720p) frames/s | 0.18 | 2.94 | -94% |
| Llama 3.2 1B tok/s | 400.6 | 889.19 | -55% |
| Qwen3 0.6B tok/s | 451.38 | 718.58 | -37% |
| Qwen3 1.7B tok/s | 267.08 | 555.1 | -52% |
| DeepSeek-R1 Distill 1.5B tok/s | 265.75 | 562.14 | -53% |
| Gemma 3 4B tok/s | 124.85 | 301.07 | -59% |
| Llama 3.2 3B tok/s | 170.47 | 432.24 | -61% |
| Mistral 7B v0.3 tok/s | 92.82 | 298.95 | -69% |
| Phi-4 Mini 3.8B tok/s | 144.42 | 398.16 | -64% |
| Qwen2.5-Coder 7B tok/s | 90.05 | 286.5 | -69% |
| SmolLM3 3B tok/s | 172.15 | 411.25 | -58% |
| DeepSeek-R1 Distill Llama 8B tok/s | 86.83 | 283.77 | -69% |
| DeepSeek-R1 Distill 14B tok/s | 46.96 | 156.33 | -70% |
| DeepSeek-R1 Distill 7B tok/s | 89.79 | 284.77 | -68% |
| Gemma 3 12B tok/s | 52.07 | 161.27 | -68% |
| Gemma 4 12B tok/s | 52.67 | 155.96 | -66% |
| Mistral Small 24B tok/s | 30.94 | 119.3 | -74% |
| Phi-4 14B tok/s | 48.18 | 177.41 | -73% |
| Qwen3 14B tok/s | 48.12 | 165.44 | -71% |
| Qwen3 30B A3B tok/s | 140.12 | 284.66 | -51% |
| Qwen3 8B tok/s | 84.09 | 261.4 | -68% |
| Codestral 22B tok/s | 32.09 | 119.81 | -73% |
| DeepSeek-R1 Distill 32B tok/s | 21.85 | 82.9 | -74% |
| Devstral Small 24B tok/s | 30.98 | 121.18 | -74% |
| Dolphin 2.9.1 Yi 1.5 34B tok/s | 21.12 | 85.15 | -75% |
| Dolphin Mistral 24B Venice tok/s | 30.95 | 120.93 | -74% |
| Dolphin X1 8B tok/s | 86.61 | 287.02 | -70% |
| Dolphin 3.0 Llama 3.1 8B tok/s | 86.54 | 285.88 | -70% |
| Dolphin 3.0 R1 Mistral 24B tok/s | 30.9 | 120.95 | -74% |
| Gemma 3 27B tok/s | 24.8 | 93.44 | -73% |
| Qwen2.5-Coder 32B tok/s | 21.92 | 82.9 | -74% |
| Qwen3-Coder 30B A3B tok/s | 142.77 | 284.88 | -50% |
| QwQ 32B tok/s | 21.87 | 82.91 | -74% |
| StarCoder2 15B tok/s | 42.19 | 144.05 | -71% |
| Z-Image Turbo (1024px) images/min | 6.32 | 48.63 | -87% |
| BiRefNet images/min | 432.33 | 1064.73 | -59% |
| Depth Anything V2 Large images/min | 740.74 | 1069.95 | -31% |
| Depth Anything V2 Small images/min | 941.18 | 1491.6 | -37% |
| SAM ViT-Base images/min | 398.72 | 1719.59 | -77% |
| SAM ViT-Huge images/min | 84.22 | 399.37 | -79% |
| Swin2SR 4x Upscaler images/min | 22.31 | 58.29 | -62% |
| Dolphin X1 Trinity Nano 6B tok/s | 171.23 | 257.93 | -34% |
| gpt-oss-20b tok/s | 144.61 | 355.56 | -59% |
| Olmo-3.1-32B-Think tok/s | 22.13 | 87.85 | -75% |
| Dolphin-Mistral-24B-Venice-Edition tok/s | 31 | 121.87 | -75% |
| DeepSeek-Coder-V2-Lite tok/s | 160.13 | 340.53 | -53% |
| DeepSeek-R1-0528-Qwen3-8B tok/s | 83.22 | 270.35 | -69% |
| EVA-Qwen2.5-14B-v0.2 tok/s | 47 | 159.46 | -71% |
| gemma-2-2b-it-abliterated tok/s | 174.56 | 418.18 | -58% |
| gemma-2-2b tok/s | 175.03 | 417.84 | -58% |
| gemma-2-9b tok/s | 57.37 | 202.22 | -72% |
| gemma-3-1b tok/s | 284.78 | 533.32 | -47% |
| gemma-3-270m tok/s | 597.49 | 926.67 | -36% |
| GLM-4.7-Flash tok/s | 103.24 | 196.52 | -47% |
