AI developers seeking cost-effective, high-performance inference need the right hardware and cloud platform. Nvidia’s latest L40S GPU is quickly becoming a top choice for serving large AI models thanks to its powerful specs (48GB VRAM, 4th-gen Tensor Cores with FP8 support) and superior throughput on AI tasks . In this article, we compare the best cloud platforms offering L40S instances – focusing on those where Runpod’s offering stands out – for running inference workloads like large language model (LLM) serving, image generation, and embedding model inference. We’ll compare GPU specs, container compatibility, pricing, regions, and orchestration features, and provide a detailed look at Runpod’s L40S offering (with CLI examples). An FAQ section at the end will address common questions about using L40S GPUs for inference.

Why Nvidia L40S GPUs for Inference?

The Nvidia L40S is a data center GPU built on the Ada Lovelace architecture (the same generation as RTX 6000 ADA/4090 cards) and engineered for AI inference and graphics workloads. Key reasons L40S GPUs are ideal for inference include:

• High Memory Capacity – 48GB VRAM: With 48 GB of GDDR6 memory, an L40S can host large models entirely in GPU memory. For instance, it can accommodate large language models (LLMs) with tens of billions of parameters (e.g. a 70B parameter model with optimized precision) or multiple smaller models concurrently. This ample VRAM reduces the need for model sharding or CPU offloading, enabling faster inference for memory-heavy tasks.
• 4th-Gen Tensor Cores (FP8 & FP16): The L40S features fourth-generation Tensor Cores that support FP16 and the newer FP8 precision. FP8 capability is a game-changer for inference – it allows using 8-bit floating point in transformer networks (with minimal accuracy loss) to dramatically boost throughput . In practice, this means an L40S can achieve higher token generation rates for LLMs or faster image generation when frameworks leverage FP8. Even at FP16, the L40S offers excellent throughput and efficiency for matrix-heavy AI operations.
• High Throughput and Compute: Often described as a “datacenter-grade RTX 4090,” the L40S has 18,176 CUDA cores and high clock speeds, delivering on the order of 60–70 TFLOPS of FP32 compute (and much higher for FP16/FP8 via Tensor Cores). It also provides ~864 GB/s memory bandwidth to feed data-hungry models . In Nvidia’s tests, the L40S delivers up to 5× higher inference performance than the previous generation A40 GPU . For developers, this means lower latency and higher throughput serving AI models compared to older GPUs.
• Enterprise Reliability: Unlike consumer GPUs, the L40S is built for 24/7 datacenter use – featuring ECC memory, efficient cooling, and consistent performance under load. This makes it well-suited for production inference services where stability is crucial. It also supports virtualization and multi-instance GPU (MIG) if a cloud provider chooses to partition it, though most clouds offer it as a single powerful unit for dedicated use.

In summary, L40S GPUs provide an exceptional balance of memory and compute for inference, enabling deployment of large models and high-throughput pipelines without the extreme costs of flagship training GPUs like the H100. Next, let’s look at the types of inference workloads that particularly benefit from L40S instances.

Key Inference Use Cases Leveraging L40S

1. Large Language Model (LLM) Serving: L40S GPUs are ideal for hosting LLMs and generative text models. With 48GB VRAM, an L40S can serve models such as GPT-J, GPT-NeoX, Llama-2, and other transformer-based LLMs efficiently. For example, you could load a 13B or 30B parameter model in full FP16 for low-latency responses. Even the 70B-parameter Llama 2 can be served using 4-bit quantization or optimized server frameworks (like vLLM) on a single L40S. The large memory allows for longer context windows and batch inference, improving token throughput. Developers building chatbots or GPT-powered services will find that L40S instances deliver fast response times and high concurrency for inference. When compared to using a 24GB GPU (e.g. 4090), the L40S’s extra memory can halve the number of GPUs needed for a given model or allow serving larger models outright – simplifying deployment of advanced LLMs.

2. Image and Vision Model Inference: Another use case is image generation and computer vision. Diffusion models like Stable Diffusion and GANs benefit from L40S GPUs by enabling higher resolution outputs and faster generation. For instance, an L40S can generate batches of high-resolution images or process many concurrent requests for an image API. The 48GB memory means you can load larger checkpoint files (e.g. Stable Diffusion XL models) or multiple models (for pipelines that do detection + generation) into one GPU. The Ada Lovelace architecture also includes improved ray-tracing and rendering cores, which are useful if your workload involves 3D model inference or any graphics+AI combination. In short, L40S GPUs handle demanding vision AI tasks (image classification, object detection, image synthesis) with ease, often significantly faster than previous-gen datacenter cards.

