T4 vs L4 for Small Models: Which GPU Is More Cost‑Efficient?

Why compare the T4 and L4 GPUs for small models?

Choosing a graphics processing unit (GPU) for deploying small or medium‑sized AI models isn’t trivial. A wrong decision can drain budgets or throttle performance. NVIDIA’s T4 and L4 GPUs sit in the mid‑range of data‑center accelerators and often appear in product catalogs as cost‑effective options for inference. But there is confusion about when each chip is appropriate, how their architectures differ, and whether upgrading to the L4 justifies the extra cost.

Clarifai, a leader in AI infrastructure and model deployment, frequently helps customers make this decision. By understanding the technical specifications, benchmarks, energy footprints, and pricing models behind both GPUs—and by leveraging Clarifai’s orchestration platform—teams can achieve better performance per dollar.

Quick digest: Which GPU is more cost‑efficient for small models?

Introduction: the race for efficient inference

AI’s meteoric rise has fueled an arms race in accelerator hardware. We often hear about H100s and A100s for training giant models, but for most startups and enterprises, small and mid‑sized models (1–14 billion parameters) dominate real‑world workloads. Here, cost efficiency and speed are paramount—production teams need to deliver fast responses without blowing out budgets or burning excess energy.

Two mid‑range GPUs—NVIDIA’s T4 (launched in 2018) and L4 (released in 2023)—are widely used for inference and small‑scale training. They share similar power envelopes (~70 W) yet differ significantly in architecture, memory capacity, and supported precisions. Upgrading to the L4 promises roughly 3× performance per watt and over 8× higher token throughput in some benchmarks. But is the investment justified when deploying small models? And how does one decide between on‑prem hardware, cloud providers, or serverless GPUs?

This deep dive is EEAT‑optimized (emphasizing experience, expertise, authority, and trust) and integrates Clarifai’s platform to help you navigate the decision. We’ll cover technical specs, real‑world benchmarks, pricing, energy efficiency, use cases, emerging technologies and a step‑by‑step decision framework. Expert insights accompany each section to provide context and guidance.

Technical specs & architecture comparison: How do T4 and L4 differ?

The first step in selecting a GPU is understanding its architecture and capabilities. At face value, the T4 and L4 look similar: both are single‑slot cards targeting inference workloads with roughly 70 W power draw. However, their internal designs and computational capabilities differ dramatically.

Overview of core specifications

Architectural improvements explained

Fifth‑generation tensor cores

The L4’s Ada Lovelace architecture introduces fifth‑generation tensor cores with FP8 precision and support for sparsity. These cores accelerate matrix operations central to transformers and diffusion models. In contrast, the T4’s Turing cores support FP16 and INT8 but lack FP8 support, resulting in lower throughput per watt.

Memory bandwidth and throughput

The L4 offers 300 GB/s memory bandwidth, while the T4’s bandwidth is lower (varying by manufacturer but roughly ~300 GB/s in aggregated tests). Combined with 24 GB memory, the L4 can handle longer sequences and micro‑batched requests without swapping to slower system memory.

Missing features: NVLink and MIG

One limitation of the L4 is that it does not support NVLink or Multi‑Instance GPU (MIG) partitioning. NVLink could allow multiple GPUs to share memory for larger models; MIG allows splitting a GPU into smaller independent instances. The T4 also lacks these features, but competitor GPUs like the A100 offer them. If you plan to scale beyond single‑GPU inference or need MIG, consider other GPUs like the L40S or H100 (available through Clarifai).

Expert insights

• Clarifai ML engineers note that architecture matters for rightsizing. Running a small language model (< 2 B parameters) on a high‑end GPU wastes resources, akin to “renting a stadium for a poker night”.
• Hardware specialists emphasize that power per watt is a more relevant metric than raw FLOPS. With 1.16 TFLOPS/W on T4 vs. 3.36 TFLOPS/W on L4, the L4 provides better energy efficiency, which translates to lower operating cost and less heat.
• Performance per dollar also depends on memory headroom. Models that exceed 16 GB VRAM may swap to CPU memory on T4, incurring steep latency penalties.

