L4 vs T4: Specs, Benchmarks & Cloud Pricing Compared

Contents

• L4 vs T4: Overview
• Quick Comparison
• Hardware Specifications Deep Dive
• Performance Benchmarks
• Cloud Pricing Comparison
• Power Consumption and Efficiency
• Use Cases: When to Choose Each
• Specific Use Case Analysis
• Video Encoding Performance
• Real-World Deployment Scenarios
• Inference Speed Deep Dive: Model-by-Model
• Memory Bandwidth Analysis
• Multi-GPU Scaling Considerations
• Thermal and Power Considerations for Data Centers
• Real-World Comparisons from Production Systems
• FAQ
• Related Resources
• Sources

L4 vs T4: Overview

L4 (Ada, 2023) vs T4 (Turing, 2018). L4 inference-focused, T4 general-purpose. L4 wins 3-5x at same price ($0.44/hr RunPod). L4 is the move for 2026.

T4 still viable for batch, latency-insensitive work. Widely available.

This compares specs, benchmarks, pricing, use cases.

Quick Comparison

Hardware Specifications Deep Dive

NVIDIA L4:

Memory: 24GB GDDR6 (vs T4's 16GB). Critical for larger models and batch inference.

Memory interface: 192-bit (same as T4) enables efficient 24GB despite smaller physical memory modules.

Memory bandwidth: 300 GB/s. Supports higher tensor throughput. Matters for memory-bound inference operations.

Tensor cores: Specialized hardware for matrix operations. L4 prioritizes inference precision (FP8, TF32) over raw FP32 throughput.

Sparsity: L4 supports structured sparsity on its fourth-generation Tensor Cores, delivering up to 485 INT8 TOPS with sparsity. T4's sparsity acceleration (50% reduction when 50% of weights zero) is an earlier implementation.

Encoding: Dedicated 128 video engines (vs T4's less capable encoder). AV1 support (T4 lacks this). Critical for video transcoding workloads.

Power efficiency: 72W TDP, same as T4 but much greater throughput. Better watts-per-inference metric.

NVIDIA Tesla T4:

Memory: 16GB GDDR6. Sufficient for 7-8B models, insufficient for 13B models requiring full precision.

Memory interface: 192-bit, limiting bandwidth despite adequate for 2018-era compute.

Tensor cores: Smaller, designed for general compute rather than inference specialization.

Sparsity: Structured sparsity (50%) available, rarely used in practice. Most models don't have 50% zero weights post-quantization.

Encoding: Basic H.264 and H.265 support. Fewer encoding engines than L4.

Power efficiency: 70W TDP. Good for era of release, now outdated.

Architecture Differences:

T4 is a general-purpose GPU designed for mixed compute. L4 is inference-focused: tensor cores optimized for small batches and low latency.

T4 excels at training and simulation. L4 excels at inference and transcoding.

Performance Benchmarks

LLM Inference Benchmarks (Tokens per Second):

Test setup: Batch size 1 (single request), FP16 precision, streaming response.

7B Model (Llama 2 7B):

L4:

• Prefill (processing input prompt): 200-250 tokens/sec
• Decode (generating response): 35-45 tokens/sec
• End-user experience: responsive chat (typical 3-5 sec for 150-token response)

T4:

• Prefill: 40-60 tokens/sec
• Decode: 8-12 tokens/sec
• End-user experience: 15-20 sec for 150-token response

L4 advantage: 4-5x faster decode (the critical metric for chat).

13B Model (Mistral 13B):

L4:

• Prefill: 150-200 tokens/sec
• Decode: 25-35 tokens/sec
• Wall-clock time: 6-10 seconds for 150-token response

T4:

• Requires quantization (16GB VRAM insufficient for FP16)
• Prefill (Q4 quantization): 25-35 tokens/sec
• Decode: 5-7 tokens/sec
• Wall-clock time: 25-40 seconds for 150-token response

L4 advantage: 3-5x faster, plus native FP16 support (no quantization needed).

Image Generation (SDXL Stable Diffusion):

L4:

• SDXL generation (1024x1024): 3-4 seconds
• Quality: Full precision (FP32)

T4:

• SDXL generation (1024x1024): 12-15 seconds with quantization
• Quality: Reduced precision

L4 advantage: 3-4x faster, no quality loss from quantization.

Cloud Pricing Comparison

RunPod Pricing (March 2026):

L4: $0.44/hr (on-demand), $0.25/hr (spot) T4: $0.20-0.30/hr (on-demand), $0.10-0.15/hr (spot)

Raw hourly cost: T4 cheaper by 30-50%. However, L4's 4x speedup means 1 hour of L4 work ≈ 4 hours of T4 work.

Cost per Inference (Fixed Task):

Task: Generate 150-token response using 7B model, 100,000 requests/month.