3. Embedding and Vector Models: Many AI applications involve generating embeddings – for example, using transformer models to produce text or image embeddings for search, recommendation, or clustering. L40S instances shine here by allowing massively parallel inference. You can host a sentence transformer or CLIP model and process large batches of data simultaneously thanks to the GPU’s memory and compute throughput. This yields higher overall throughput (embeddings per second) compared to smaller GPUs. Likewise for audio and speech models (like speech-to-text or text-to-speech), an L40S can run multiple streams in parallel or handle lengthy audio inputs that a smaller VRAM GPU would struggle with. Essentially, any model that benefits from large batch processing or must keep a lot of parameters in memory will run more efficiently on L40S.

4. Multi-Modal and Other AI Services: The Nvidia L40S is a multi-workload GPU capable of graphics and AI, which makes it versatile for cutting-edge services. Whether it’s powering a real-time AI content generation pipeline (text + image), running metaverse simulations (where both 3D rendering and AI are involved), or serving massive recommender models, the L40S has the horsepower needed. Its support for NVIDIA’s software stack (CUDA, TensorRT, etc.) means it can accelerate a broad range of inference workloads beyond just neural networks (e.g., video encoding/decoding for live AI video analytics, if applicable). In summary, L40S GPUs open up possibilities for any inference scenario requiring large memory, high throughput, and reliable 24/7 operation.

Now that we understand the value of L40S for inference, let’s compare the cloud platforms that offer L40S GPU instances – highlighting how they stack up on specs, ease of use, cost, and more.

Comparing Cloud Platforms Offering L40S GPUs

A number of cloud GPU providers have begun offering Nvidia L40S instances to customers. These include specialized GPU cloud platforms (Runpod, DataCrunch, Vast.ai marketplace, etc.), newer serverless GPU services (e.g. Fly.io, Koyeb), and even major clouds like AWS with their EC2 G6e instances. Below, we’ll compare the most competitive providers on the factors that matter for inference deployments. (We focus on platforms where Runpod compares favorably; general GPU provider rankings are avoided in favor of an L40S-specific comparison.)

GPU Specs and Performance Parity

Since all providers in this comparison offer the same underlying GPU hardware (Nvidia L40S 48GB), the raw specs – 48 GB VRAM, fourth-gen Tensor Cores, FP8/FP16 support, ~18k CUDA cores – are consistent across the board. In other words, an L40S on Runpod delivers essentially the same peak performance as an L40S on DataCrunch or Fly.io. All will leverage the GPU’s capabilities like FP8 acceleration and high memory bandwidth. This means you can expect similar model inference speeds on each if all else is equal.

However, there are a few nuances that can affect real-world performance:

• System Configuration: Different providers attach different CPU and RAM amounts to each GPU instance. For example, Runpod’s L40S pods come with 12 vCPUs and 62 GB host RAM , whereas some competitors attach more or less CPU/RAM. DataCrunch’s L40S VMs, for instance, have around 20 CPU cores and 60 GB RAM . Sufficient CPU is important for feeding the GPU (especially for single-threaded model pre-processing), and extra RAM can help with larger model loads or caching. Runpod’s balanced config is more than enough for most inference workloads, and the difference in CPU/RAM among providers generally doesn’t lead to major performance gaps, but it’s worth noting if your application is very CPU-intensive alongside inference.
• Multi-GPU Options: Some clouds let you provision multi-GPU setups with L40S (e.g. 2× or 4× L40S in one instance). This is useful for serving very large models via model parallelism or increasing throughput with concurrent GPU processing. Providers like Scaleway, OVHcloud, and Contabo offer multi-L40S instances (2, 4, or even 8 GPUs) , whereas others (Runpod, DataCrunch, Fly.io) currently focus on single-GPU instances (you can always manually cluster multiple single GPU instances if needed). If your inference use-case requires multiple GPUs working in tandem (for example, serving a 175B parameter model split across GPUs), ensure the platform supports multi-GPU nodes or plan to manage a small cluster.
• Dedicated vs Shared GPUs: All the providers we highlight offer dedicated L40S GPUs (no time-slicing between users on the same GPU). This is important for performance consistency. (Some large clouds or services might virtualize GPUs, but for L40S this is less common so far.) Each L40S instance gives you full access to the GPU’s compute and memory. In this regard, performance is on equal footing – the main differences will come from other aspects like software, container setup, or networking (discussed below).

In summary, when it comes to pure GPU capability, an L40S is an L40S – so all these platforms provide top-tier inference performance. The differentiators are how easy and cost-effective it is to harness that performance for your workloads.