Real‑world performance & benchmarks: Which GPU delivers better throughput?

Raw specs are useful, but benchmarks on actual models reveal the true picture. Many open‑source experiments have compared T4 and L4 using popular language and vision models.

Language model inference: the Qwen study

A September 2025 Medium post benchmarked Qwen2.5‑Coder‑7B (a 7 billion‑parameter model) across the T4, L4 and H100. The T4 generated roughly 3.8 tokens per second, while the L4 achieved ~30.2 tokens per second using the FlashAttention 2 optimization, an 8× throughput increase. This dramatic gap makes the L4 more suitable for interactive applications like chatbots or coding assistants.

For 14 B models, the T4 often ran out of memory or experienced severe GPU swapping, whereas the L4’s 24 GB VRAM allowed the model to run with moderate throughput. The article concluded that the L4 is the “production sweet spot” for 7 B models and offers the best cost‑performance ratio among mid‑range GPUs.

Video analytics and computer vision

The T4 remains popular in video analytics because its INT8 performance and 16 GB memory can handle multiple video streams with high batch sizes. In contrast, the L4 excels at transformer‑based vision tasks (e.g., DETR, ViT) and multimodal inference, thanks to its improved tensor cores and memory. For example, if you build a multi‑modal summarization model (text plus images) on Clarifai, the L4 will handle complex computations more gracefully.

Clarifai’s internal benchmarks

Within Clarifai’s platform, users frequently benchmark models on multiple GPUs. A typical scenario: a startup running a 5 B‑parameter conversational model. On the T4, average latency hits 280 ms with 8 requests per second. On the L4, latency drops to 70 ms with the same concurrency. At scale, this equates to ~4× throughput and smoother user experience. Clarifai’s deployment dashboard records these metrics, enabling teams to decide whether the extra cost of the L4 justifies the performance gain.

Expert insights

• Inference experts caution that latency and throughput depend on batch size and context length. A T4 may match L4 throughput if the model is small and requests are micro‑batched appropriately. However, as context windows grow, the T4 quickly becomes a bottleneck.
• Researchers from the Qwen benchmarking study note that the H100 becomes necessary only when model size exceeds 30 B parameters. For 7–14 B models, the L4 often hits the sweet spot between performance and cost.
• Video analytics practitioners point out that T4’s maturity means robust software support and ecosystem—beneficial for legacy pipelines.

Cost analysis: purchasing vs. cloud usage

Hardware decisions rarely hinge solely on performance. Budget considerations, operating costs, and flexible usage patterns play major roles. Let’s break down the cost landscape for T4 and L4.

Upfront purchase prices

Market estimates suggest a T4 card costs around ₹1.8–2.2 lakh (≈$2,200–2,700), while an L4 card costs ₹2.6–3 lakh (≈$3,200–3,600). These prices fluctuate with supply and demand and exclude cooling, power supplies, and server chassis. Reselling older T4 units is common, but their depreciation may be higher given generational differences.

Cloud pricing: on‑demand vs. spot vs. serverless

Pricing on cloud providers varies widely. According to GetDeploying’s index, L4 hourly rates range from $0.28 (spot) to $3.40 (on‑demand), while T4 ranges $0.11 to $4.35. Factors include region, availability, spot interruptions, and reserved commitments.

Serverless GPU platforms like Modal and Clarifai offer additional flexibility. Modal rents L4 GPUs for about $0.45–$0.80 per hour, automatically scaling to zero when idle. Clarifai similarly auto‑hibernates idle GPUs, returning them to a resource pool to reduce idle cost.

Total cost of ownership (TCO)

When buying hardware, calculate TCO: purchase price + energy costs + cooling + maintenance + depreciation. A 70 W GPU running 24/7 consumes about 0.07 kWh × 24 hours × cost per kWh. If electricity costs ₹8/kWh (~$0.10), that’s roughly ₹13/day ($0.16) per GPU—not huge individually but significant at scale. Add cooling (30–40% overhead), and energy begins to rival hardware depreciation.

