Nvidia L40 48GB vs A100 40GB: better value for LLM inference?

On a Monday in late May, we were wrestling with a new 70B parameter model that refused to fit comfortably into our standard A100 40GB instances without aggressive quantization. The L40 48GB, a card we’d mostly overlooked for serious inference work, suddenly looked like a viable — and potentially cheaper — alternative. We dug into the vendor pricing pages and spec sheets, because the marketing copy for these things rarely tells the full story.

L40 vs A100: key architectural differences for inference

The Nvidia L40 (Ada Lovelace architecture) and the A100 (Ampere architecture) are both workhorse GPUs, but they’re built for slightly different generations of problems. For LLM inference, the key differences boil down to memory, core types, and power efficiency.

First, VRAM. The L40 we’re looking at ships with a healthy 48GB of GDDR6 memory. The A100 in question has 40GB of HBM2. That 8GB difference might seem minor, but it’s often the margin between fitting a larger model (or a less aggressively quantized one) and not. On the memory bandwidth front, the A100 40GB typically offers ~1.5 TB/s, while the L40 sits closer to ~864 GB/s. This is a significant difference that can impact raw token throughput, especially when serving many concurrent requests or using larger batch sizes.

Next, the compute cores. The A100 features third-generation Tensor Cores, purpose-built for AI workloads, offering impressive FP16 and TF32 performance. The L40, being a newer Ada Lovelace generation card, uses fourth-generation Tensor Cores. While the L40 has fewer overall Tensor Cores than a comparable A100, its newer architecture can sometimes compensate through improved efficiency and faster processing per core. The L40 also generally has a lower Thermal Design Power (TDP) of around 300W, compared to the A100’s ~400W, which translates directly to lower electricity costs for bare-metal deployments or slightly cheaper hourly rates in the cloud due to reduced cooling requirements.

Hourly pricing comparison for L40 48GB and A100 40GB

When it comes to cloud rentals, the hourly rate is usually the first number we check. For LLM inference, where jobs can run for hours or days, even small differences add up quickly. We pulled the latest on-demand hourly pricing from a couple of providers in the weeks leading up to June 2026. Keep in mind, these rates are for a single GPU and can fluctuate.

Provider	GPU	On-demand Hourly Rate
Runpod	Nvidia L40 48GB	$0.99
Runpod	Nvidia A100 40GB	$1.19
Lambda Labs	Nvidia L40 48GB	$0.95
Lambda Labs	Nvidia A100 40GB	$1.15

As you can see, the Nvidia L40 48GB is consistently cheaper per hour than the A100 40GB, often by about $0.20. This aligns with the L40’s positioning as a more cost-effective inference-focused card. For a deeper dive into A100 pricing across more providers, you can always check our full [/blog/a100-cloud-pricing-comparison/](A100 Cloud Pricing: Runpod, Vultr, Lambda, Vast.ai Battle for Your DL Dollars).

According to Runpod’s pricing page, their on-demand hourly pricing for Nvidia L40 48GB is $0.99, while for Nvidia A100 40GB it’s $1.19. [https://www.runpod.io/gpu-prices] Similarly, Lambda Labs’ pricing page indicates an on-demand hourly rate of $0.95 for Nvidia L40 48GB and $1.15 for Nvidia A100 40GB. [https://lambdalabs.com/service/gpu-cloud/pricing]

These numbers are vendor-published as of mid-June 2026. Spot instance pricing, of course, can be significantly lower but comes with its own set of preemption risks and setup headaches. For consistent, predictable inference, on-demand is often the safer, if more expensive, bet.

Expected performance for common LLM inference workloads

Translating raw specs into real-world LLM inference performance involves a few moving parts: the model size, quantization level, batch size, and the inference engine (like vLLM or TGI). The L40’s 48GB VRAM is its most immediate advantage over the 40GB A100. This extra memory allows it to run larger models or use less aggressive quantization methods, which can improve output quality, or simply enable models that wouldn’t otherwise fit. For example, a Llama 3 70B model in a 4-bit quantized format might just squeeze into an A100 40GB, but the L40 offers more headroom for things like larger context windows or dynamic batching without hitting OOM errors.

When it comes to pure token throughput, the A100’s higher memory bandwidth often gives it an edge, especially with smaller models or highly optimized inference pipelines. We’ve seen A100s consistently deliver high tokens/second on established benchmarks for models like Llama 3 8B. However, the L40’s newer architecture and fourth-gen Tensor Cores can close this gap, particularly with newer inference frameworks that better leverage its capabilities. Modern frameworks like vLLM are constantly being optimized for the latest hardware, which can sometimes allow newer, lower-spec cards to outperform older, higher-spec ones in specific scenarios. For instance, in our tests for [/blog/modal-vs-replicate-llama3-inference/](Modal vs Replicate for Llama 3 Inference: A Cost and Latency Showdown), the software stack played as big a role as the underlying GPU.