L4 approach:

• 100K requests × 5 sec/request = 139 hours/month
• Cost: 139 hours × $0.44 = $61/month

T4 approach:

• 100K requests × 20 sec/request = 556 hours/month
• Cost: 556 hours × $0.20 = $111/month

L4 is cheaper despite higher hourly rate. L4 saves $50/month on this workload.

Cost per Inference (High-Volume Batch):

Task: Process 1 million requests/month, batch size 8 (less latency-sensitive).

L4 approach:

• Processing throughput: 8 requests × 25 tokens/sec (decode) = 200 tokens/sec
• 1M requests × 150 tokens = 150M tokens total
• Time: 150M / 200 = 750,000 seconds = 208 hours/month
• Cost: 208 × $0.44 = $91/month

T4 approach:

• Processing throughput: 8 requests × 6 tokens/sec (decode, quantized) = 48 tokens/sec
• Time: 150M / 48 = 3.125M seconds = 868 hours/month
• Cost: 868 × $0.20 = $174/month

L4 saves $83/month even with heavy batch processing.

Lambda GPU Cloud Pricing:

Lambda T4: $0.35/hr Lambda L4: Not yet widely available (emerging in 2026)

AWS SageMaker ML Instance Pricing:

ml.p3.2xlarge (T4): $1.06/hr on-demand ml.g10x.xlarge (L4): $1.11/hr on-demand

AWS pricing compressed: T4 and L4 nearly equivalent on AWS, favoring L4 due to performance.

Power Consumption and Efficiency

Thermal Design Power (TDP):

L4: 72W T4: 70W

Nearly identical, surprising given 3-5x performance difference. L4 is more efficient per watt of inference.

Watts per Inference (Energy Efficiency):

Measured in joules per token (lower is better):

L4: 0.08 J/token (72W ÷ 900 tokens/sec typical workload) T4: 0.35 J/token (70W ÷ 200 tokens/sec typical workload)

L4 is 4.4x more energy-efficient. Important for data center operations and sustainability.

Total Cost of Ownership (TCO) over 3 years:

Hardware cost (amortized): L4 ~$2,500, T4 ~$1,500 (if owned)

Electricity (assuming 50% utilization):

• L4: 72W × 0.5 × 8760 hours/year × $0.12/kWh = $36/year
• T4: 70W × 0.5 × 8760 hours/year × $0.12/kWh = $36/year

Operational costs minimal. Choose based on inference throughput requirements.

Use Cases: When to Choose Each

Choose L4 if:

• Inference latency matters (chat, real-time APIs)
• Running 13B+ models (native FP16 needed)
• VRAM matters (24GB vs 16GB)
• Want to avoid quantization (quality sensitive)
• Image generation or transcoding
• Processing throughput more important than raw cost
• Modern infrastructure investment desired

Choose T4 if:

• Latency insensitive (batch processing, overnight jobs)
• Budget extremely constrained
• Existing infrastructure has T4 investments
• Running small models (3-7B) only
• Workload compatible with quantization
• Cloud provider only offers T4 (legacy constraint)

Specific Use Case Analysis

Production Chat API:

L4 required. T4 latency (15-20 sec per response) makes for poor user experience. L4 at 4-6 seconds acceptable. User retention drops significantly above 10-second latency.

Cost: L4 ($0.44/hr) more expensive hourly, but fewer total GPUs needed due to throughput. Typical setup: 2x L4 vs 8x T4 for same user capacity.

Batch Document Processing:

T4 acceptable. Processing 100,000 documents overnight: T4 bottleneck is latency, not throughput. Save $100+/month using T4.

Example: 100K documents × 10 sec/doc ÷ 1000 docs/hr = 100 hours on T4. Cost: $20. On L4: 25 hours, cost: $11. T4 worth it here.

Video Transcoding:

L4 strongly preferred. Built-in AV1 encoder, 128 video engines. T4 lacks AV1. For transcoding 1000 videos/month to multiple formats, L4 saves weeks of processing time.

Processing 1 million minutes of video:

• L4: 500 hours (efficient encoding) = $220
• T4: 4,000 hours (slower, no AV1) = $800

L4 saves $580/month.

Fine-tuning:

T4 insufficient (16GB VRAM barely fits 7B model). L4 required for 13B. For LoRA fine-tuning, both work. For full fine-tuning, L4 advantage significant.

Training 7B model on 10K examples:

• L4: 10-15 hours compute = $4.40-6.60 + labor
• T4: 30-50 hours compute = $6-10 + labor

L4 marginally faster but both viable.

Classification and Embedding:

Both adequate. Small batch sizes, short prompts. Either works. T4 cost advantage (30%) means $10-20/month difference for small workloads.

Embedding 1M documents (1000 tokens each):

L4: 600 hours = $264 T4: 1,500 hours = $300

T4 nearly equivalent, cost advantage minimal.

Video Encoding Performance

AV1 Encoding (L4 Only):

L4 supports dedicated AV1 hardware. T4 lacks this entirely.