Cloud solutions shift these costs to the provider, but you pay a premium for convenience. The trade‑off is scalability—cloud GPUs scale to zero when unused, whereas on‑prem GPUs remain idle yet still consume energy.

Expert insights

• Clarifai’s FinOps team warns that idle GPUs can waste up to 32% of cloud spend. Right‑sizing and auto‑hibernation can reclaim this waste.
• Economists at the Stanford AI Index report that inference hardware costs decrease ~30% per year while energy efficiency improves ~40% annually. Budget planning should consider rapid price declines.
• CIOs recommend mixing reserved and spot instances for predictable workloads and bursting to serverless for unpredictable spikes.

Energy efficiency & sustainability: More than just dollars

With data centers consuming escalating amounts of power, energy efficiency has become a key factor in GPU selection. Besides lowering electricity bills, efficient GPUs help reduce carbon footprints and meet sustainability goals.

Performance per watt

As highlighted, L4 achieves around 3.36 TFLOPS per watt, nearly 3× more efficient than T4’s 1.16 TFLOPS/W. This translates into lower energy consumption per inference request. For high‑throughput services processing millions of requests per day, those savings accumulate quickly.

Understanding inference energy

Inference cost is a function of tokens generated, latency, and power draw. An NVIDIA blog notes that inference hardware costs are dropping thanks to improved model optimization and full‑stack solutions. However, energy efficiency remains critical: goodput (throughput at target latency) is now a preferred metric.

A 2025 research paper on multimodal LLM inference measured energy consumption on NVIDIA A100 GPUs and found that adding images increased energy usage by 3–25× and latency by 2–12×. The authors proposed input‑complexity‑aware batching and dynamic voltage and frequency scaling (DVFS) to cut energy without sacrificing throughput. While this study used A100 hardware, its principles apply to T4 and L4: batching and frequency adjustments can increase efficiency for multi‑modal tasks.

Low‑precision formats and FP4

Energy efficiency leaps will come from low‑precision computation. NVIDIA’s NVFP4 format (available on next‑gen Blackwell GPUs) promises 25–50× energy efficiency gains while keeping accuracy losses negligible. It reduces memory requirements by 8×, enabling massive language models to run on fewer chips. Although T4 and L4 don’t support FP4, understanding this emerging technology helps future‑proof decisions.

Consumer GPUs and sustainability

A peer‑reviewed study found that clusters built from RTX 4090 consumer GPUs deliver 62–78% of H100 throughput at about half the cost, offering a low‑carbon alternative when paired with renewable‑rich grids. This suggests that for latency‑tolerant batch workloads, mixing consumer GPUs with T4/L4 could cut costs and emissions. However, consider that consumer cards lack data‑center features like ECC memory and long‑term reliability.

Clarifai’s contribution to sustainability

Clarifai’s platform further minimizes energy waste. By scaling GPUs down to zero during idle periods and scheduling jobs across multiple cloud regions, Clarifai helps clients reduce carbon footprints. The platform can also prioritize GPUs in regions with cleaner energy or support on‑premises local runner deployments to leverage renewable energy sources.

Expert insights

• Energy experts argue that performance per watt is one of the most important metrics for inference. Even a small difference in TDP and efficiency can translate into thousands of dollars saved annually at scale.
• Research on multimodal models stresses the need for complexity‑aware batching, where requests with similar image/text ratios are grouped to optimize GPU energy usage.
• Sustainability advocates highlight that using renewable energy and re‑purposing consumer GPUs can reduce environmental impact while providing cost benefits.

Use cases & workload matching: Which workloads favor T4 or L4?

Not all models demand the same hardware. Matching the right GPU to the right workload ensures maximum efficiency.

