datacenter
租用 Nvidia L4.
Ada Lovelace
24GB VRAM
72W
From $0.18/hr
Per hour
$0.18
Per day
$4.32
Per week
$30.24
Per month
$130
Provider spread
5 providers ·
up to 78% cheaper at the low end
Cheapest · $0.18/hr on TensorDock
Median $0.61/hr
Most expensive · $0.81/hr on Amazon Web Services
Price history
Daily median across providers.
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Where to rent it
All providers carrying this GPU.
Buy vs rent
Should you rent or own?
Your usage
Rent on cloud
Per day
—
Per month
—
at $0.18/hr cheapest provider rate
Own on-prem
Electricity per day
—
72W TDP
Rent breakeven on $2,500 MSRP
— days
· — months
Workloads
Suitable workloads.
AI models that fit
See all 7 →
Run these on this GPU.
- Whisper Large v3 1× · fp16
- Whisper Medium 1× · fp16
- DeepSeek R1 Distill Qwen 7B 1× · fp16
Cloud instances
See all 2 →
Hyperscaler bundles.
Pre-configured on 2 clouds — from $0.61/hr total
($0.61/hr per GPU).
FAQ
Frequently asked.
What AI models can I run on a Nvidia L4?
The grid above lists every open-weights model with a recommended GPU configuration for this card. Each row tells you the minimum GPU count and the quantization level (FP16, FP8, INT8, INT4) needed to load the model in 24GB of VRAM.
What's the VRAM minimum to run a model on the Nvidia L4?
Rule of thumb: a model needs roughly (parameters × bytes-per-weight × 1.2) of VRAM to load, plus headroom for the KV cache during inference. FP16 = 2 bytes/weight, FP8/INT8 = 1 byte, INT4 = 0.5 bytes. A 70B model at FP16 needs ~168GB; at INT4 it drops to ~42GB and fits a single high-VRAM card.
How does quantization (FP16 vs FP8 vs INT4) affect what fits?
Lower-precision quantization shrinks the memory footprint nearly linearly with the bit count. The trade-off is output quality: FP16 is the reference, FP8 is usually indistinguishable for most prompts, INT8 introduces small quality losses, INT4 is noticeably degraded on reasoning-heavy tasks but fine for chat. The badge on each row tells you which level the recommendation assumes.
Can I fine-tune on the Nvidia L4 or only do inference?
Fine-tuning needs 4–8× more VRAM than inference at the same model size — gradients, optimizer state, and activations all live in memory. LoRA / QLoRA cut that overhead dramatically (often 4–10×). The notes column flags whether a row is an inference-only recommendation or includes a fine-tuning path.
Where do these GPU-count recommendations come from?
We curate them from official model cards, community benchmark threads (r/LocalLLaMA, HuggingFace forum), and known-good configurations published by the model makers. Each recommendation has been verified to load at the stated quantization on the listed GPU count — though throughput and context-length still vary by workload.
Cloud instance options
Pre-configured instances on hyperscalers.
Whole-instance bundles (GPU + vCPU + RAM + disk) on the major clouds. Per-GPU rate often drops as the count rises. View = spec page · Launch = sign up (affiliate).
Cheapest bundle
$0.61/hr
Lowest $/hr per GPU
$0.61/hr
Providers
2
Instance shapes
2
| Provider | Instance | GPUs | vCPU | RAM | Disk | $/hr | $/hr per GPU | |
|---|---|---|---|---|---|---|---|---|
| g2-standard-4 | 1× | — | — | — | $0.61/hr | $0.61/hr | ||
| g6.xlarge | 1× | — | — | — | $0.81/hr | $0.81/hr |
Looking for the cheapest rate?
Hyperscaler bundles include managed networking + SLAs. Raw per-GPU rental on P2P marketplaces is typically 3–10× cheaper.
See raw rental rates on the Overview tab →
FAQ
Cloud instances — common questions.
What AI models can I run on a Nvidia L4?
The grid above lists every open-weights model with a recommended GPU configuration for this card. Each row tells you the minimum GPU count and the quantization level (FP16, FP8, INT8, INT4) needed to load the model in 24GB of VRAM.
What's the VRAM minimum to run a model on the Nvidia L4?
Rule of thumb: a model needs roughly (parameters × bytes-per-weight × 1.2) of VRAM to load, plus headroom for the KV cache during inference. FP16 = 2 bytes/weight, FP8/INT8 = 1 byte, INT4 = 0.5 bytes. A 70B model at FP16 needs ~168GB; at INT4 it drops to ~42GB and fits a single high-VRAM card.