| SmolLM3-3B tok/s | 171.66 | 420.3 | -59% |
| KAT-Coder-V2.5-Dev tok/s | 113.6 | 239.66 | -53% |
| L3-8B-Stheno-v3.2 tok/s | 86.8 | 293 | -70% |
| LFM2.5-1.2B tok/s | 429.96 | 955.13 | -55% |
| Llama-2-7B tok/s | 96.69 | 315.9 | -69% |
| Llama-3.2-3B-Instruct-uncensored tok/s | 167.63 | 448.94 | -63% |
| Meta-Llama-3.1-8B tok/s | 86.69 | 293 | -70% |
| Phi-4-mini tok/s | 144.27 | 409.58 | -65% |
| Mistral-7B-Instruct-v0.1 tok/s | 92.45 | 306.44 | -70% |
| Mistral-7B-Instruct-v0.2 tok/s | 92.74 | 306.79 | -70% |
| Mistral-7B-Instruct-v0.3 tok/s | 92.45 | 306.46 | -70% |
| Ornith-1.0-35B tok/s | 104.9 | 232.36 | -55% |
| Ornith-1.0-9B tok/s | 73.78 | 240.67 | -69% |
| Phi-3.5-mini tok/s | 145.97 | 352.25 | -59% |
| Qwen-AgentWorld-35B-A3B tok/s | 104.36 | 233.04 | -55% |
| Qwen3-0.6B tok/s | 446.74 | 742.64 | -40% |
| Qwen3-1.7B tok/s | 262.49 | 575.97 | -54% |
| Qwen3-14B tok/s | 47.8 | 169.52 | -72% |
| Qwen3-8B tok/s | 83.04 | 270.35 | -69% |
| Qwen2.5-0.5B tok/s | 508.78 | 843.4 | -40% |
| Qwen2.5-1.5B tok/s | 265.7 | 584.96 | -55% |
| Qwen2.5-14B tok/s | 47.02 | 159.33 | -70% |
| Qwen2.5-32B tok/s | 21.98 | 83.41 | -74% |
| Qwen2.5-3B tok/s | 168.23 | 409.56 | -59% |
| Qwen2.5-7B tok/s | 89.68 | 293.63 | -69% |
| Qwen2.5-Coder-1.5B tok/s | 264.79 | 584.5 | -55% |
| Qwen2.5-Coder-3B tok/s | 168 | 410.06 | -59% |
| GLM-4.7-Flash-REAP-23B-A3B tok/s | 97.48 | 178.49 | -45% |
| Josiefied-Qwen3-8B-abliterated-v1 tok/s | 83.26 | 270.09 | -69% |
| LFM2.5-8B-A1B tok/s | 268.04 | 596.16 | -55% |
| Mistral-Nemo-Instruct-2407 tok/s | 57 | 196.92 | -71% |
| Hermes-4-14B tok/s | 47.8 | 169.51 | -72% |
| phi-2 tok/s | 172.45 | 356.65 | -52% |
| Qwen3-30B-A3B tok/s | 140.14 | 291.82 | -52% |
| Qwen2.5-Coder-14B-Instruct-abliterated tok/s | 46.91 | 159.5 | -71% |
| Qwen3-4B-Instruct-2507 tok/s | 129.9 | 339.72 | -62% |
| Qwen3-4B-Thinking-2507 tok/s | 129.99 | 339.74 | -62% |
| SmolLM2-135M tok/s | 680.37 | 855.87 | -21% |
| Cydonia-24B-v4.3 tok/s | 31.04 | 121.9 | -75% |
| Qwen2.5 1.5B LoRA train tok/s | 4214.4 | 22351.1 | -81% |
| Qwen2.5 7B LoRA train tok/s | 1239 | 14323.1 | -91% |
| SmolLM2 1.7B LoRA train tok/s | 3668.3 | 26114.3 | -86% |
| TinyLlama 1.1B LoRA train tok/s | 5499.6 | 26938.2 | -80% |
| FLUX.1 Schnell images/min | 0 | 59.54 | n/a |
| AI21-Jamba-Reasoning-3B tok/s | 166.29 | 380.88 | -56% |
| Codestral 22B (Q3_K_M) tok/s | 28.01 | 107.98 | -74% |
| DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored tok/s | 87.84 | 292.55 | -70% |
| DeepSeek-R1-Distill-Llama-70B tok/s | 0 | 48.11 | n/a |
| DeepSeek-R1 Distill 14B (Q3_K_M) tok/s | 41.72 | 144.74 | -71% |
| DeepSeek-R1-Distill-Qwen-32B-abliterated tok/s | 22.14 | 83.19 | -73% |
| dolphin-2.9-llama3-8b tok/s | 87.98 | 290.47 | -70% |
| Gemma 3 12B (Q3_K_M) tok/s | 46.29 | 149.61 | -69% |
