NVIDIA B200 vs NVIDIA L4, AI & Machine Learning Comparison
NVIDIA B200 wins 143 of 146 benchmarks, averaging 377.1% faster.
Both cards were measured first-party on our bench, same suite, same test rig.
What the numbers say
The gap is widest in Z-Image Turbo (1024px), where NVIDIA B200 leads by 1258% (62.47 vs 4.6 images/min); the closest fight is Depth Anything V2 Small (24% apart); VRAM decides part of this one: NVIDIA B200 runs 145 of our 12 AI workloads while the other card runs 138, models that don't fit score zero.
Benchmark results head-to-head
| Benchmark | NVIDIA B200 | NVIDIA L4 | Difference |
|---|---|---|---|
| Qwen3 4B tok/s | 317.93 | 84 | +278% |
| Llama 3.1 8B tok/s | 274.41 | 50.45 | +444% |
| Qwen2.5-Coder 14B tok/s | 150.96 | 27.46 | +450% |
| Qwen3 32B tok/s | 78.56 | 12.42 | +533% |
| Llama 3.3 70B tok/s | 44.54 | 0 | n/a |
| Stable Diffusion XL images/min | 46.12 | 5.18 | +790% |
| Z-Image Turbo images/min | 32.325 | 2.475 | +1206% |
| FLUX.1 dev images/min | 13.136 | 0 | n/a |
| FLUX.1 Kontext dev images/min | 5.721 | 0 | n/a |
| Qwen-Image-Edit images/min | 4.86 | 0 | n/a |
| Wan 2.2 5B (720p) frames/s | 1.88 | 0.15 | +1153% |
| DeepSeek-R1 Distill Llama 8B tok/s | 273.9 | 50.39 | +444% |
| DeepSeek-R1 Distill 1.5B tok/s | 456.7 | 191.38 | +139% |
| DeepSeek-R1 Distill 14B tok/s | 150.69 | 27.39 | +450% |
| DeepSeek-R1 Distill 7B tok/s | 277.02 | 53.27 | +420% |
| Gemma 3 12B tok/s | 151.41 | 31.01 | +388% |
| Gemma 3 4B tok/s | 291.82 | 82.03 | +256% |
| Gemma 4 12B tok/s | 146.36 | 31.49 | +365% |
| Llama 3.2 1B tok/s | 881.78 | 259.48 | +240% |
| Llama 3.2 3B tok/s | 422.09 | 107.1 | +294% |
| Mistral 7B v0.3 tok/s | 287.42 | 54.02 | +432% |
| Mistral Small 24B tok/s | 114.04 | 17.33 | +558% |
| Phi-4 14B tok/s | 163.85 | 27.24 | +502% |
| Phi-4 Mini 3.8B tok/s | 375.74 | 88.48 | +325% |
| Qwen2.5-Coder 7B tok/s | 277.98 | 53.26 | +422% |
| Qwen3 0.6B tok/s | 599.63 | 357.79 | +68% |
| Qwen3 1.7B tok/s | 551.76 | 177.5 | +211% |
| Qwen3 14B tok/s | 158.34 | 27.61 | +473% |
| Qwen3 30B A3B tok/s | 270.84 | 96.15 | +182% |
| Qwen3 8B tok/s | 253.45 | 48.95 | +418% |
| SmolLM3 3B tok/s | 400.73 | 112.07 | +258% |
| Codestral 22B tok/s | 112.16 | 18.14 | +518% |
| DeepSeek-R1 Distill 32B tok/s | 78.55 | 12.28 | +540% |
| Devstral Small 24B tok/s | 113.91 | 17.34 | +557% |
| Dolphin 2.9.1 Yi 1.5 34B tok/s | 79.35 | 11.92 | +566% |
| Dolphin Mistral 24B Venice tok/s | 113.93 | 17.34 | +557% |
| Dolphin X1 8B tok/s | 273.85 | 50.12 | +446% |
| Dolphin 3.0 Llama 3.1 8B tok/s | 274.05 | 50.24 | +445% |
| Dolphin 3.0 R1 Mistral 24B tok/s | 113.95 | 17.32 | +558% |
| Gemma 3 27B tok/s | 86.08 | 14.18 | +507% |