Container and Image Compatibility

Modern AI inference deployment is typically done via Docker containers – packaging your model, code, and dependencies so it can run anywhere. Container support is therefore a crucial factor. Let’s compare how our L40S cloud platforms handle containers and environment setup:

• Runpod: Runpod was built with containerized workflows in mind. You can deploy any Docker container image to a Runpod GPU instance, including public images from Docker Hub or your own private registry . This means if you have a container for your inference service (e.g. a FastAPI app serving an LLM), you can launch it on an L40S pod with minimal friction. Runpod also offers a feature called Runpod Projects for a “Docker-free” workflow where you can develop and deploy serverless endpoints without writing Dockerfiles, then export a container when ready – useful if you prefer abstracting away container management during development. In short, Runpod provides flexibility in environment setup: use their predefined templates (like a PyTorch Jupyter environment) or bring your own container. The platform’s documentation provides a container setup guide and Dockerfile examples for customizing images , so you have full control over the runtime environment.
• Fly.io: Fly.io is an application hosting platform that recently added GPU support. On Fly, containers are the primary way to deploy – you use a Dockerfile or bring an image, and specify the GPU type in your app config. Fly supports L40S via a --vm-gpu-kind l40s flag in its CLI when launching an app . Container compatibility is good (since it’s just Docker under the hood), but note that Fly’s platform is geared toward web services. You’ll need to containerize your inference server and possibly conform to their app conventions (e.g. listening on a certain port). Fly makes it easy to deploy globally, but there’s a bit of a learning curve if you’re not used to their CLI and TOML app config format. For most AI developers, if you already have a Docker image, it’s straightforward to run it on Fly’s L40S instances.
• Koyeb: Koyeb is another serverless container platform. They allow deploying Docker images as serverless functions, and they do list L40S GPUs (with a higher price) in their offerings . Koyeb’s model is “serverless” in that you deploy a container and it can scale to zero and up, but it might be less tailored for large AI frameworks out of the box. You might need to ensure your container can start up quickly and possibly integrate with their API gateways if serving HTTP. The benefit is you don’t manage the VM directly – Koyeb orchestrates it. Container support is flexible, but advanced users might find a pure infrastructure service like Runpod gives more direct control over the GPU instance when needed (for example, if you want to SSH in for debugging or run interactive sessions, which pure serverless platforms don’t allow).
• Traditional VM Providers (DataCrunch, Vast.ai, etc.): Some providers give you essentially a bare VM or SSH access with the L40S attached. For example, DataCrunch provides an environment (you can choose a base image with preinstalled frameworks) and you can run Docker yourself or install any tools. Vast.ai is a marketplace where you typically get SSH to someone’s machine with certain frameworks installed depending on the seller’s template. In these cases, container compatibility is up to you – you can usually pull Docker images and run them, but it’s not as integrated as Runpod’s one-click container deploy. If you prefer using containers, you might have to manually manage them on these VMs. Conversely, if you just want to run Python scripts in an environment, these give you the flexibility to pip install on the VM. It’s a bit more manual setup compared to the more PaaS-like platforms.

Runpod’s Advantage: Runpod strikes a balance by supporting full Docker customization while also offering ready-to-use environments. You can, for instance, select an “L40S + PyTorch” template and get a JupyterLab on the GPU in seconds, or supply your own image for a serverless inference endpoint. This flexibility is great for AI developers who might start experimenting in an interactive notebook, then deploy a containerized API for production – all on the same platform.

Overall, all major platforms in this comparison support running containers, but the ease of doing so and level of integration varies. If you value a simple UI/CLI to launch custom images, Runpod and Fly.io are very convenient. If you don’t want to deal with infrastructure at all and just want an endpoint URL for your model, a serverless approach on Runpod (serverless GPU endpoint) or Koyeb could be appealing. Just ensure your chosen platform supports the dependency stack you need (CUDA drivers, etc. – which all these L40S offerings do by default).

