NVIDIA A100 40GB SXM4 vs NVIDIA A100 80GB SXM4, AI & Machine Learning Comparison
NVIDIA A100 80GB SXM4 wins 127 of 149 benchmarks, averaging 5.5% 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 TinyLlama 1.1B served, where NVIDIA A100 80GB SXM4 leads by 77% (3221.3 vs 5701.5 serve tok/s); the closest fight is Wan 2.2 5B (720p) (0% apart); VRAM decides part of this one: NVIDIA A100 80GB SXM4 runs 155 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 A100 80GB SXM4 | Difference |
|---|---|---|---|
| Qwen3-4B tok/s | 193.14 | 197 | -2% |
| Llama-3.1-8B tok/s | 157.26 | 162.57 | -3% |
| Qwen2.5-Coder-14B tok/s | 86.2 | 89.45 | -4% |
| Qwen3-32B tok/s | 43.8 | 45.53 | -4% |
| Llama-3.3-70B tok/s | 0 | 24.4 | n/a |
| Stable Diffusion XL images/min | 18.576 | 16.36 | +14% |
| Z-Image Turbo images/min | 9.975 | 9.975 | 0% |
| FLUX.1 dev images/min | 4.264 | 4.264 | 0% |
| FLUX.1 Kontext dev images/min | 1.993 | 1.993 | 0% |
| Qwen-Image-Edit images/min | 0 | 1.64 | n/a |
| LTX-Video (distilled) frames/s | 8.95 | 8.95 | 0% |
| Wan 2.2 5B (720p) frames/s | 0.66 | 0.66 | 0% |
| DeepSeek-R1 Distill Llama 8B tok/s | 157.16 | 159.37 | -1% |
| DeepSeek-R1 Distill 1.5B tok/s | 320.78 | 327.66 | -2% |
| DeepSeek-R1 Distill 14B tok/s | 86.39 | 87 | -1% |
| DeepSeek-R1 Distill 7B tok/s | 159.09 | 162.96 | -2% |
| Gemma 3 12B tok/s | 88.97 | 91.45 | -3% |
| Gemma 3 4B tok/s | 174.14 | 176.94 | -2% |
| Gemma 4 12B tok/s | 87.14 | 92.43 | -6% |
| Llama 3.2 1B tok/s | 532.08 | 525.32 | +1% |
| Llama 3.2 3B tok/s | 261.24 | 264.25 | -1% |
| Mistral 7B v0.3 tok/s | 165.9 | 171.92 | -4% |
| Mistral Small 24B tok/s | 62.42 | 61.38 | +2% |
| Phi-4 14B tok/s | 98.65 | 95.22 | +4% |
| Phi-4 Mini 3.8B tok/s | 236.71 | 239.17 | -1% |
| Qwen2.5-Coder 7B tok/s | 159.9 | 164.6 | -3% |
| Qwen3 0.6B tok/s | 417.88 | 428.39 | -2% |
| Qwen3 1.7B tok/s | 340.76 | 343.58 | -1% |
| Qwen3 14B tok/s | 90.01 | 90.64 | -1% |
| Qwen3 30B A3B tok/s | 169.1 | 177.32 | -5% |
| Qwen3 8B tok/s | 146.21 | 149.96 | -3% |
| SmolLM3 3B tok/s | 246.25 | 249.76 | -1% |
| Codestral 22B tok/s | 63.74 | 63.26 | +1% |
| DeepSeek-R1 Distill 32B tok/s | 43.13 | 43.11 | 0% |
| Devstral Small 24B tok/s | 62.4 | 61.63 | +1% |
| Dolphin 2.9.1 Yi 1.5 34B tok/s | 42.97 | 43.47 | -1% |
| Dolphin Mistral 24B Venice tok/s | 62.46 | 62.72 | -0% |
| Dolphin X1 8B tok/s | 156.3 | 158.62 | -1% |
| Dolphin 3.0 Llama 3.1 8B tok/s | 156.55 | 158.63 | -1% |
| Dolphin 3.0 R1 Mistral 24B tok/s | 62.4 | 61.17 | +2% |
| Gemma 3 27B tok/s | 47.47 | 48.43 | -2% |
| Qwen2.5-Coder 32B tok/s | 43.14 | 43.36 | -1% |
| Qwen3-Coder 30B A3B tok/s | 173.47 | 182.26 | -5% |
| QwQ 32B tok/s | 43.02 | 43.27 | -1% |
| StarCoder2 15B tok/s | 80.25 | 79.22 | +1% |
| FLUX.1 Schnell images/min | 28.44 | 28.39 | 0% |