When to choose the T4

• Models under 2 B parameters: If your model is relatively small (e.g., classification networks or < 2 B‑parameter language models), the T4 often suffices. The memory footprint stays well within 16 GB, and the T4 can deliver adequate throughput at a lower cost.
• Latency‑tolerant applications: Batch processing tasks like document classification, offline translation, or background summarization can tolerate higher latency. The T4 offers cost savings and draws less power.
• Video analytics with INT8: Many legacy computer vision pipelines operate at INT8 precision. The T4’s design still performs well for video stream analytics and object detection.
• Edge deployments: Because of its single‑slot form factor and efficient power consumption, T4 can be deployed in edge servers or micro data centers without requiring heavy cooling.

When to choose the L4

• 7–14 B‑parameter models: If your model size exceeds 7 B parameters or uses larger context windows, the 24 GB memory of the L4 prevents swapping and ensures consistent performance.
• Generative AI applications: Chatbots, code assistants, or multimodal models benefit from the L4’s support for FP8 precision and higher throughput.
• Concurrent real‑time inference: When hundreds or thousands of simultaneous requests hit your API, the L4’s better throughput ensures lower latency.
• Future‑proofing: If you anticipate scaling your model or adopting Mixture‑of‑Experts (MoE) architectures, the extra memory and newer cores offer headroom.

Other GPUs to consider

While this article focuses on T4 and L4, it’s helpful to mention alternatives:

• A10G: Similar to T4 with 24 GB memory and improved tensor cores; often recommended by Clarifai alongside T4 for < 2 B models.
• L40S: A dual‑slot GPU with 48 GB memory; ideal for visual AI, image generation, and rendering.
• RTX 4070 Super / 4090 (consumer): Suitable for development and experimentation; cost‑effective but lacking enterprise features.

Expert insights

• Clarifai’s deployment advisors emphasize matching GPU types to model size and workload concurrency. T4 or A10G for < 2 B models; L4 or L40S for 7–14 B models; H100 or B200 for > 30 B models.
• Ops engineers stress the importance of load testing under realistic concurrency. The T4 may appear adequate at low traffic but saturate when requests spike.
• Data scientists remind that model optimizations (quantization, pruning, knowledge distillation) can allow a T4 to handle larger models, but these techniques add complexity.

Clarifai’s GPU platform: Right‑sizing & orchestration for cost‑efficient AI

Choosing the right GPU is only part of the puzzle. Deployment orchestration—scheduling jobs, scaling up and down, and selecting the right instance type—determines ongoing efficiency. Clarifai’s compute platform plays a central role here.

How Clarifai simplifies GPU selection

Rather than manually provisioning and managing GPUs, you can deploy models through Clarifai’s console or API. During deployment, Clarifai’s orchestrator automatically chooses the right GPU based on model size, memory requirements, and expected traffic. For example, if you deploy a 1 B‑parameter model, Clarifai may select an AWS G4dn instance with T4 GPUs. When you scale to a 10 B model, the orchestrator may switch to AWS G6 or g2 instances with L4 GPUs.

Auto‑hibernation and cost savings

Idle GPUs are expensive. Clarifai implements auto‑hibernation: when your service experiences low traffic, the platform pauses the GPU instance, saving up to 40% of cloud spend. When traffic returns, the instance resumes. This feature is particularly impactful for startups with spiky workloads.

Benchmarking within Clarifai

Clarifai enables A/B testing of models across GPU types. You can deploy your model on both T4 and L4 instances simultaneously, funnel traffic to each and measure metrics such as latency, tokens per second, and cost per million tokens. After collecting data, simply adjust your deployment to the most cost‑efficient option.

Integration with major cloud providers and local runners

Clarifai supports deployment on AWS, Google Cloud, Microsoft Azure, and its own multi‑cloud infrastructure. For companies requiring data residency or on‑premises deployments, Clarifai’s Local Runner allows running your model on local GPUs—including T4 or L4—while benefiting from Clarifai’s API interface and management.

If you’re unsure which GPU fits your workload, sign up for Clarifai’s free tier. Within minutes you can upload your model, select an instance type, and benchmark performance across T4 and L4 GPUs. The platform’s pay‑as‑you‑grow pricing ensures you only pay for what you use.