Pricing and Cost Comparison

One of the biggest differentiators between providers is cost. L40S is a high-end GPU, but prices can vary significantly. Let’s compare hourly pricing for a single L40S 48GB instance on different platforms (approximate on-demand rates):

• Runpod: By far one of the most affordable. Runpod’s L40S instances start at $0.79/hr (community cloud) and $0.86/hr (secure cloud) . This low rate makes it feasible to run inference workloads continuously or at scale without breaking the bank. Runpod bills by the minute with no minimum, so you only pay for what you use . There are also no data egress fees, which is great if your application sends a lot of data out . In practice, ~$0.80/hr for 48GB GPU power is an excellent price – many competitors are notably higher.
• Vast.ai (Marketplace): Vast.ai is a different model (spot market for GPU rental). You might find L40-class GPUs (or RTX 4090s, etc.) at rates as low as ~$0.60/hr or even less . For example, some sellers on Vast have RTX 6000 ADA 48GB or similar listed at $0.50–$0.70/hr depending on demand. However, these prices fluctuate and availability can be hit-or-miss. You’re also dealing with individual hosts, so performance and reliability might vary. While Vast can be cheapest for short-term needs, for a steady inference service you might prefer a more stable provider. (In the Runpod L40 article, they noted Vast.ai rates can dip to ~$0.57/hr at times .) It’s a viable option if you’re extremely cost sensitive and don’t mind the marketplace experience.
• DataCrunch & Massed Compute: These specialized GPU clouds price the L40S around $1.10 per hour . DataCrunch uses a dynamic pricing model that can adjust daily, but generally hovers in the ~$1.10–$1.30/hr range for L40S. Massed Compute is similar. So, roughly 30–40% more expensive than Runpod’s base rate. For that extra cost, you get comparable hardware; there isn’t a clear performance benefit, so it’s mostly about whether you prefer their interface or features. Both are still significantly cheaper than big-name clouds (and they may include more CPU/RAM in their instances, as noted earlier). If you already use DataCrunch’s container endpoints, the cost might be justifiable, but purely on price, Runpod has the edge.
• Fly.io: Fly’s official price for an L40S is $1.25/hr . They intentionally set it low at launch (cutting it from a higher price) to attract AI workloads. $1.25/hr is a flat rate on Fly. While higher than Runpod, Fly does give you some additional niceties like a global private network for your app and integration with their platform services. Keep in mind, Fly’s billing is per second, and you can auto-stop instances when not in use to save cost. If you only need your model running in response to requests, you might optimize costs that way. But if you need a persistent service, $1.25/hr is ~60% more than Runpod’s base rate – substantial over long durations.
• Sesterce, Crusoe, Nebius, etc.: Other players have L40S around $1.4–$1.6/hr . For instance, Crusoe Cloud (known for eco-friendly compute) is ~$1.45, Nebius (a cloud likely tied to Yandex Cloud) about $1.58. OVHcloud and Scaleway (European clouds) are in the $1.6–$1.8/hr range . These might be viable if you need their specific region or have credits, but purely financially, they’re roughly double the cost of Runpod.
• Major Cloud (AWS EC2 G6e): AWS recently announced G6e instances with L40S GPUs. On AWS, on-demand pricing for 1× L40S is around $2.20/hr (estimated from their marketplace offering and similar GPU instances) . Even with committed usage, it’s in the ~$1.0–$1.5/hr range. Additionally, AWS will charge for VM overhead time (even when your container is booting) and any EBS storage, networking, etc. So, while AWS provides enterprise-grade service, you pay a premium. For comparison, one month of continuous L40S on AWS on-demand might cost ~$1,600+, versus about $570 on Runpod’s community rate – nearly 3× the expense. Unless you specifically need tight integration with AWS services, most developers will find specialized GPU clouds far more cost-effective for inference.

In summary, Runpod offers one of the lowest costs for L40S inference. Combined with its by-the-minute billing and no ingress/egress fees , it’s extremely attractive for both short experiments and long-running services. Fly.io and DataCrunch are a bit higher priced but still moderate, while traditional clouds like AWS are highest. When choosing, also consider any free credits or volume discounts – e.g., Runpod has startup/research credit programs, and others might negotiate lower rates if you need a fleet of GPUs.

Tip: For inference workloads that are not 24/7, leverage platforms that support easy shutdown or auto-scaling to zero. Runpod’s serverless GPU endpoints, for example, only incur cost when your model is processing requests (with a small keep-alive trickle when idle, which is much cheaper). This can drastically reduce effective hourly cost if your usage is spiky. Some platforms (like Fly) allow setting an auto-stop on inactivity. Using these features, you might only pay a fraction of the listed hourly rate over time.