Another subtle factor is the efficiency of memory access. While the A100 has higher bandwidth, how efficiently the LLM’s weights are accessed and processed can vary. We’re also not factoring in the impact of [/blog/gpu-instance-storage-nvme-pricing-comparison/](GPU Instance Storage), which can be a bottleneck for model loading, but typically has less impact once the model is in VRAM for inference.

Where L40 48GB shines for LLM inference

The L40 48GB isn’t just a cheaper alternative; it has specific strengths that make it shine for certain LLM inference workloads:

1. Larger Models and Context Windows: The most obvious win is the 48GB of VRAM. For models that are just too large for a 40GB A100, or for those requiring very long context windows, the L40 becomes a necessity rather than an option. This is particularly relevant as LLM sizes and context needs continue to grow. Running a Llama 3 70B in FP16, for example, is more feasible on an L40 without resorting to extreme quantization.
2. Cost-Efficiency: As shown in the pricing table, the L40 is generally cheaper to rent per hour. When you combine this with its lower power consumption, the operational cost for continuous inference can be significantly lower over time. For teams running many concurrent inference jobs or maintaining always-on API endpoints, these savings add up.
3. Newer Architecture Benefits: The Ada Lovelace architecture brings improvements beyond just VRAM. Its fourth-generation Tensor Cores and other architectural refinements can offer better efficiency for certain operations, especially when using the latest optimized inference libraries. While not always translating to raw speed dominance over A100, it often means better performance per watt or per dollar.
4. Gaming/Graphics Workloads: While our focus is LLM inference, it’s worth noting that the L40 has strong roots in professional visualization and graphics. If your inference stack occasionally needs to render complex visual outputs or run graphics-intensive pre/post-processing, the L40’s versatility can be a bonus.

Where A100 40GB still holds its ground

Despite the L40’s advantages, the A100 40GB isn’t ready for retirement, particularly in certain inference scenarios:

1. Raw Throughput on Smaller Models/Batch Sizes: For smaller LLMs or scenarios where you need to maximize tokens per second on limited batch sizes, the A100’s higher memory bandwidth can still provide a noticeable edge. Highly optimized inference engines might be able to extract more raw speed from the A100 for these specific use cases.
2. Mature Ecosystem and Optimization: The A100 has been the industry standard for AI for years. This means a vast amount of existing code, drivers, and frameworks are heavily optimized for its Ampere architecture. If you’re running a legacy inference stack or relying on highly specialized libraries, the A100 might offer more stable and predictable performance without needing extensive re-optimization.
3. Training Workloads (and hybrid inference): While this post focuses on inference, the A100’s robust training capabilities mean that if you’re doing occasional fine-tuning or a hybrid training/inference setup on the same hardware, the A100 remains extremely capable. Our exploration of [/blog/dual-a100-vs-h100-llm-training/](Dual A100 40GB vs H100 80GB) highlights its enduring power for training.
4. Availability and Familiarity: A100s are widely available across almost all cloud GPU providers, and many ML engineers are deeply familiar with deploying and optimizing workloads on them. Sometimes, the devil you know is preferable to a slightly cheaper, less familiar option, especially in production environments where stability is paramount.

Which GPU offers better value for your LLM inference budget?

After sifting through the specs and pricing pages, our verdict is clear: for most new LLM inference deployments, the Nvidia L40 48GB offers a more compelling value proposition, especially if you’re working with larger models or prioritize cost-efficiency. The extra 8GB of VRAM is a game-changer for many contemporary LLMs, letting you run larger context windows or less quantized models without jumping to the much more expensive H100s.

At roughly $0.20 per hour cheaper than the A100 40GB, the L40 makes a strong case for itself. If you run an inference server for 720 hours a month, that’s a savings of around $144 per GPU. When you factor in the L40’s lower power consumption, the total cost of ownership looks even better.

However, if your specific workload is deeply optimized for Ampere’s architecture, or if you’re pushing for the absolute maximum token throughput on smaller models with a high batch size, the A100 40GB might still deliver marginally better raw performance. But for every other scenario, particularly when model size and cost are primary concerns, the L40’s combination of more VRAM and a lower hourly rate is difficult to beat. We’d recommend starting with an L40 for new inference projects and only considering the A100 if you hit specific performance bottlenecks that the L40 demonstrably can’t overcome. If you want to try the same workload yourself, Runpod offers L40 instances at competitive rates.