Expert insights

• Clarifai’s founders note that rightsizing is often overlooked. Many teams overspend on top‑tier GPUs when a mid‑range card like T4 or L4 suffices. Proper benchmarking can reduce costs significantly.
  
  
• MLOps professionals highlight that orchestration—automatic scaling, job scheduling, and dynamic instance selection—can yield bigger savings than simply switching hardware.
  
  
• Users appreciate Clarifai’s simple UI and API, which reduce the complexity of provisioning GPUs across different cloud providers.
  
  

Future‑proofing: emerging GPUs & technologies beyond T4/L4

Technology evolves quickly, and decisions today must consider tomorrow’s landscape. Here’s a glance at emerging GPUs and innovations that could reshape cost efficiency.

Blackwell and FP4: the next generation

NVIDIA’s Blackwell B200 (released March 2024) and forthcoming B300 represent massive leaps over Hopper and Ada architectures. The B200 packs 192 GB HBM3e memory, 8 TB/s bandwidth, and delivers 2,250 TFLOPS FP16 and 20 PFLOPS FP4. Its NVFP4 format offers 25–50× energy efficiency gains while maintaining similar accuracy. While B200 pricing ranges from $2.79 to $16/hour on cloud marketplaces—far above T4 or L4—it hints at a future where low‑precision computation dramatically reduces operational costs.

Metrics like goodput and energy per token

Modern inference planning involves metrics beyond raw throughput. Goodput, defined as throughput achieved while meeting latency targets, helps balance performance and user experience. Similarly, energy per token measures the joules consumed to generate each token. Expect these metrics to become standard in cost‑efficiency analyses.

Dynamic voltage & frequency scaling and input‑aware scheduling

Energy studies on multimodal inference highlight techniques like DVFS—down‑clocking GPU frequencies during low‑complexity tasks to save energy—and input‑complexity‑aware batching, where requests with similar complexity are processed together. Future GPU orchestration platforms (including Clarifai) may incorporate such controls automatically.

Heterogeneous and decentralized compute

A growing trend is mixing consumer GPUs with enterprise GPUs to reduce costs. The peer‑reviewed study showing RTX 4090 clusters deliver near‑H100 performance at half the cost validates the hybrid infrastructure model. Decentralized GPU networks like those championed by IO.net aim to democratize compute and reduce costs through peer‑to‑peer sharing.

AMD and other competitors

While NVIDIA dominates the AI accelerator space, other players like AMD’s MI300X offer 192 GB memory and competitive performance at potentially lower cost. Keeping tabs on alternative architectures may provide further cost‑efficient options.

Expert insights

• AI hardware analysts predict that Blackwell’s FP4 format will eventually trickle down to mid‑range GPUs, providing large energy savings.
• MLOps thought leaders emphasize that adopting hybrid or decentralized compute frameworks can mitigate supply shortages and reduce carbon footprints.
• Economists advise planning for hardware depreciation and leapfrog upgrades; investing in flexible platforms like Clarifai ensures smooth transitions when new GPUs arrive.

How to choose between T4 and L4: A step‑by‑step guide

Selecting a GPU requires balancing performance, cost, and growth plans. Use this structured approach to make an informed decision.

Step 1: Profile your model and workload

• Model parameters & memory footprint: Assess model size (parameters), context length, and expected batch size. If memory requirements exceed 16 GB (e.g., 7 B models with long context windows), the T4 may cause swapping.
• Latency sensitivity: Determine acceptable latency. Chatbots and interactive applications require low latency (≤100 ms), favoring L4. Batch tasks can tolerate higher latency, making T4 viable.
• Concurrency: Estimate queries per second. High concurrency favors the higher throughput of the L4.

Step 2: Benchmark on both GPUs

Run your model on T4 and L4 instances—Clarifai allows this via a few API calls. Measure tokens per second, latency at your target concurrency, and memory utilization. Also track energy consumption if running on-prem or if your cloud platform provides power metrics.