| Qwen2.5-Coder 32B tok/s | 78.53 | 12.29 | +539% |
| Qwen3-Coder 30B A3B tok/s | 277.68 | 99.14 | +180% |
| QwQ 32B tok/s | 78.47 | 12.28 | +539% |
| StarCoder2 15B tok/s | 138.64 | 24.4 | +468% |
| FLUX.1 Schnell images/min | 81.41 | 0 | n/a |
| Z-Image Turbo (1024px) images/min | 62.47 | 4.6 | +1258% |
| Krea 2 Turbo images/min | 9.5 | 0 | n/a |
| BiRefNet images/min | 1082.03 | 305.81 | +254% |
| Depth Anything V2 Large images/min | 1039.47 | 770.37 | +35% |
| Depth Anything V2 Small images/min | 1137.49 | 916.66 | +24% |
| SAM ViT-Base images/min | 1662.34 | 315.89 | +426% |
| SAM ViT-Huge images/min | 388.61 | 70.46 | +452% |
| Swin2SR 4x Upscaler images/min | 11.97 | 18.23 | -34% |
| Qwen2.5 1.5B LoRA train tok/s | 15322 | 3720.9 | +312% |
| Qwen2.5 7B LoRA train tok/s | 13722 | 1063.9 | +1190% |
| SmolLM2 1.7B LoRA train tok/s | 18064.7 | 3240.2 | +458% |
| TinyLlama 1.1B LoRA train tok/s | 17993.5 | 4756 | +278% |
| TinyLlama 1.1B served serve tok/s | 11562.2 | 2584.3 | +347% |
| Qwen2.5 1.5B served serve tok/s | 9186.4 | 1827 | +403% |
| Qwen2.5 7B served serve tok/s | 5799.1 | 469.9 | +1134% |
| SmolLM2 1.7B served serve tok/s | 9672.9 | 1524.9 | +534% |
| AI21-Jamba-Reasoning-3B tok/s | 360.54 | 107.59 | +235% |
| Olmo-3.1-32B-Think tok/s | 79.75 | 12.27 | +550% |
| Codestral 22B (Q3_K_M) tok/s | 89.9 | 17.96 | +401% |
| Dolphin-Mistral-24B-Venice-Edition tok/s | 113.8 | 17.2 | +562% |
| DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored tok/s | 274.3 | 48.82 | +462% |
| DeepSeek-Coder-V2-Lite tok/s | 325.85 | 110.11 | +196% |
| DeepSeek-R1-0528-Qwen3-8B tok/s | 252.98 | 49.07 | +416% |
| DeepSeek-R1-Distill-Llama-70B tok/s | 44.49 | 0 | n/a |
| DeepSeek-R1 Distill 14B (Q3_K_M) tok/s | 120.47 | 27.87 | +332% |
| DeepSeek-R1-Distill-Qwen-32B-abliterated tok/s | 78.48 | 12.26 | +540% |
| dolphin-2.9-llama3-8b tok/s | 274.48 | 50.04 | +449% |
| Dolphin X1 Trinity Nano 6B tok/s | 209.31 | 166.6 | +26% |
| EVA-Qwen2.5-14B-v0.2 tok/s | 150.67 | 27.3 | +452% |
| gemma-2-2b-it-abliterated tok/s | 392.32 | 113 | +247% |
| gemma-2-2b tok/s | 391.94 | 114.17 | +243% |
| gemma-2-9b tok/s | 187.45 | 33.8 | +455% |
| Gemma 3 12B (Q3_K_M) tok/s | 125.31 | 32.29 | +288% |
| gemma-3-1b tok/s | 519.19 | 216.27 | +140% |
| gemma-3-270m tok/s | 889.52 | 493.31 | +80% |
| GLM-4.7-Flash-REAP-23B-A3B tok/s | 167.76 | 70.87 | +137% |
| GLM-4.7-Flash tok/s | 183.82 | 75.03 | +145% |
| Josiefied-Qwen3-8B-abliterated-v1 tok/s | 253.28 | 48.72 | +420% |
| gpt-oss-20b tok/s | 351.42 | 91.78 | +283% |
| Hermes-3-Llama-3.2-3B tok/s | 420.6 | 106.14 | +296% |
| Hermes-4-70B tok/s | 44.49 | 0 | n/a |