How does quantization (FP16 vs FP8 vs INT4) affect what fits?
Lower-precision quantization shrinks the memory footprint nearly linearly with the bit count. The trade-off is output quality: FP16 is the reference, FP8 is usually indistinguishable for most prompts, INT8 introduces small quality losses, INT4 is noticeably degraded on reasoning-heavy tasks but fine for chat. The badge on each row tells you which level the recommendation assumes.
Can I fine-tune on the Nvidia L4 or only do inference?
Fine-tuning needs 4–8× more VRAM than inference at the same model size — gradients, optimizer state, and activations all live in memory. LoRA / QLoRA cut that overhead dramatically (often 4–10×). The notes column flags whether a row is an inference-only recommendation or includes a fine-tuning path.
Where do these GPU-count recommendations come from?
We curate them from official model cards, community benchmark threads (r/LocalLLaMA, HuggingFace forum), and known-good configurations published by the model makers. Each recommendation has been verified to load at the stated quantization on the listed GPU count — though throughput and context-length still vary by workload.
AI models
FP16
FP16
FP16
FP16
FP16
FP16
FP16
Models that run on this GPU.
GPU-count + quantization recommendations covering fine-tuning, inference, and run-it-yourself scenarios on the Nvidia L4.
Whisper Large v3
by OpenAI
Required GPUs
1× Nvidia L4
Total VRAM
24 GB
Parameters
2B
Context
30 tokens
Datacenter STT workhorse.
Whisper Medium
by OpenAI
Required GPUs
1× Nvidia L4
Total VRAM
24 GB
Parameters
1B
Context
30 tokens
DeepSeek R1 Distill Qwen 7B
by DeepSeek
Required GPUs
1× Nvidia L4
Total VRAM
24 GB
Parameters
8B
Context
128K tokens
Llama 3.2 3B
by Meta AI
Required GPUs
1× Nvidia L4
Total VRAM
24 GB
Parameters
3B
Context
128K tokens
Stable Diffusion XL
by Stability AI
Required GPUs
1× Nvidia L4
Total VRAM
24 GB
Parameters
4B
Llama 3.1 8B
by Meta AI
Required GPUs
1× Nvidia L4
Total VRAM
24 GB
Parameters
8B
Context
128K tokens
Mistral 7B v0.3
by Mistral AI
Required GPUs
1× Nvidia L4
Total VRAM
24 GB
Parameters
7B
Context
33K tokens
Renting for inference?
Pair these models with the cheapest provider on the rental table.
See rental rates on the Overview tab →
FAQ
AI models on this GPU.
What AI models can I run on a Nvidia L4?
The grid above lists every open-weights model with a recommended GPU configuration for this card. Each row tells you the minimum GPU count and the quantization level (FP16, FP8, INT8, INT4) needed to load the model in 24GB of VRAM.
What's the VRAM minimum to run a model on the Nvidia L4?
Rule of thumb: a model needs roughly (parameters × bytes-per-weight × 1.2) of VRAM to load, plus headroom for the KV cache during inference. FP16 = 2 bytes/weight, FP8/INT8 = 1 byte, INT4 = 0.5 bytes. A 70B model at FP16 needs ~168GB; at INT4 it drops to ~42GB and fits a single high-VRAM card.
How does quantization (FP16 vs FP8 vs INT4) affect what fits?
Lower-precision quantization shrinks the memory footprint nearly linearly with the bit count. The trade-off is output quality: FP16 is the reference, FP8 is usually indistinguishable for most prompts, INT8 introduces small quality losses, INT4 is noticeably degraded on reasoning-heavy tasks but fine for chat. The badge on each row tells you which level the recommendation assumes.
Can I fine-tune on the Nvidia L4 or only do inference?
Fine-tuning needs 4–8× more VRAM than inference at the same model size — gradients, optimizer state, and activations all live in memory. LoRA / QLoRA cut that overhead dramatically (often 4–10×). The notes column flags whether a row is an inference-only recommendation or includes a fine-tuning path.
Where do these GPU-count recommendations come from?
We curate them from official model cards, community benchmark threads (r/LocalLLaMA, HuggingFace forum), and known-good configurations published by the model makers. Each recommendation has been verified to load at the stated quantization on the listed GPU count — though throughput and context-length still vary by workload.