| Z-Image Turbo (1024px) images/min | 18.7 | 18.65 | 0% |
| BiRefNet images/min | 611.56 | 916.67 | -33% |
| Depth Anything V2 Large images/min | 536.74 | 876.29 | -39% |
| Depth Anything V2 Small images/min | 613.42 | 925.15 | -34% |
| SAM ViT-Base images/min | 696.77 | 744.79 | -6% |
| SAM ViT-Huge images/min | 146.81 | 145.7 | +1% |
| Swin2SR 4x Upscaler images/min | 17.25 | 28.72 | -40% |
| Kokoro TTS 82M x realtime | 142.03 | 110.17 | +29% |
| MusicGen Small x realtime | 1.08 | 0.74 | +46% |
| Whisper large-v3 x realtime | 102.47 | 73.83 | +39% |
| Dolphin X1 Trinity Nano 6B tok/s | 153.27 | 158.41 | -3% |
| gpt-oss-20b tok/s | 205.1 | 212.63 | -4% |
| Olmo-3.1-32B-Think tok/s | 43.67 | 46.49 | -6% |
| Dolphin-Mistral-24B-Venice-Edition tok/s | 62.56 | 65.77 | -5% |
| DeepSeek-Coder-V2-Lite tok/s | 205.42 | 213.91 | -4% |
| DeepSeek-R1-0528-Qwen3-8B tok/s | 145.84 | 152.76 | -5% |
| EVA-Qwen2.5-14B-v0.2 tok/s | 86.48 | 90.35 | -4% |
| Qwen2.5 1.5B LoRA train tok/s | 6349.7 | 8252.8 | -23% |
| Qwen2.5 7B LoRA train tok/s | 3670.8 | 3900.6 | -6% |
| SmolLM2 1.7B LoRA train tok/s | 6938.8 | 9750.8 | -29% |
| TinyLlama 1.1B LoRA train tok/s | 7471 | 9553.3 | -22% |
| gemma-2-2b-it-abliterated tok/s | 232.27 | 244.41 | -5% |
| gemma-2-2b tok/s | 234.68 | 244.52 | -4% |
| gemma-2-9b tok/s | 101.97 | 108.68 | -6% |
| gemma-3-1b tok/s | 310.69 | 324.08 | -4% |
| gemma-3-270m tok/s | 574.24 | 617.58 | -7% |
| GLM-4.7-Flash tok/s | 119.1 | 124.08 | -4% |
| SmolLM3-3B tok/s | 247.66 | 254.6 | -3% |
| KAT-Coder-V2.5-Dev tok/s | 137.31 | 146.38 | -6% |
| LFM2.5-1.2B tok/s | 572.69 | 576.95 | -1% |
| Llama-2-7B tok/s | 172.53 | 180.09 | -4% |
| Llama-3.2-3B-Instruct-uncensored tok/s | 260.83 | 269.13 | -3% |
| Meta-Llama-3.1-8B tok/s | 156.68 | 164.24 | -5% |
| Phi-4-mini tok/s | 237.38 | 243.38 | -2% |
| Mistral-7B-Instruct-v0.1 tok/s | 166.39 | 173.87 | -4% |
| Mistral-7B-Instruct-v0.2 tok/s | 166.8 | 173.8 | -4% |
| Mistral-7B-Instruct-v0.3 tok/s | 167.06 | 173.27 | -4% |
| Ornith-1.0-35B tok/s | 131.77 | 138.8 | -5% |
| Ornith-1.0-9B tok/s | 126.79 | 134.67 | -6% |
| Phi-3.5-mini tok/s | 222.53 | 229.46 | -3% |
| Qwen-AgentWorld-35B-A3B tok/s | 131.16 | 139.69 | -6% |
| Qwen3-0.6B tok/s | 408.39 | 432.66 | -6% |
| Qwen3-1.7B tok/s | 341.85 | 350.4 | -2% |
| Qwen3-14B tok/s | 90.07 | 94.35 | -5% |
| Qwen3-8B tok/s | 145.96 | 152.8 | -4% |
| Qwen2.5-0.5B tok/s | 517.09 | 571.49 | -10% |
| Qwen2.5-1.5B tok/s | 315.59 | 335.29 | -6% |
| Qwen2.5-14B tok/s | 86.07 | 90.46 | -5% |
| Qwen2.5-32B tok/s | 43.23 | 45.6 | -5% |
| Qwen2.5-3B tok/s | 240.48 | 248.47 | -3% |
| Qwen2.5-7B tok/s | 159.81 | 166.92 | -4% |
| Qwen2.5-Coder-1.5B tok/s | 322.96 | 333.87 | -3% |
| Qwen2.5-Coder-3B tok/s | 241.51 | 248.7 | -3% |
