datacenter
Rent Nvidia L40S.
Ada Lovelace
48GB VRAM
350W
From $0.28/hr
Per hour
$0.28
Per day
$6.72
Per week
$47.04
Per month
$202
Provider spread
10 providers ·
up to 84% cheaper at the low end
Cheapest · $0.28/hr on Novita AI
Median $0.92/hr
Most expensive · $1.80/hr on AceCloud
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 $0.28/hr cheapest provider rate
Own on-prem
Electricity per day
—
350W TDP
Rent breakeven on $9,000 MSRP
— days
· — months
Workloads
Suitable workloads.
AI models that fit
See all 28 →
Run these on this GPU.
- DeepSeek R1 Distill Qwen 14B 1× · fp16
- FLUX.1 Schnell 1× · fp16
- Gemma 3 27B 1× · fp16
Cloud instances
See all 6 →
Hyperscaler bundles.
Pre-configured on 6 clouds — from $0.28/hr total
($0.28/hr per GPU).
FAQ
Frequently asked.
What AI models can I run on a Nvidia L40S?
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 48GB of VRAM.
What's the VRAM minimum to run a model on the Nvidia L40S?
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 L40S 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
$0.28/hr
Lowest $/hr per GPU
$0.28/hr
Providers
6
Instance shapes
6
| Provider | Instance | GPUs | vCPU | RAM | Disk | $/hr | $/hr per GPU | |
|---|---|---|---|---|---|---|---|---|
| novita-l40s-spot | 1× | — | — | — | $0.28/hr | $0.28/hr | ||
| rcrate-l40s | 1× | — | — | — | $1.10/hr | $1.10/hr | ||
| l40s-pcie | 1× | — | — | — | $1.30/hr | $1.30/hr | ||
| gpu-l40sx1 | 1× | — | — | — | $1.57/hr | $1.57/hr | ||
|
V
Vultr
|
vc2-gpu-l40s | 1× | — | — | — | $1.70/hr | $1.70/hr | |
| ace-l40s-on-demand | 1× | — | — | — | $1.80/hr | $1.80/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 L40S?
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 48GB of VRAM.
What's the VRAM minimum to run a model on the Nvidia L40S?
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 L40S 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
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
Models that run on this GPU.
GPU-count + quantization recommendations covering fine-tuning, inference, and run-it-yourself scenarios on the Nvidia L40S.
DeepSeek R1 Distill Qwen 14B
by DeepSeek
Required GPUs
1× Nvidia L40S
Total VRAM
48 GB
Parameters
15B
Context
128K tokens
FLUX.1 Schnell
by Black Forest Labs
Required GPUs
1× Nvidia L40S
Total VRAM
48 GB
Parameters
12B
Gemma 3 27B
by Google DeepMind
Required GPUs
1× Nvidia L40S
Total VRAM
48 GB
Parameters
27B (27B active)
Context
128K tokens
Qwen 2.5 Coder 32B
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia L40S
Total VRAM
48 GB
Parameters
33B
Context
128K tokens
DeepSeek R1 Distill Qwen 32B
by DeepSeek
Required GPUs
1× Nvidia L40S
Total VRAM
48 GB
Parameters
33B
Context
128K tokens
Qwen 2.5 32B
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia L40S
Total VRAM
48 GB
Parameters
33B
Context
128K tokens
Qwen 3 32B
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia L40S
Total VRAM
48 GB
Parameters
33B
Context
128K tokens
LLaVA 34B
by LLaVA Project
Required GPUs
1× Nvidia L40S
Total VRAM
48 GB
Parameters
34B
Context
4K tokens
Code Llama 34B
by Meta AI
Required GPUs
1× Nvidia L40S
Total VRAM
48 GB
Parameters
34B
Context
16K tokens
DeepSeek Coder 33B
by DeepSeek
Required GPUs
1× Nvidia L40S
Total VRAM
48 GB
Parameters
33B
Context
16K tokens
Yi-34B
by 01.AI
Required GPUs
1× Nvidia L40S
Total VRAM
48 GB
Parameters
34B
Context
32K tokens
Qwen: Qwen3.6 35B A3B
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia L40S
Total VRAM
48 GB
Parameters
35B
Context
262K tokens
Qwen: Qwen3.5-35B-A3B
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia L40S
Total VRAM
48 GB
Parameters
35B
Context
262K tokens
TheDrummer: Skyfall 36B V2
by Thedrummer
Required GPUs
1× Nvidia L40S
Total VRAM
48 GB
Parameters
36B
Context
33K tokens
Llama 3.3 70B
by Meta AI
Required GPUs
1× Nvidia L40S
Total VRAM
48 GB
Parameters
70B
Context
128K tokens
Qwen 2.5 72B
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia L40S
Total VRAM
48 GB
Parameters
73B
Context
128K tokens
DeepSeek R1 Distill Llama 70B
by DeepSeek
Required GPUs
1× Nvidia L40S
Total VRAM
48 GB
Parameters
70B
Context
128K tokens
Llama 3.1 70B
by Meta AI
Required GPUs
1× Nvidia L40S
Total VRAM
48 GB
Parameters
70B
Context
128K tokens
Code Llama 70B
by Meta AI
Required GPUs
1× Nvidia L40S
Total VRAM
48 GB
Parameters
70B
Context
16K tokens
Hermes 3 70B
by Nous Research
Required GPUs
1× Nvidia L40S
Total VRAM
48 GB
Parameters
70B
Context
128K tokens
Nous: Hermes 4 70B
by Nous Research
Required GPUs
1× Nvidia L40S
Total VRAM
48 GB
Parameters
70B
Context
131K tokens
Qwen: Qwen2.5 VL 72B Instruct
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia L40S
Total VRAM
48 GB
Parameters
72B
Context
131K tokens
Sao10K: Llama 3.1 70B Hanami x1
by Sao10k
Required GPUs
1× Nvidia L40S
Total VRAM
48 GB
Parameters
70B
Context
16K tokens
Sao10K: Llama 3.3 Euryale 70B
by Sao10k
Required GPUs
1× Nvidia L40S
Total VRAM
48 GB
Parameters
70B
Context
131K tokens
Magnum v4 72B
by Anthracite Org
Required GPUs
1× Nvidia L40S
Total VRAM
48 GB
Parameters
72B
Context
33K tokens
Sao10K: Llama 3.1 Euryale 70B v2.2
by Sao10k
Required GPUs
1× Nvidia L40S
Total VRAM
48 GB
Parameters
70B
Context
131K tokens
Sao10k: Llama 3 Euryale 70B v2.1
by Sao10k
Required GPUs
1× Nvidia L40S
Total VRAM
48 GB
Parameters
70B
Context
8K tokens
Meta: Llama 3 70B Instruct
by Meta AI
Required GPUs
1× Nvidia L40S
Total VRAM
48 GB
Parameters
70B
Context
8K 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 L40S?
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 48GB of VRAM.
What's the VRAM minimum to run a model on the Nvidia L40S?
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 L40S 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.