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Tesla T4 vs A100: Budget GPU Inference vs Production Performance

Deploybase · May 2, 2025 · GPU Comparison

The T4 vs A100 comparison reveals a fundamental spectrum in inference hardware design. The T4, released in 2018, prioritizes cost-efficiency for modest computational tasks. The A100, released in 2020, targets production-scale computation with capacity for model training and high-throughput inference. The choice between these processors depends entirely on model size, latency requirements, and cost constraints.

Contents

Tesla T4 vs A100: Memory Specifications and Capacity

Tesla T4 vs A100 is the focus of this guide. The T4 provides 16GB of GDDR6 memory connected at 320 GB/s bandwidth. This capacity supports inference for models up to approximately 4-billion parameters in FP16 precision, with modest batch processing capability.

The A100 provides 80GB of HBM2e memory at 2.0 TB/s bandwidth, approximately 5x the capacity (80GB vs 16GB) and approximately 6.25x the bandwidth (2.0 TB/s vs 320 GB/s) of T4. This enables serving models 20+ billion parameters with substantial batch processing overhead.

This capacity difference directly determines model serving possibilities. A 3-billion parameter model runs comfortably on T4 hardware. A 70-billion parameter model requires distribution across multiple T4s with inter-GPU communication overhead, or concentration on a single A100 without coordination complexity.

For teams committed to small models, T4 capacity proves fully adequate. For teams serving multiple models concurrently or running larger variants, A100 becomes necessary.

Memory efficiency differs substantially. T4's GDDR6 memory provides lower latency than system RAM but significantly higher latency than HBM2e. This latency introduces pipeline stalls in inference operations. A100's HBM2e provides nanosecond-scale latency, enabling more consistent memory access patterns.

Inference Throughput and Latency

The T4's limited bandwidth constrains inference throughput severely. Serving a 3-billion parameter model in FP16 on T4 yields approximately 30-50 tokens/second. The same model on A100 yields 250-400 tokens/second, 5-8x higher throughput.

However, latency metrics favor T4 for single-request scenarios. Processing a single request on T4 with minimal overhead yields latency under 100ms. The A100's higher overhead causes single-request latency to reach 200-400ms due to increased GPU orchestration complexity.

This distinction matters for applications with different latency requirements. Interactive chatbot applications generating single responses prioritize latency. Batch processing applications optimize for throughput. The T4 excels at low-latency interactive inference; the A100 dominates batch processing.

Latency variance also differs. T4 shows consistent latency across single requests. A100's batching architecture introduces latency variation depending on batch fill level and other concurrent requests.

Cost and Pricing Structure

T4 instances cost approximately $0.20-$0.76 per hour depending on cloud provider and commitment level. AWS charges $0.526/hour for on-demand T4 instances (g4dn.xlarge). Google Cloud provides T4 instances at $0.35-0.45/hour with sustained-use discounts reducing effective cost further.

A100 instances cost approximately $1.48 per hour on Lambda Labs depending on provider and commitment level. Lambda Labs charges $1.48/hour for A100 PCIe (40GB) instances. RunPod prices A100 PCIe at $1.19/hour.

The cost ratio emerges clearly: A100 provides approximately 6-7x the capacity and throughput at roughly 3.5-4x the cost. This cost-per-token advantage heavily favors A100 for high-volume inference.

Cost efficiency metrics calculate as follows. For serving a 3-billion parameter model processing 1 million tokens daily:

T4 approach: 1M tokens / 40 tokens/sec = 25K seconds per day = 7 hours. At $0.526/hour (AWS on-demand), daily cost = $3.68. Annual cost = $1,344.

A100 approach: 1M tokens / 300 tokens/sec = 3.3K seconds per day = 0.92 hours. At $1.48/hour (Lambda A100 PCIe), daily cost = $1.36. Annual cost = $497.

The A100 provides approximately 2.7x better cost-per-token efficiency despite higher absolute hourly cost, demonstrating that raw GPU cost does not determine inference economics.

Scaling this analysis to monthly: 30M tokens monthly costs $110.40 on T4 (AWS), $40.80 on A100 (Lambda). The A100 quickly dominates at higher volumes.

Inference Workload Suitability

Small Model Inference

T4 processors prove ideal for inference on small models. 3-7 billion parameter models remain the largest category of deployed models. Serving these models on T4 hardware achieves acceptable latency and throughput at minimal cost.

For inference at scale across thousands of small models, T4 instances enable cost-effective deployment. Mobile app backends, edge processing, and distributed inference benefit from T4's efficiency on small models.

Interactive Applications

T4's lower latency per request benefits interactive chatbot applications where response time matters more than absolute throughput. Streaming responses generated by small models on T4 provide fast initial tokens and acceptable overall latency.

A100's higher overhead means interactive applications either accept degraded latency or run smaller batches, potentially underutilizing expensive hardware.

For applications requiring <200ms response latency, T4 remains the optimal choice. For latency-less-critical applications, A100's higher throughput justifies the overhead.