Step 3: Compare costs

Use data from GetDeploying and cloud provider pricing to calculate hourly costs: multiply your expected GPU time by hourly rate. Evaluate spot vs. reserved vs. serverless options. Consider energy cost and cooling if on-prem.

Step 4: Factor in scalability and future needs

If you plan to scale to larger models (≥14 B parameters) or require FP8 precision, lean toward the L4 or even L40S. If your workloads are stable and small, the T4 offers a cheaper baseline. Also consider new GPUs arriving soon; investing in flexible orchestration platforms reduces migration friction.

Step 5: Make your decision and monitor

After evaluating performance and cost, choose the GPU that meets current needs with headroom for growth. Deploy via Clarifai to monitor usage and set alerts for performance or cost anomalies. Regularly re‑benchmark as your model evolves and as new hardware becomes available.

Expert insights

• FinOps specialists emphasize the importance of benchmarks before purchase. Too often, teams purchase expensive hardware without testing real workloads.
• Engineers advise starting with spot or serverless instances on Clarifai to gather data before committing to reservations or hardware purchases.
• Startup founders highlight that choosing a slightly more expensive GPU like the L4 can be beneficial if it shortens inference latency, leading to better user satisfaction and retention.

Frequently asked questions (FAQs)

Can I train small models on T4 or L4?

Yes. Both GPUs support mixed‑precision training. However, the L4’s 24 GB memory and higher FP32/FP16 throughput make it more comfortable for fine‑tuning 7–14 B models. The T4 can handle lightweight training (< 2 B parameters) but may be slower.

How does Clarifai simplify GPU management?

Clarifai removes the burden of infrastructure by automatically selecting GPU types, scaling capacity, and hibernating idle instances. You can deploy a model via the GUI or API and let the platform handle the rest. Clarifai also integrates with major clouds and offers a local runner for on‑prem deployments.

Can I run multimodal models on the T4?

Multimodal models (combining text and images) demand more memory and compute. While T4 can handle simple multimodal inference, the L4 or L40S is recommended for efficient multimodal processing, as research shows that adding images significantly increases energy and latency.

When are consumer GPUs like RTX 4090 a good choice?

Consumer GPUs can be cost‑effective for development, experimentation, or latency‑tolerant batch tasks. A peer‑reviewed study showed RTX 4090 clusters deliver 62–78% of H100 throughput at half the cost. However, they lack enterprise reliability features and may not be suitable for mission‑critical services.

How will FP4 and Blackwell GPUs affect cost efficiency?

FP4 and Blackwell GPUs promise dramatic improvements in energy efficiency and memory usage, enabling massive models to run on fewer chips. While adoption is limited today, expect these technologies to trickle down to mid‑range GPUs, reducing operating costs further.

Conclusion: Key takeaways

• The L4 significantly outperforms the T4 in throughput, memory capacity, and energy efficiency, making it ideal for 7–14 B‑parameter models, generative AI, and concurrent inference workloads.
• The T4 remains a budget‑friendly choice for models under 2 B parameters, latency‑tolerant tasks, and video analytics pipelines.
• Clarifai’s compute platform simplifies GPU selection, automatically matches models to appropriate GPUs, and reduces idle costs with auto‑hibernation. Benchmarking your model on both GPUs within Clarifai is the best way to determine cost efficiency.
• Energy efficiency and sustainability are increasingly important. L4 delivers nearly 3× better performance per watt than T4, and emerging technologies like FP4 promise even bigger leaps.
• New GPUs (B200, B300) and hybrid infrastructures (mixing consumer and enterprise GPUs) will continue to reshape the cost‑performance landscape. Planning for flexibility and leveraging platform‑level orchestration ensures you’re ready for the future.
  
  

Ready to find your perfect GPU match? Sign up for Clarifai’s free tier and start benchmarking your models on T4 and L4 today. In just a few clicks you’ll know exactly which GPU offers the best balance of speed, cost, and sustainability for your AI projects.