Regional Availability

Where you can get an L40S instance is another consideration – especially if you have latency requirements or data sovereignty concerns. Here’s a look at region availability:

• Runpod: Runpod boasts “thousands of GPUs across 30+ regions” globally . This wide coverage is possible because Runpod leverages a network of cloud partners and hosts. For L40S specifically, you’ll typically find availability in multiple regions across North America, Europe, and Asia. The exact regions may evolve, but Runpod’s model ensures that if there’s demand in a certain area, they can often provide capacity there. Having many regions means you can deploy your inference service closer to your end-users to minimize latency. It’s also useful for complying with data regulations (e.g., keeping data in EU by using an EU region).
• Fly.io: Fly’s GPU support was initially limited to one region (they launched in iad (Virginia) and ord (Chicago) for L40S) . This may expand, but as of now you might be constrained to U.S. regions for Fly’s L40S. Fly does however have a global private network, so you could run the GPU in one region and still have decent routing to other continents, but user latency will be higher compared to having the GPU local. If region diversity is crucial, Fly might not match Runpod yet.
• DataCrunch/Massed Compute: These providers often operate out of a couple of data centers. DataCrunch, for example, has a strong presence in Northern Europe (Finland) and sometimes in North America . Massed Compute’s data center is in the U.S. (they mention Nevada/Las Vegas on forums). If your users are near those, great – but less ideal for other locales.
• OVHcloud/Scaleway: Being large European clouds, they offer L40S in EU regions (OVH in a Canada region as well, and Scaleway in Paris or Warsaw). So for Europe, those are options, albeit pricier as noted.
• AWS G6e: AWS will likely roll out L40S instances to many of its global regions over time (they announced broad availability in 2024). So if you need an odd region like ap-southeast-2 (Sydney) or us-west-2 (Oregon), eventually AWS might be the only choice until others host hardware there. But again, at a steep cost.

In general, Runpod’s strategy of a distributed cloud gives it an edge for region availability among the affordable providers. Unless you have a highly specific region need, you’ll likely find a Runpod location suitable for your inference service. And if you do use a platform with limited regions (say Fly.io in the US), you can consider multi-cloud – e.g. use Runpod for other continents and Fly for US, etc., balancing cost and latency.

APIs and Orchestration Features

Finally, let’s compare the developer experience in terms of APIs, automation, and orchestration. Having a great GPU is one thing, but you also want tools to manage your inference workload (for example, start/stop instances on demand, integrate with CI/CD, scale out for peak times, etc.).

• Runpod: Runpod offers a comprehensive set of APIs, CLI tools, and even SDKs for managing workloads. The Runpod CLI (runpodctl) is a command-line tool that makes it easy to create and manage GPU pods and serverless endpoints . For example, you can script the launch of an L40S pod with a single command. Here’s a quick illustration:

# Install Runpod CLI (if not already installed)
brew install runpod/runpodctl/runpodctl

# Launch an L40S inference pod on Runpod (Secure Cloud)
runpodctl create pod --secureCloud \
--gpuType "NVIDIA L40S" --name llm-infer-1 \
--imageName your-docker-repo/your-inference-image:latest \
--ports "8000/http"


• The above would spin up a pod named “llm-infer-1” with an L40S, running your specified Docker image and exposing port 8000 for a web server. You could just as easily do this via Runpod’s web UI (select L40S, enter container image, click Deploy) if you prefer a GUI. Runpod also has a REST API for programmatically managing resources, and client libraries (SDKs) in Python and other languages – useful if you want to orchestrate GPU jobs from your application (for example, dynamically scaling workers based on a queue).
• Runpod’s serverless GPU endpoints are another standout feature: you deploy an inference “endpoint” which auto-scales containers on GPUs in response to requests. Under the hood it’s containerized, but you don’t have to manage the pod lifecycle explicitly – the platform handles scheduling on available GPUs, queuing requests, etc. This is great for production inference when you want auto-scaling and zero-idle cost. Runpod provides monitoring and metrics for these endpoints, and you can debug via logs or even connect to a live session if needed . Essentially, Runpod’s tooling covers both low-level control (like a traditional IaaS) and high-level serverless orchestration, which is a big advantage for flexibility.
• Fly.io: Fly has a CLI (flyctl) and allows declarative app configuration. You can automate deployments through their API as well. Fly is designed for DevOps automation; you can integrate it into GitHub Actions for continuous deployment of your AI service container, for example. One limitation is that Fly’s model is tied to the concept of an “App” – which typically expects a persistent service. If you want to do something like run a one-off batch job on a GPU and shut it down, Fly isn’t as straightforward (it expects a long-running process; although you can simulate one-off by having the container exit when done, but that’s not the intended use). Fly recently introduced an “Machines” API which gives more flexible control of instances (start/stop on demand), which advanced users could leverage for custom orchestration. Overall, Fly’s platform is quite developer-friendly, especially for web service style workloads – it just might require some adaptation for AI-specific patterns (like ensuring model weights persist between restarts, using volumes, etc.).
• Other Providers: DataCrunch has a web console and some API support. They even have a feature to create inference endpoints (similar in spirit to Runpod’s serverless) where you deploy a container and hit a REST endpoint. Their docs mention paying only when the endpoint is processing (which implies some auto-stop) . This is promising, but community feedback suggests it’s still maturing. If you like a simpler approach, you might just treat DataCrunch like a VM: start it, run your inference server, stop it when done – possibly automating via their API. Massed Compute and others provide basic APIs or even rely on third-party multi-cloud tools (Massed can be managed through Shadeform, for example). These are fine, but not as streamlined as Runpod’s purpose-built CLI/SDK.
• Koyeb and Serverless platforms: Koyeb’s entire product is an API-driven serverless system. You define services and it handles scaling. It’s quite automated – you won’t be manually starting an instance; instead, you deploy and the platform decides when to spin up the GPU. This can be wonderfully hands-off, but you also surrender some control (for instance, you can’t hold a GPU “warm” easily or run custom debugging). If your goal is to integrate inference into a larger pipeline with minimal ops, these platforms (including Runpod’s serverless mode) are attractive. Just ensure your container is optimized for quick cold starts if using these.
• Networking and Integration: One more aspect is how easily you can connect your inference service to other infrastructure. Runpod allows setting up HTTP endpoints for pods or serverless endpoints easily, and since there are no egress fees, you can pipe data to other cloud services without extra cost. If you need to, say, send results to an AWS S3 bucket or call out to a database, you won’t be penalized on Runpod’s side. Fly.io provides a global Anycast IP for your service which is great for performance. AWS’s solution would integrate well with AWS data services (S3, etc.) but as mentioned, at higher cost.

