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 7 providers starting at $0.20/GPU/hour, with a market median of $0.34/GPU/hour across 17 configurations.
Hardware specifications
Same across all providers
Median price across all providers
8 offerings from 7 providers
Sorted by price ascending. Compare configurations side-by-side.
| Provider | Count | vCPU | RAM | Region | Per GPU hour | Total/hr | Action |
|---|---|---|---|---|---|---|---|
| ×1 | 12 | 201 GB | $0.590 | $0.59 | Launch | ||
| ×4 | 96 | 251 GB | From$0.200 -6.2% 30d | From$0.80 | Launch | ||
Theta EdgeCloudTrending | ×1 | 48 | 252 GB | $0.330 -21.4% 30d | $0.33 | Launch | |
Up to$500credit | ×1 | 12 | 201 GB | $0.590 0.0% 30d | $0.59 | Launch | |
| ×1 | — | — | -- | $0.690 | $0.69 | ||
| ×1 | 8 | 48 GB | $0.881 -3.5% 30d | From$0.88 | Launch | ||
| ×1 | 8 | 48 GB | -- | $1.000 0.0% 30d | $1.00 | Launch | |
Sesterce CloudReferral link | ×1 | 8 | 48 GB | $1.045 0.0% 30d | From$1.05 | Launch | |
| ×1 | 8 | 48 GB | $1.230 +17.1% 30d | $1.23 | 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 or 6× · 11% spare | 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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