| Hermes-3-Llama-3.2-3B tok/s | 169.23 | 446.78 | -62% |
| Hermes-4-70B tok/s | 0 | 48.1 | n/a |
| Qwen3-Coder-Next-abliterated tok/s | 0 | 199.12 | n/a |
| Laguna-XS-2.1 tok/s | 0 | 0 | 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 |
| Mistral Small 24B (Q3_K_M) tok/s | 26.8 | 108.37 | -75% |
| Nanbeige4.2-3B tok/s | 0 | 0 | n/a |
| NemoMix-Unleashed-12B tok/s | 57.52 | 197.06 | -71% |
| Nemotron-3-Nano-30B-A3B tok/s | 0 | 345.31 | n/a |
| Phi-4 14B (Q3_K_M) tok/s | 43.56 | 165.06 | -74% |
| Qwen3-4B-Instruct-2507 tok/s | 130.83 | 339.78 | -61% |
| Qwen3-Coder-Next tok/s | 0 | 196.34 | n/a |
| Qwen3-Next-80B-A3B-Thinking tok/s | 0 | 194.98 | n/a |
| Qwen1.5-0.5B tok/s | 542.19 | 894.84 | -39% |
| Qwen2-1.5B tok/s | 265.45 | 583.79 | -55% |
| Uncensored tok/s | 47.45 | 159.48 | -70% |
| Qwen2.5-72B tok/s | 0 | 48.28 | n/a |
| Qwen2.5-Coder-0.5B tok/s | 513.36 | 843.44 | -39% |
| Qwen2.5-Coder 32B (Q3_K_M) tok/s | 18.78 | 74.28 | -75% |
| Qwen2.5-Coder-7B-Instruct-abliterated tok/s | 91.41 | 292.21 | -69% |
| 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 |
| Qwen3 30B A3B (Q3_K_M) tok/s | 120.38 | 268.71 | -55% |
Whole-job comparison
How long each card takes to finish a complete pipeline, not just one model. NVIDIA A10G is faster on 0 of 6; NVIDIA B300 on 6.
| Workflow | NVIDIA A10G | NVIDIA B300 | Difference |
|---|---|---|---|
| 50-image depth pass | 7 s | 5 s | NVIDIA B300 1.41x faster |
| 500-image masking run | 6 min | 78 s | NVIDIA B300 4.64x faster |
| 24-frame storyboard | 9 min | 1.5 min | NVIDIA B300 5.98x faster |
| 200-product catalogue cutout | 9.6 min | 3.7 min | NVIDIA B300 2.57x faster |
| Full codebase review | 21.3 min | 6.3 min | NVIDIA B300 3.38x faster |
| 10 short social clips | 50.7 min | 4.7 min | NVIDIA B300 10.70x faster |
Specifications compared
| NVIDIA A10G | NVIDIA B300 | |
|---|---|---|
| VRAM | 24GB | 288GB |
| Architecture | Ampere | Blackwell Ultra |
| Memory bandwidth | 600 GB/s | 8 TB/s |
| Boost clock | 1,710 MHz | n/a |
| TDP | 150 W | 1400 W |
| Launch MSRP | $2,800 | $40,000 |
| Release | 2021-11-01 | 2025-11-01 |
FAQ
- Which is better for ai & machine learning: NVIDIA A10G or NVIDIA B300?
- NVIDIA B300 performs better for ai & machine learning, winning 139 of 141 benchmarks in our suite with an average 228.9% advantage.
- What are the main hardware differences between NVIDIA A10G and NVIDIA B300?
- NVIDIA A10G has 24GB VRAM and a 150W TDP, while NVIDIA B300 has 288GB VRAM and a 1400W TDP.
- Does VRAM matter more than speed between NVIDIA A10G and NVIDIA B300?
- For AI, yes, NVIDIA B300 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 B300?
- Wan 2.2 5B (720p): NVIDIA B300 leads by roughly 276% (0.18 vs 2.94 frames/s) in our testing.
NVIDIA A10G full review · NVIDIA B300 full review · All AI & Machine Learning rankings