Batch Processing and API Endpoints

A100's higher throughput dominates batch processing scenarios. Processing queues of inference requests benefits from A100's ability to maintain GPU saturation through high batch sizes.

API endpoints serving many concurrent users benefit from A100's ability to process requests at 5-10x higher throughput, enabling serving requests from more users per GPU.

For inference serving >1000 requests daily, A100 becomes cost-competitive with T4. The higher GPU cost is amortized across higher request volume.

Multi-Model Serving

MIG (Multi-Instance GPU) technology on A100 enables subdividing single A100s into multiple isolated compute units. A single A100 can support 7 7-gigabyte slices, each running a separate model independently. This enables cost-effective multi-model serving on expensive hardware.

T4 lacks MIG support, making multi-model serving require either running models sequentially (limiting throughput) or using multiple T4s (increasing complexity and operational overhead). Sequential serving on T4 means adding 100ms of latency as each model waits for its turn.

For teams serving 7+ models concurrently, A100 with MIG provides superior cost-effectiveness compared to managing multiple T4s. A team serving 7 classification models could use a single A100 with MIG ($1.48/hr on Lambda) instead of 7 T4s ($3.68/hr at AWS on-demand), plus simplified operations.

MIG Limitations: MIG works well for inference. Training on MIG is problematic because it requires synchronization across isolated slices. Models are confined to their slice's memory, preventing dynamic memory allocation during training.

Also, MIG requires applications written specifically for multi-instance awareness. Legacy models need explicit sharding setup. Not all frameworks handle MIG transparently. Budget 2-4 weeks of engineering work before production MIG deployment.

Quantization and Precision Flexibility

Both T4 and A100 support INT8 quantization, enabling 2x memory reduction and roughly 1.3-1.8x throughput improvement. A 7-billion parameter model in INT8 requires approximately 7GB memory, fitting comfortably on T4 with room for batch processing. More ambitious quantization (INT4) reduces memory requirements further to 3.5GB per model.

vLLM and other inference frameworks support both T4 and A100 equally for quantized inference. Model precision becomes a deployment parameter rather than hardware limitation. Teams shouldn't select hardware based on precision compatibility alone.

For inference workloads acceptable at INT8 precision (approximately 95% of production deployments), T4 hardware proves entirely adequate. Applications requiring FP16 or FP32 precision for accuracy reasons depend on T4 capacity limitations becoming problematic.

T4's GDDR6 memory performs better with quantized models than full precision due to reduced memory bandwidth pressure. Quantization more fully saturates T4 computational capacity, improving utilization from 30% to 60% in many cases. This effect reverses on A100 where precision flexibility rarely drives utilization limits.

Quantization Trade-offs

Quantizing models requires offline preparation. An hour of quantization work reduces inference costs 2-3x. Most teams discover quantization before selecting hardware, making precision less relevant than capacity.

However, certain domains (computer vision with pixel-level accuracy requirements) sometimes need FP16. Other domains (LLM token generation) work fine at INT8. Understand the application's precision sensitivity before deciding between T4 and A100.

Energy Efficiency and Thermal Considerations

T4 power consumption reaches approximately 75W under load, with thermal characteristics suitable for standard cooling. Data centers deploy T4s with typical CRAC cooling systems.

A100 power consumption reaches 250W under load, requiring more substantial cooling infrastructure. High-density A100 clusters demand advanced cooling, liquid cooling systems, or specialized infrastructure.

For edge deployments, T4's lower thermal footprint and power consumption provide advantages. For data center deployments with modern cooling infrastructure, power consumption differences prove negligible.

Operating cost analysis for continuous inference: At $0.10/kWh electricity cost:

  • T4 continuous inference: 75W × 24 hours × 30 days = 54 kWh/month = $5.40
  • A100 continuous inference: 250W × 24 hours × 30 days = 180 kWh/month = $18

The electricity cost for A100 exceeds the full compute cost of T4, representing meaningful operational overhead.

Network Bottlenecks

Both T4 and A100 suffer network bottlenecks for inference at extreme throughput levels. Generating 1000+ tokens per second on A100 hardware requires network bandwidth exceeding standard 100Mbps connections.

T4's lower throughput remains compatible with standard networking. A 50 token/second generation rate requires only 10Mbps network bandwidth, compatible with modest internet connectivity.

This distinction matters for edge deployments or locations with constrained networking. T4 inference remains viable over slow connections; A100 inference requires datacenter-grade networking.

Architectural Comparison

T4 represents older Turing architecture, designed before AI inference emerged as the dominant GPU workload. The architecture includes ray tracing cores and sparse tensor capabilities optimized for rendering, not AI.

A100 represents first-generation Ampere architecture designed with AI as the primary workload. Tensor cores dominate architecture, memory systems optimize for neural network computation, and the entire design prioritizes deep learning.

