How much does it cost to run NVIDIA T4 GPUs in 2025?

The NVIDIA Tesla T4 remains a workhorse GPU for AI inference in 2025, even six years after its launch. With 16 GB of GDDR6 memory and Turing architecture, it delivers 130 TOPS of INT8 performance while consuming just 70W, making it the most cost-effective option for small model inference workloads.

T4 specs & why density matters

The Tesla T4 shines on deployment flexibility vs raw performance. Its 70W power draw and single-slot form factor allow servers to pack multiple GPUs without specialized cooling. Built on the Turing architecture, the T4’s key specifications include:

• Architecture: Turing (TU104) with 2,560 CUDA cores
• Memory: 16 GB GDDR6 (256-bit bus)
• Bandwidth: 320 GB/s memory bandwidth
• Performance: 8.1 TFLOPS FP32, 130 TOPS INT8 with Tensor Cores
• Power: 70W max TDP (no external power needed)
• Special features: 320 Tensor Cores, dedicated NVENC/NVDEC engines

The T4 can decode up to 38 concurrent 1080p streams, making it ideal for video analytics pipelines. The 16 GB memory handles most production models when properly quantized, while the low power draw keeps operational costs minimal.

NVIDIA T4 cloud pricing breakdown

The T4 offers the lowest entry price for GPU compute across major cloud providers. Here’s the current pricing landscape as of August 2025:

Choosing the right T4 provider

Picking a cloud T4 provider is not just about picking the lowest per-hour price. Your decision should also be influenced by your expected consumption patterns.

• Variable workloads and inference: Modal’s autoscaling and per-second usage billing mean you 1) always get exactly the number of GPUs you need 2) pay only for what you use. This is the most cost-efficient option when you have variable compute demands.
• Budget experiments: GCP spot at $0.14/hr or Vast.ai marketplace for rock-bottom pricing, if you are willing to manage compute instances and need to prioritize cost over development speed.
• Stable, 24/7 workloads: If you can confidently forecast and commit to usage, GCP 3-year reserved instances at ~$0.16/hr are the cheapest option.

Most real-world AI workloads see variable traffic, making serverless the most economical choice. Even “production” workloads often idle 60%+ of the time overnight and on weekends.

T4 limitations and workarounds

Understanding the T4’s constraints helps avoid deployment bottlenecks:

Memory constraints

• Llama-2-7B: Fits comfortably with room for batching
• Llama-2-13B: Requires INT8 quantization
• Llama-2-70B: Not viable even with INT4

Product limitations

• No NVLink means PCIe-only multi-GPU communication (32 GB/s)
• No MIG support requires container-level multi-tenancy
• 1/4 the compute of L40S impacts generation speed on large models

If you need to support slightly larger models, or increase inference performance for existing models, check out A10Gs or L4s. For much larger models like state-of-the-art video generation models or LLMs, you’ll need L40S, A100, or H100 GPUs.

Real-world T4 deployment patterns

• Video processing: T4 can decode up to 38 concurrent 1080p streams, ideal for video transcoding pipelines and live video inference on security camera feeds
• Virtual desktops: T4 supports NVIDIA GRID/vWS profiles for VDI environments, accelerating CAD applications and creative workloads
• Edge computing: The 70W power draw and passive cooling enable deployment in edge locations with limited cooling capacity
• AI inference: T4 delivers up to 40x higher throughput than CPUs for inference tasks like image classification and recommendation systems. Ideal for smaller models.

Quick-start: deploy on T4 in under 5 minutes

If you’re interested in trying out a T4 today, Modal is the fastest way to get started. With our Python SDK, you can attach GPUs to serverless functions without having to touch a cloud console or manage any instances.

import modal

app = modal.App()

@app.function(gpu="T4")
def run_inference():
    # This will run on an T4 on Modal

When you call run_inference in this example, Modal automatically spins up T4s on-demand in less than a second, scaling from zero to hundreds of GPUs based on traffic. No quota requests, no instance management, no idle charges.

Direct purchase price

A T4 sells for just $845, making it a much more reasonable purchase than something like a H100, which would likely set you back $25,000. With cloud options hovering around ~$0.50/hr, break-even could happen in just 70 days. This might make sense for hobbyist use cases or small-scale experiments, but it is a no-go for any business that needs flexible capacity at scale and managed hardware.

T4 remains the inference value leader

The Tesla T4 delivers unbeatable value for mainstream inference in 2025. At $0.000164/second on Modal, teams can experiment for pennies and scale seamlessly. While it won’t run 70B parameter models, the T4 makes GPU acceleration accessible for workloads that don’t need cutting-edge hardware.

At Modal, we see T4s typically being used for inference of smaller fine-tunedLLMs (sub-3B parameters), previous-generation image generation models, reranking models, and embedding models. The T4’s combination of low cost and broad availability makes it the practical choice. As newer GPUs push into higher price brackets, the humble T4 continues to democratize AI deployment, especially when paired with serverless infrastructure that eliminates idle waste.