| AI21-Jamba-Reasoning-3B tok/s | 231.15 | 239.23 | -3% |
| Codestral 22B (Q3_K_M) tok/s | 48.37 | 49.52 | -2% |
| DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored tok/s | 156.62 | 164.46 | -5% |
| DeepSeek-R1 Distill 14B (Q3_K_M) tok/s | 65.33 | 67.13 | -3% |
| DeepSeek-R1-Distill-Qwen-32B-abliterated tok/s | 43.07 | 43.48 | -1% |
| dolphin-2.9-llama3-8b tok/s | 153.34 | 164.09 | -7% |
| Gemma 3 12B (Q3_K_M) tok/s | 69.7 | 71.88 | -3% |
| GLM-4.7-Flash-REAP-23B-A3B tok/s | 109.1 | 112.75 | -3% |
| Josiefied-Qwen3-8B-abliterated-v1 tok/s | 146.8 | 152.29 | -4% |
| Hermes-3-Llama-3.2-3B tok/s | 261 | 269.13 | -3% |
| L3-8B-Stheno-v3.2 tok/s | 156.55 | 164.51 | -5% |
| LFM2.5-8B-A1B tok/s | 351.47 | 369.01 | -5% |
| Mistral-Nemo-Instruct-2407 tok/s | 106.33 | 111.07 | -4% |
| Mistral Small 24B (Q3_K_M) tok/s | 45.34 | 46.63 | -3% |
| NemoMix-Unleashed-12B tok/s | 105.05 | 110.99 | -5% |
| Nemotron-3-Nano-30B-A3B tok/s | 192.44 | 204.06 | -6% |
| Hermes-4-14B tok/s | 89.97 | 94.48 | -5% |
| phi-2 tok/s | 228.1 | 235.55 | -3% |
| Phi-4 14B (Q3_K_M) tok/s | 80.71 | 82.68 | -2% |
| Qwen3-30B-A3B tok/s | 167.1 | 178.14 | -6% |
| Qwen3-4B-Instruct-2507 tok/s | 192.89 | 199.39 | -3% |
| Qwen1.5-0.5B tok/s | 497.73 | 540.32 | -8% |
| Qwen2-1.5B tok/s | 322.09 | 333.79 | -4% |
| Uncensored tok/s | 85.82 | 89.43 | -4% |
| Qwen2.5-Coder-0.5B tok/s | 542.36 | 562.59 | -4% |
| Qwen2.5-Coder-14B-Instruct-abliterated tok/s | 86.34 | 90.28 | -4% |
| Qwen2.5-Coder 32B (Q3_K_M) tok/s | 31.04 | 31.99 | -3% |
| Qwen2.5-Coder-7B-Instruct-abliterated tok/s | 159.76 | 166.84 | -4% |
| Qwen3 30B A3B (Q3_K_M) tok/s | 142.78 | 146.72 | -3% |
| Qwen3-4B-Instruct-2507 tok/s | 193.39 | 199.65 | -3% |
| Qwen3-4B-Thinking-2507 tok/s | 189.89 | 199.71 | -5% |
| SmolLM2-135M tok/s | 544.06 | 554.44 | -2% |
| Cydonia-24B-v4.3 tok/s | 62.41 | 65.82 | -5% |
| Qwen2.5 1.5B served serve tok/s | 4111.1 | 4564.7 | -10% |
| Qwen2.5 7B served serve tok/s | 1769.6 | 2187.2 | -19% |
| SmolLM2 1.7B served serve tok/s | 3865.2 | 4351.4 | -11% |
| TinyLlama 1.1B served serve tok/s | 3221.3 | 5701.5 | -44% |
| Qwen3-Coder-Next-abliterated tok/s | 0 | 119.56 | 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 | 118.62 | n/a |
| Qwen3-Next-80B-A3B-Thinking tok/s | 0 | 118.93 | n/a |
| Qwen2.5-72B tok/s | 0 | 23.95 | n/a |
| Qwen3-Coder-Next tok/s | 0 | 120.71 | n/a |
| Qwen3-Next-80B-A3B-Thinking tok/s | 0 | 120.98 | n/a |
| Qwen3-Next-80B-A3B tok/s | 0 | 117.44 | n/a |
| DeepSeek-R1-Distill-Llama-70B tok/s | 0 | 22.99 | n/a |
| Hermes-4-70B tok/s | 0 | 24.47 | n/a |
| Llama-3.3-70B-Instruct-abliterated tok/s | 0 | 22.92 | n/a |
| Meta-Llama-3.1-70B tok/s | 0 | 22.92 | 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 10 of 15; NVIDIA A100 80GB SXM4 on 5.