| SmolLM3-3B tok/s | 399.05 | 110.77 | +260% |
| Qwen3-Coder-Next-abliterated tok/s | 186.57 | 0 | n/a |
| KAT-Coder-V2.5-Dev tok/s | 230.68 | 79.01 | +192% |
| L3-8B-Stheno-v3.2 tok/s | 274.48 | 50.07 | +448% |
| Laguna-XS-2.1 tok/s | 0 | 0 | n/a |
| LFM2.5-1.2B tok/s | 916.79 | 278.53 | +229% |
| LFM2.5-8B-A1B tok/s | 575.15 | 170.2 | +238% |
| Llama-2-7B tok/s | 292 | 56.06 | +421% |
| Llama-3.2-3B-Instruct-uncensored tok/s | 419.24 | 101.16 | +314% |
| Llama-3.3-70B-Instruct-abliterated tok/s | 44.48 | 0 | n/a |
| Meta-Llama-3.1-70B tok/s | 44.48 | 0 | n/a |
| Meta-Llama-3.1-8B tok/s | 274.17 | 50.36 | +444% |
| Phi-4-mini tok/s | 375.06 | 87.21 | +330% |
| Mistral-7B-Instruct-v0.1 tok/s | 286.89 | 53.6 | +435% |
| Mistral-7B-Instruct-v0.2 tok/s | 286.37 | 53.31 | +437% |
| Mistral-7B-Instruct-v0.3 tok/s | 286.48 | 53.66 | +434% |
| Mistral-Nemo-Instruct-2407 tok/s | 183.92 | 33.01 | +457% |
| Mistral Small 24B (Q3_K_M) tok/s | 87.72 | 16.62 | +428% |
| Nanbeige4.2-3B tok/s | 0 | 0 | n/a |
| NemoMix-Unleashed-12B tok/s | 183.93 | 33.02 | +457% |
| Nemotron-3-Nano-30B-A3B tok/s | 345.93 | 0 | n/a |
| Hermes-4-14B tok/s | 158.22 | 27.48 | +476% |
| Ornith-1.0-35B tok/s | 221.13 | 70.31 | +215% |
| Ornith-1.0-9B tok/s | 226.2 | 43.35 | +422% |
| phi-2 tok/s | 348.72 | 114.99 | +203% |
| Phi-3.5-mini tok/s | 325 | 90.56 | +259% |
| Phi-4 14B (Q3_K_M) tok/s | 135.21 | 27.86 | +385% |
| Qwen-AgentWorld-35B-A3B tok/s | 221.78 | 70.23 | +216% |
| Qwen3-0.6B tok/s | 596.73 | 356.93 | +67% |
| Qwen3-1.7B tok/s | 551.93 | 176.08 | +213% |
| Qwen3-14B tok/s | 158.19 | 27.48 | +476% |
| Qwen3-30B-A3B tok/s | 271.69 | 95.02 | +186% |
| Qwen3-4B-Instruct-2507 tok/s | 316.66 | 83.03 | +281% |
| Qwen3-8B tok/s | 253.14 | 48.34 | +424% |
| Qwen3-Coder-Next tok/s | 186.39 | 0 | n/a |
| Qwen3-Next-80B-A3B-Thinking tok/s | 186.03 | 0 | n/a |
| Qwen1.5-0.5B tok/s | 699.73 | 431.41 | +62% |
| Qwen2-1.5B tok/s | 454.55 | 189.18 | +140% |
| Qwen2.5-0.5B tok/s | 818.27 | 399.4 | +105% |
| Qwen2.5-1.5B tok/s | 454.69 | 191.39 | +138% |
| Uncensored tok/s | 150.51 | 27.29 | +452% |
| Qwen2.5-14B tok/s | 150.57 | 27.37 | +450% |
| Qwen2.5-32B tok/s | 78.55 | 12.24 | +542% |
| Qwen2.5-3B tok/s | 388.14 | 110.84 | +250% |
| Qwen2.5-72B tok/s | 45.34 | 0 | n/a |
| Qwen2.5-7B tok/s | 277.17 | 53.32 | +420% |
| Qwen2.5-Coder-0.5B tok/s | 828.48 | 397.1 | +109% |
| Qwen2.5-Coder-1.5B tok/s | 455.35 | 184.6 | +147% |
| Qwen2.5-Coder-14B-Instruct-abliterated tok/s | 150.67 | 27.29 | +452% |
| Qwen2.5-Coder 32B (Q3_K_M) tok/s | 60.24 | 12.05 | +400% |
| Qwen2.5-Coder-3B tok/s | 388.17 | 109.42 | +255% |