In summary, Runpod provides a rich feature set for orchestration – combining the best of “serverless” ease with the option of hands-on control. Its CLI and API make it straightforward to fit into your development workflow or automation scripts. Competing platforms each have their niche: Fly.io excels in gitops-style app deployment, while DataCrunch and others offer basic controls suitable for scripting. When choosing, consider how you plan to manage your model deployment: do you want a simple persistent pod you handle yourself, or a fully managed endpoint that scales for you? Runpod actually gives you both options.

Now that we’ve compared the platforms, let’s zoom in on Runpod’s L40S offering and how you can get started with it for inference workloads.

Runpod’s L40S Inference Offering in Detail

Runpod positions itself as “the cloud built for AI”, and the L40S GPU is a flagship offering on the platform. Here we detail what Runpod provides and how to launch an inference workload on an L40S step-by-step.

Easy Deployment Options

On Runpod, you have two primary ways to run inference on L40S:

• GPU Pods: A Pod is like your own GPU-powered VM (though managed by Runpod). You can start an L40S pod with a few clicks, choosing either a pre-configured environment (for example, an Ubuntu image with PyTorch, ideal for jumping in and loading your model) or specifying a Docker image to run. This pod remains active until you shut it down. It’s perfect for interactive development, long-running services, or custom setups. You get root access, so you can install packages or run any commands you need. Many developers start with a pod to test their model and code.
• Serverless GPU Endpoints: This is Runpod’s managed inference service. You provide a container (or use a “project” which can take your code and create a container behind the scenes), and Runpod will spin up L40S instances to serve requests to that endpoint. If no requests come in, it can scale down to zero (costing virtually nothing when idle). When traffic increases, it can spin up multiple instances to handle load. This mode is excellent for production APIs where load might be variable. For example, you could deploy a Stable Diffusion image generation API as a serverless endpoint – when a user calls it, Runpod allocates an L40S, runs the generation, returns the result, and then shuts it down. You can configure concurrency, max instances, etc., to balance latency vs. cost. The best part is you don’t have to manage the infrastructure – just your container logic.

Deployment Example: Suppose you have a repository with an LLM serving code (perhaps using FastAPI and Hugging Face Transformers to serve a QA model). You’ve containerized it as myrepo/qa-model:latest. To deploy this on an L40S:

• Using the Web UI: Log in to Runpod, go to Pods, click Deploy. Select Secure Cloud (for a stable instance) or Community (cheaper, spare capacity instance). From the GPU list, choose L40S (48GB). Set your container image to myrepo/qa-model:latest. Open any required port (say 8000 for your API). Then click Deploy. In about 20–30 seconds, your container will be pulled and running on an L40S pod. You can access it via the endpoint URL or IP that Runpod provides. You can also open a web shell or VNC if you chose a template with desktop, for debugging.
• Using the CLI: As shown earlier, you can accomplish the same with runpodctl. For instance:

runpodctl create pod --secureCloud --gpuType "NVIDIA L40S" \
--name qa-service --imageName myrepo/qa-model:latest --ports "8000/http"