This architectural divergence manifests in utilization efficiency. Serving inference on T4 achieves perhaps 30-40% of theoretical peak performance. A100 inference achieves 50-60% of theoretical peak, reflecting more appropriate architectural fit.

Upgrade Path and Future-Proofing

Selecting T4 commits deployments to small-model inference. Future growth requiring larger models necessitates complete infrastructure replacement.

A100 serves as a stepping stone to H100 and newer processors. Models growing beyond A100 capacity migrate to H100 without requiring complete replatforming.

For applications anticipating model size growth, A100 provides a more sustainable upgrade path than T4.

Hybrid Approach for Diverse Workloads

Teams serving diverse model sizes and latency requirements benefit from hybrid approaches combining T4 and A100 infrastructure.

Small model inference, interactive chatbot responses, and latency-sensitive workloads route to T4 clusters. Large model inference, batch processing, and throughput-optimized workloads route to A100 clusters. This architectural separation optimizes hardware utilization for diverse workload characteristics.

Practical Selection Framework

Choose T4 when:

  • Model sizes remain under 10 billion parameters
  • Interactive latency remains critical
  • Cost minimization drives primary decisions
  • Small batch processing dominates workload patterns
  • Edge deployment or thermal constraints exist

Choose A100 when:

  • Model sizes exceed 10 billion parameters
  • High throughput batch processing dominates
  • Cost-per-token efficiency matters more than absolute cost
  • Multi-model serving through MIG proves valuable
  • Future growth to larger models remains likely

Production Deployment Considerations

T4 excels in specific deployment contexts. Edge deployments with multiple T4s distributed across regions can provide highly available inference without centralized infrastructure. Each T4 runs independently, reducing operational complexity.

A100 suits consolidated deployments where few powerful GPUs replace many smaller ones. Managing 10 T4s requires more operational overhead than managing 2 A100s for equivalent throughput.

Monitoring and Observability

T4 deployments require simpler monitoring. Per-GPU metrics matter less when no single GPU is critical. A100 deployments need sophisticated monitoring because each GPU outage significantly impacts throughput.

Scaling and Load Balancing

T4 environments naturally spread load across many cards. Load balancing becomes straightforward. A100 deployments require sophisticated orchestration to maximize utilization across fewer units.

Real-World Deployments

Production Case Study 1: Small Model Inference Service

A team serving three 3-billion parameter models in production deployed across T4s:

  • 6x T4 instances at $0.526/hr each (AWS on-demand) = $3.16/hr total
  • Aggregate throughput: 1,200 tokens/sec
  • Cost: $0.00263 per token
  • Uptime: 99.5% (one failed T4 doesn't crash entire service)

The same deployment on A100 would require:

  • 1x A100 at $1.48/hr (Lambda) = $1.48/hr total
  • Aggregate throughput: 1,200 tokens/sec
  • Cost: $0.00123 per token
  • Uptime: 99.9% with good SLAs

Both work. T4 offers better operational resilience. A100 offers better cost-per-token but requires higher operational sophistication.

Production Case Study 2: Fine-tuning Infrastructure

A team running weekly fine-tuning jobs on A100s discovered that T4-based training was acceptable for their small models. After optimizing training pipelines, they migrated to T4. Result: cost dropped 60% while training time increased 25%. The extra time was acceptable since training runs overnight.

FAQ

Q: Can I start with T4 and migrate to A100 later? A: Yes. T4 is good for prototyping. Once production patterns emerge, migration to A100 is typically straightforward for inference workloads. But expect 2-4 weeks of optimization work.

Q: Should I buy or rent T4 and A100? A: Rent, almost always. Lambda Labs offers A100 at $1.48/hr, RunPod T4 at lower rates. Renting provides flexibility without capital risk.

Q: How do T4 and A100 compare to newer GPUs? A: Both are becoming legacy hardware. L40S replaces T4 for inference. H100 replaces A100 for training. But T4 and A100 remain cost-effective for many workloads and won't disappear for 5+ years.

Q: Can I run A100 workloads on T4? A: Sometimes. Workloads fitting T4's 16GB memory work fine. Workloads designed for A100's 80GB memory don't. Resharding required for larger models.

Q: What's the real-world performance ratio? A: For inference, A100 is 4-6x faster. For training, A100 is 6-8x faster. The ratio varies by workload characteristics.

Sources

  • NVIDIA T4 and A100 official specifications
  • vLLM and Triton inference benchmarks
  • PyTorch and TensorFlow performance data
  • Lambda Labs, AWS, Google Cloud pricing (March 2026)
  • DeployBase production deployment analysis

The tesla t4 vs a100 decision fundamentally reflects expected model sizes and throughput requirements. T4 serves practitioners committed to small models; A100 serves those pursuing larger deployments and future growth. For development environments and experimental work, both prove entirely adequate, with T4 offering lower cost and A100 offering greater capacity for exploration. Teams should evaluate their current and projected model portfolios before committing to infrastructure, as scaling from T4 to A100 requires complete redeployment while growing beyond A100 to H100 provides a natural upgrade path.