The NVIDIA L4 is an enterprise-class GPU built on the Ada Lovelace architecture, featuring 24 GB of memory with 300 GB/s of memory bandwidth and a 72 W TDP. It delivers 30.3 TFLOPS of FP32 performance and 121 TFLOPS of FP16 tensor performance. Currently available from 6 providers starting at $0.20/GPU/hour, with a market median of $1.00/GPU/hour across 21 configurations.
Hardware specifications
Same across all providers
Median price across all providers
6 offerings from 6 providers
Sorted by price ascending. Compare configurations side-by-side.
| Provider | Count | vCPU | RAM | Region | Per GPU hour | Total/hr | Action |
|---|---|---|---|---|---|---|---|
| ×1 | 6 | 62 GB | $0.490 | $0.49 | Launch | ||
| ×1 | 24 | 252 GB | From$0.200 +10.0% 30d | From$0.20 | Launch | ||
Up to$500credit | ×1 | 6 | 62 GB | $0.490 0.0% 30d | $0.49 | Launch | |
| ×1 | 8 | 48 GB | $0.917 +1.5% 30d | From$0.92 | Launch | ||
| ×4 | 32 | 192 GB | -- | From$0.998 +0.1% 30d | From$3.99 | Launch | |
Sesterce CloudReferral link | ×1 | 8 | 48 GB | $1.045 0.0% 30d | From$1.05 | Launch | |
| ×1 | 8 | 48 GB | $1.050 0.0% 30d | From$1.05 | Launch |
LLMs that fit on the NVIDIA L4
Estimated VRAM for popular open-weight LLMs at 16-bit (FP16/BF16), 8-bit (FP8/INT8) and 4-bit (INT4/MXFP4) precision, against 24 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 8× |
| Solar Open2 250B Upstage | 250B 15B active | — Not released | 330 GB Too large | 192 GB Tight on 8× · 19% spare |
| Qwen3 235B-A22B Alibaba | 235B 22B active | — Not released | 310 GB Too large | 181 GB 8× · 11 GB free |
| Laguna-S 2.1 Poolside | 118B 8B active | 282 GB Too large | 155 GB 8× · 37 GB free | 90 GB 4× · 5.7 GB free |
| gpt-oss-120b OpenAI | 117B 5.1B active | — Not released | — Not released | 78 GB 4× · 18 GB free |
| Qwen3.6 35B-A3B Alibaba | 35B 3B active | 86 GB 4× · 9.7 GB free | 47 GB Barely fits on 2× | 28 GB 2× · 20 GB free or 1× · 4% spare |
| Qwen3 32B Alibaba | 32.8B | 79 GB 4× · 17 GB free | 43 GB 2× · 4.8 GB free | 25 GB 2× · 23 GB free or 1× · 14% spare |
| Gemma 4 31B Google | 31.3B | 75 GB 4× · 21 GB free | 41 GB 2× · 6.7 GB free | 24 GB 2× · 24 GB free or 1× · 19% spare |
| GLM-4.7 Flash Z.ai | 31.2B | 75 GB 4× · 21 GB free | 41 GB 2× · 6.8 GB free | 24 GB Barely fits on 1× |
| Qwen3.6 27B Alibaba | 27.8B | 67 GB 4× · 29 GB free | 37 GB 2× · 11 GB free | 21 GB 1× · 2.7 GB free |
| Mistral Small 3.2 24B Mistral AI | 24B | 58 GB 4× · 38 GB free | 32 GB 2× · 16 GB free | 18 GB 1× · 5.6 GB free |
| gpt-oss-20b OpenAI | 21B 3.6B active | — Not released | — Not released | 17 GB 1× · 7.5 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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