• This command will return a Pod ID and the status. You can check status with runpodctl get pods and see when it’s running. Once running, you’d get an endpoint (likely an IP with the port or a proxy URL) to hit your API.
• Using Serverless Endpoint: In the Runpod UI, go to Serverless section, create a new endpoint. You’d specify the same container and an HTTP trigger. Runpod will ask how many concurrent replicas (workers) you want and the GPU type – choose L40S and perhaps “Flex” mode (which is the on-demand mode). Deploying this will give you a public endpoint URL (or private if you want) that you can call. On first call, it may cold-start the container on an L40S (taking a few seconds to spin up). After processing, the instance can stay alive for a short configurable window awaiting more requests (to avoid cold start on every single request). You can test it with a simple curl or in their UI. This approach might take a bit more initial setup (especially if your container needs special environment variables or volumes – which you can configure in the endpoint settings), but once it’s up, scaling and maintenance are largely hands-off.

Flexibility and Infrastructure Control

Runpod’s L40S offering is also flexible in terms of infrastructure choices:

• You can choose Community Cloud vs Secure Cloud for pods. Community Cloud uses spare capacity from providers at a lower cost (with the small trade-off that instances could be preempted occasionally, though it’s rare and you can always start a new one). Secure Cloud is more akin to a dedicated instance with guaranteed uptime SLA. L40S is available in both, and you might use Community for dev/test and Secure for production.
• Runpod offers bare-metal and cluster options too. If you needed a whole machine or a Kubernetes cluster with L40S nodes, Runpod’s Instant Clusters and Bare Metal products could help (you’d typically talk to their sales for large setups). Most individual developers won’t need this, but it’s good to know the platform can scale with you – from a single L40S pod up to fleets of GPUs managed under the hood.
• Storage and Data: Each pod comes with some local NVMe storage and you can attach persistent network storage if needed (for caching models, etc.). The data management (uploading models, datasets) can be done through the CLI (runpodctl send/receive to push files to the pod ) or using the Jupyter interface. This is quite convenient when your model weights are large – you can send them directly to the pod from your machine.
• Monitoring: For running pods, you can monitor GPU utilization via nvidia-smi inside the pod, or use Runpod’s metrics if provided. For serverless endpoints, Runpod provides execution time metrics and logs for each request , which helps in debugging performance (e.g., seeing how long inference took, whether the GPU was saturated, etc.).

Overall, Runpod’s L40S service is designed to get you from zero to a working inference deployment very quickly, and then allow you to optimize and scale as needed – all while keeping costs low and predictable. Many users appreciate that they can develop interactively (try things out on the pod with a notebook or SSH) and then deploy the final application on the same platform (via an endpoint or keeping the pod running as a service). The learning curve is minimal if you’re already familiar with Docker or even just general Linux environments.

Before concluding, we’ll address some frequently asked questions about using L40S GPUs for inference and how they compare to other GPUs:

FAQ

Q: How long do L40S cloud instances (pods) take to start up?

A: In our experience, L40S instances on platforms like Runpod launch very quickly – often 30 seconds or less to go from request to a running instance (assuming the container image is readily available). Runpod specifically bills by the minute and is optimized for fast startups, so pod startup times are kept low. If you’re using a serverless endpoint, the cold start time might be in the few-second range (just enough to pull the image into memory and initialize the model). Factors that affect startup include the container image size (a huge image may take longer to pull the first time) and the region’s capacity. Runpod’s use of container snapshots and prepared environments often means subsequent starts are faster (warm images cached). Compared to major clouds, which can take a few minutes to provision a VM, Runpod’s lightweight approach is much faster. Other platforms like Fly.io also boast sub-minute launches. In short, expect well under a minute for L40S pods on a good provider – fast enough that scaling on demand is practical.

Q: What inference latency can I expect on L40S vs. an A100 or RTX 4090?