| Qwen2.5-Coder-7B-Instruct-abliterated tok/s | 276.57 | 53.02 | +422% |
| Qwen3 30B A3B (Q3_K_M) tok/s | 229.58 | 94.03 | +144% |
| Qwen3-4B-Instruct-2507 tok/s | 316.7 | 83.13 | +281% |
| Qwen3-4B-Thinking-2507 tok/s | 317.03 | 83.12 | +281% |
| Qwen3-Coder-Next tok/s | 189.76 | 0 | n/a |
| Qwen3-Next-80B-A3B-Thinking tok/s | 188.78 | 0 | n/a |
| Qwen3-Next-80B-A3B tok/s | 179.71 | 0 | n/a |
| SmolLM2-135M tok/s | 815.3 | 652.15 | +25% |
| Cydonia-24B-v4.3 tok/s | 113.81 | 17.3 | +558% |
Whole-job comparison
How long each card takes to finish a complete pipeline, not just one model. NVIDIA B200 is faster on 5 of 6; NVIDIA L4 on 1.
| Workflow | NVIDIA B200 | NVIDIA L4 | Difference | Cost per run |
|---|---|---|---|---|
| 50-image depth pass | 6 s | 17 s | NVIDIA B200 3.00x faster | $0.009 vs $0.002 |
| 500-image masking run | 85 s | 7.5 min | NVIDIA B200 5.25x faster | $0.141 vs $0.055 |
| 24-frame storyboard | 89 s | 12.2 min | NVIDIA B200 8.19x faster | $0.149 vs $0.089 |
| 10 short social clips | 5.6 min | 60.3 min | NVIDIA B200 10.77x faster | $0.558 vs $0.442 |
| Full codebase review | 6.6 min | 36.4 min | NVIDIA B200 5.50x faster | $0.660 vs $0.267 |
| 200-product catalogue cutout | 17.2 min | 12.1 min | NVIDIA L4 1.42x faster | $1.712 vs $0.089 |
Renting by the hour, NVIDIA L4 finishes 6 of 6 cheaper. The quicker card is not automatically the cheaper way to get the work done.
Cost to rent
| Card | Per hour |
|---|---|
| NVIDIA B200 | $5.980 |
| NVIDIA L4 | $0.440 |
NVIDIA L4 is 13.59x cheaper per hour. Cheapest on-demand rate we see across RunPod and Vast.
Specifications compared
| NVIDIA B200 | NVIDIA L4 | |
|---|---|---|
| VRAM | 192GB | 24GB |
| Architecture | Blackwell | Ada Lovelace |
| Memory bandwidth | 8 TB/s | 300 GB/s |
| Boost clock | n/a | 2,040 MHz |
| TDP | 1000 W | 72 W |
| Launch MSRP | $40,000 | $2,500 |
| Release | 2025-02-01 | 2023-03-21 |
FAQ
- Which is better for ai & machine learning: NVIDIA B200 or NVIDIA L4?
- NVIDIA B200 performs better for ai & machine learning, winning 143 of 146 benchmarks in our suite with an average 377.1% advantage.
- What are the main hardware differences between NVIDIA B200 and NVIDIA L4?
- NVIDIA B200 has 192GB VRAM and a 1000W TDP, while NVIDIA L4 has 24GB VRAM and a 72W TDP.
- Does VRAM matter more than speed between NVIDIA B200 and NVIDIA L4?
- For AI, yes, NVIDIA B200 fits 22 more of our 12 workloads than NVIDIA L4. 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 B200 and NVIDIA L4?
- Z-Image Turbo (1024px): NVIDIA B200 leads by roughly 1258% (62.47 vs 4.6 images/min) in our testing.
NVIDIA B200 full review · NVIDIA L4 full review · All AI & Machine Learning rankings