| Workflow | NVIDIA A100 40GB SXM4 | NVIDIA A100 80GB SXM4 | Difference | Cost per run |
|---|---|---|---|---|
| 50-image depth pass | 10 s | 5 s | NVIDIA A100 80GB SXM4 1.94x faster | $0.003 vs $0.002 |
| 30-minute podcast pass | 59 s | 67 s | NVIDIA A100 40GB SXM4 1.14x faster | $0.016 vs $0.026 |
| 24-frame storyboard | 3 min | 3.7 min | NVIDIA A100 40GB SXM4 1.21x faster | $0.051 vs $0.085 |
| 500-image masking run | 3.5 min | 3.5 min | NVIDIA A100 80GB SXM4 1.00x faster | $0.058 vs $0.081 |
| 60-second AI short film | 4 min | 4.7 min | NVIDIA A100 40GB SXM4 1.18x faster | $0.066 vs $0.108 |
| 60-second AI short film, narrated | 4.2 min | 4.9 min | NVIDIA A100 40GB SXM4 1.18x faster | $0.070 vs $0.114 |
| 6-panel comic page | 4.8 min | 6 min | NVIDIA A100 40GB SXM4 1.25x faster | $0.080 vs $0.139 |
| Character sheet, 12 poses | 6.3 min | 7.6 min | NVIDIA A100 40GB SXM4 1.21x faster | $0.104 vs $0.175 |
| Full codebase review | 11.7 min | 11.2 min | NVIDIA A100 80GB SXM4 1.04x faster | $0.195 vs $0.259 |
| 200-product catalogue cutout | 12.2 min | 7.3 min | NVIDIA A100 80GB SXM4 1.67x faster | $0.203 vs $0.169 |
| 10 short social clips | 13.7 min | 15.1 min | NVIDIA A100 40GB SXM4 1.11x faster | $0.228 vs $0.350 |
| 40-product photo shoot | 22.2 min | 23.5 min | NVIDIA A100 40GB SXM4 1.06x faster | $0.370 vs $0.543 |
| 40-product shoot, start to finish | 24.8 min | 25 min | NVIDIA A100 40GB SXM4 1.01x faster | $0.414 vs $0.579 |
| 100-photo restoration batch | 50.2 min | 50.8 min | NVIDIA A100 40GB SXM4 1.01x faster | $0.836 vs $1.177 |
| 100-photo restore and enlarge | 56 min | 54.3 min | NVIDIA A100 80GB SXM4 1.03x faster | $0.934 vs $1.258 |
Renting by the hour, NVIDIA A100 40GB SXM4 finishes 13 of 15 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 A100 80GB SXM4 | $1.390 |
NVIDIA A100 40GB SXM4 is 1.39x cheaper per hour. Cheapest on-demand rate we see across RunPod and Vast.
Specifications compared
| NVIDIA A100 40GB SXM4 | NVIDIA A100 80GB SXM4 | |
|---|---|---|
| VRAM | 40GB | 80GB |
| Architecture | Ampere | Ampere |
| Memory bandwidth | 1555 GB/s | 2039 GB/s |
| Boost clock | 1,410 MHz | 1,410 MHz |
| TDP | 400 W | 400 W |
| Launch MSRP | $12,000 | $17,000 |
| Release | 2020-05-14 | 2020-11-16 |
FAQ
- Which is better for ai & machine learning: NVIDIA A100 40GB SXM4 or NVIDIA A100 80GB SXM4?
- NVIDIA A100 80GB SXM4 performs better for ai & machine learning, winning 127 of 149 benchmarks in our suite with an average 5.5% advantage.
- What are the main hardware differences between NVIDIA A100 40GB SXM4 and NVIDIA A100 80GB SXM4?
- NVIDIA A100 40GB SXM4 has 40GB VRAM and a 400W TDP, while NVIDIA A100 80GB SXM4 has 80GB VRAM and a 400W TDP.
- Does VRAM matter more than speed between NVIDIA A100 40GB SXM4 and NVIDIA A100 80GB SXM4?
- For AI, yes, NVIDIA A100 80GB SXM4 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 A100 80GB SXM4?
- TinyLlama 1.1B served: NVIDIA A100 80GB SXM4 leads by roughly 77% (3221.3 vs 5701.5 serve tok/s) in our testing.
NVIDIA A100 40GB SXM4 full review · NVIDIA A100 80GB SXM4 full review · All AI & Machine Learning rankings