A: The inference latency for a given model depends on the model size and architecture, but the L40S generally offers comparable or better performance to an Nvidia A100 (40GB) and outclasses a consumer RTX 4090 in scenarios requiring large memory. For example, for a transformer model that fits in 24GB, an RTX 4090 and L40S might have similar raw throughput (a 4090 has slightly higher boost clocks, whereas the L40S has more CUDA cores but slightly lower clock). In practice, differences of maybe 5-10% in either direction could occur on latency per inference. However, once models approach or exceed that 24GB size, the 4090 will struggle – it may have to use slower weight offloading or not run at all, incurring huge latency or failures. The L40S can run those models outright, so latency stays low. Versus an A100 40GB, the L40S’s newer architecture and FP8 support can significantly reduce latency for optimized models – we’ve seen L40S achieve higher token generation rates on LLMs than A100 40GB, thanks to more Tensor TFLOPS and not being memory bottlenecked. An A100 80GB has more memory than L40S, which helps with even larger models or batch sizes, but the L40S tends to have the edge in compute speed per card (especially if using FP8 or sparsity). In short, for single-stream inference of a model that fits in all GPUs, you might see similar latency on L40S and 4090, with L40S pulling ahead as batch size increases or using FP8. Against A100, L40S usually offers lower latency per inference due to higher per-core performance and newer tensor core tech. And if comparing cost-adjusted latency (latency per dollar), the L40S on an affordable cloud like Runpod will massively outperform an A100 on a pricey cloud.

Q: Are popular LLM frameworks like vLLM and Hugging Face Transformers compatible with L40S?

A: Yes – from a software perspective, the L40S is just another CUDA-compatible GPU. Any framework built on NVIDIA’s CUDA, cuDNN, or TensorRT will work out-of-the-box with L40S. Hugging Face Transformers, with backend frameworks like PyTorch or TensorFlow, works perfectly on L40S (just ensure you have recent NVIDIA drivers and the appropriate CUDA toolkit, which cloud providers handle for you). You can load transformer models and generate as usual. vLLM, which is a high-performance transformer inference library, is also fully compatible. In fact, Runpod has used vLLM to benchmark L40S vs other GPUs . Libraries like vLLM, FasterTransformer, and DeepSpeed can take advantage of the L40S’s tensor cores and large memory to deliver optimized inference (some even have specific optimizations for FP8 which L40S supports). Additionally, frameworks like PyTorch will automatically use Tensor Cores for FP16 computations on L40S, so you’re benefiting from the hardware without any special effort. In summary, any model or library that runs on NVIDIA GPUs will run on L40S – you don’t need a special version or anything. Just be mindful to match your CUDA/PyTorch version to the driver provided by the cloud image (most platforms keep these updated to recent versions given L40S is a new GPU).

Q: What models run best on L40S GPUs for inference?

A: Large generative models are the prime candidates – e.g., Transformer-based models (GPT-style LLMs, BERT-like encoders, transformer TTS, etc.), which benefit from the tensor core acceleration and 48GB memory. For instance, a 13B-parameter language model in 16-bit precision (~26GB memory) runs flawlessly on L40S and can serve many queries per second. Vision transformers and Stable Diffusion models (which often use 16GB+ when doing higher-res generation) also run exceptionally well on L40S, allowing you to generate bigger images or process multiple images in parallel. Embedding models (sentence transformers, CLIP, etc.) that might normally be memory-bound (when processing large batches or long texts/images) thrive on L40S – you can crank up batch sizes to maximize throughput. Also, multi-modal models (like Flamingo-style vision+language, or image captioning models) which tend to be large, will fit comfortably. Essentially, the ideal models for L40S inference are those that either: 1) Require lots of GPU memory (model size or working memory), 2) Can leverage lower precision (FP16/FP8) for speed – L40S will give a big speedup there, or 3) Need consistent, high throughput (where the L40S’s stronger sustained performance beats out consumer cards). Examples: ChatGPT-like assistants running open-source LLMs, stable diffusion web apps generating 4K images, real-time video analytics using large CNNs, and recommendation systems with big embedding tables (the GPU can even handle some indexing if needed). On the other hand, very small models (say a tiny CNN) won’t specifically benefit from an L40S versus any other GPU – but if you plan to host many such models or handle many requests concurrently, the L40S has the capacity to consolidate that workload onto one GPU.

Conclusion: The Nvidia L40S GPU unlocks high-end inference capabilities previously limited to the most expensive hardware, and a number of cloud platforms have made this GPU accessible. Among them, Runpod’s L40S offering stands out for its combination of low pricing, flexible deployment (serverless or full-control pods), container-friendly environment, and global availability . Whether you’re serving a large language model, an image generation service, or any demanding AI workload, leveraging L40S in the cloud can drastically improve performance per dollar. We encourage AI developers to explore Runpod’s L40S instances – check out Runpod’s L40S pricing page for the latest rates and visit the container setup guide to get started with custom environments. With the right platform, deploying large inference workloads on L40S can be both seamless and cost-efficient, freeing you to focus on building amazing AI applications.