The NVIDIA L40S is an enterprise-class GPU built on the Ada Lovelace architecture, featuring 48 GB of memory with 864 GB/s of memory bandwidth and a 350 W TDP. It delivers 91.6 TFLOPS of FP32 performance and 362 TFLOPS of FP16 tensor performance. Currently available from 10 providers starting at $0.47/GPU/hour, with a market median of $1.27/GPU/hour across 43 configurations.
Hardware specifications
Same across all providers
Median price across all providers
11 offerings from 10 providers
Sorted by price ascending. Compare configurations side-by-side.
| Provider | Count | vCPU | RAM | Region | Per GPU hour | Total/hr | Action |
|---|---|---|---|---|---|---|---|
| ×1 | 16 | 94 GB | $0.990 | $0.99 | Launch | ||
| ×4 | 64 | 252 GB | From$0.470 +50.9% 30d | From$1.88 | Launch | ||
Novita AIReferral link | ×1 | 22 | 125 GB | $0.550 | $0.55 | ||
| ×2 | 44 | 256 GB | From$0.925 +0.3% 30d | From$1.85 | Launch | ||
| ×1 | 12 | 72 GB | $0.970 0.0% 30d | From$0.97 | Launch | ||
Up to$500credit | ×1 | 16 | 94 GB | $0.990 | $0.99 | Launch | |
Thunder ComputeReferral link | ×1 | 4 | 32 GB | -- | From$0.990 | From$0.99 | Launch |
Up to$1credit | ×8 | 64 | 1.5 TB | From$1.256 +0.2% 30d | From$10.05 | Launch | |
| ×1 | 20 | 60 GB | -- | $1.370 | From$1.37 | Launch | |
| ×1 | 16 | 180 GB | -- | From$1.560 | From$1.56 | Launch | |
| ×1 | 8 | 96 GB | $1.712 +1.5% 30d | From$1.71 | Launch | ||
Sesterce CloudReferral link | ×1 | 8 | 96 GB | From$1.870 0.0% 30d | From$1.87 | Launch |
LLMs that fit on the NVIDIA L40S
Estimated VRAM for popular open-weight LLMs at 16-bit (FP16/BF16), 8-bit (FP8/INT8) and 4-bit (INT4/MXFP4) precision, against 48 GB per GPU. Showing the 12 largest that fit.
| Model | Parameters | 16-bit | 8-bit | 4-bit |
|---|---|---|---|---|
| DeepSeek V4 Flash DeepSeek | 284B | — Not released | — Not released | 192 GB Barely fits on 4× |
| Solar Open2 250B Upstage | 250B 15B active | 601 GB Too large | 330 GB 8× · 54 GB free | 192 GB 8× · 192 GB free or 4× · 19% spare |
| Qwen3 235B-A22B Alibaba | 235B 22B active | 564 GB Too large | 310 GB 8× · 74 GB free | 181 GB 4× · 11 GB free |
| Laguna-S 2.1 Poolside | 118B 8B active | 282 GB 8× · 102 GB free | 155 GB 4× · 37 GB free | 90 GB 2× · 5.7 GB free |
| gpt-oss-120b OpenAI | 117B 5.1B active | — Not released | — Not released | 78 GB 2× · 18 GB free |
| Qwen3.6 35B-A3B Alibaba | 35B 3B active | 86 GB 2× · 9.7 GB free | 47 GB Barely fits on 1× | 28 GB 1× · 20 GB free |
| Qwen3 32B Alibaba | 32.8B | 79 GB 2× · 17 GB free | 43 GB 1× · 4.8 GB free | 25 GB 1× · 23 GB free |
| Gemma 4 31B Google | 31.3B | 75 GB 2× · 21 GB free | 41 GB 1× · 6.7 GB free | 24 GB 1× · 24 GB free |
| GLM-4.7 Flash Z.ai | 31.2B | 75 GB 2× · 21 GB free | 41 GB 1× · 6.8 GB free | 24 GB 1× · 24 GB free |
| Qwen3.6 27B Alibaba | 27.8B | 67 GB 2× · 29 GB free | 37 GB 1× · 11 GB free | 21 GB 1× · 27 GB free |
| Mistral Small 3.2 24B Mistral AI | 24B | 58 GB 2× · 38 GB free | 32 GB 1× · 16 GB free | 18 GB 1× · 30 GB free |
| gpt-oss-20b OpenAI | 21B 3.6B active | — Not released | — Not released | 17 GB 1× · 31 GB free |
Estimates: model weights plus 20% for KV cache and runtime overhead, at short context lengths. Long contexts and large batches need more. Rows marked “tight” hold the weights but not that full margin. A ×N figure is the total VRAM across N of these GPUs and makes no claim about interconnect throughput. How we estimate this · Start from a model instead
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