NVIDIA T4: H.264, H.265 only

NVIDIA L4: H.264, H.265, AV1

1920x1080 video encoding (8-second clip):

L4 with AV1: 2-3 seconds encoding T4 with H.265: 8-12 seconds encoding L4 with H.265: 1-2 seconds encoding

For video platforms transcoding to multiple codecs, L4 AV1 support justifies migration.

Real-World Deployment Scenarios

Scenario 1: Customer Support Chatbot

Requirements: 1,000 concurrent users, 5-second response time target

T4 approach:

• Need 50x T4 GPUs (each serves 20 concurrent users at 20 sec latency)
• Monthly: 50 GPUs × $0.20/hr × 730 hours = $7,300

L4 approach:

• Need 15x L4 GPUs (each serves 67 concurrent users at 5 sec latency)
• Monthly: 15 GPUs × $0.44/hr × 730 hours = $4,818

L4 saves $2,482/month ($30k/year) despite higher per-GPU cost.

Scenario 2: Daily Batch Inference

Requirements: Process 10 million documents daily (1,000 tokens each, 500-token output)

T4 approach:

• Throughput: 50 tokens/sec per GPU
• Time: 10M × 1500 tokens / 50 tokens/sec = 300,000 seconds = 83 hours/day
• Need continuous 3-4x GPUs for daily completion
• Monthly: 4 GPUs × $0.20 × 730 = $584

L4 approach:

• Throughput: 200 tokens/sec per GPU
• Time: 10M × 1500 / 200 = 75,000 seconds = 21 hours/day
• Need continuous 1x GPU for daily completion
• Monthly: 1 GPU × $0.44 × 730 = $321

L4 cheaper even with higher hourly rate. Faster completion also valuable.

Inference Speed Deep Dive: Model-by-Model

Phi-2 (2.7B Model):

L4:

• Prefill: 400+ tokens/sec
• Decode: 100+ tokens/sec
• Total for 150-token response: 2-3 seconds

T4:

• Prefill: 80-100 tokens/sec
• Decode: 20-25 tokens/sec
• Total: 8-10 seconds

L4 advantage: 3-4x

Code Llama 34B (Quantized Q4):

L4:

• Can't fit (34B model needs 17GB at Q4, L4 has 24GB but leaves little headroom)
• Possible with int8 quantization

T4:

• Can't fit (doesn't have 24GB)
• Can't run this model

L4 wins by supporting larger models.

Yi-34B (Quantized Q4):

L4:

• Prefill: 80-100 tokens/sec
• Decode: 12-15 tokens/sec
• 8-bit variant: 15-20 tokens/sec

T4:

• Can't fit in 16GB VRAM
• Would require aggressive quantization with quality loss

L4 advantage: Supports the model natively.

Memory Bandwidth Analysis

Memory bandwidth (GB/s) determines inference speed for memory-bound operations.

L4: 300 GB/s T4: 320 GB/s (32 GB/s per module × 10 modules)

Bandwidth advantage: 0.94x (T4 has slightly higher bandwidth)

However, this translates to only 10-15% performance difference in practice for typical models. The tensor architecture matters more than bandwidth.

L4's tensor specialization for inference (TF32, sparsity in software) provides 3-5x practical speedup beyond bandwidth advantage.

Multi-GPU Scaling Considerations

Scaling Inference Clusters:

L4: Two 24GB L4s ($0.88/hr) can serve 1,000+ concurrent users with 5-second latency. Horizontal scaling straightforward.

T4: Four 16GB T4s ($0.80-1.20/hr) needed to match throughput. More GPUs increase operational complexity.

Cost analysis at 10,000 concurrent users:

• 10x L4 cluster: $4.40/hr = $3,160/month
• 30x T4 cluster: $6-9/hr = $4,320-6,480/month

L4 cheaper at scale despite higher per-unit cost.

Multi-GPU Batching:

L4 batching: Batch size 8-16 per GPU. Two GPUs handle 16-32 concurrent requests.

T4 batching: Batch size 2-4 per GPU. Two GPUs handle 4-8 concurrent requests.

Batching efficiency matters for high-concurrency systems. L4's larger batches reduce response latency variance (predictability).

Thermal and Power Considerations for Data Centers

Data Center Total Cost of Ownership (TCO):

Hardware purchase (amortized over 5 years): L4 ~$2,500, T4 ~$1,500

Electricity (assuming continuous operation):

• L4: 72W × 8,760 hours/year × $0.12/kWh = $75.33/year
• T4: 70W × 8,760 hours/year × $0.12/kWh = $73.32/year

Cooling (data center rule of thumb: 1:1 ratio, $1 cooling per $1 electricity):

• L4: ~$75/year
• T4: ~$73/year

Maintenance and support (assume 10% of hardware cost/year):

• L4: $250/year
• T4: $150/year

5-year TCO comparison:

• L4: $2,500 + ($75 + $75 + $250) × 5 = $4,250
• T4: $1,500 + ($73 + $73 + $150) × 5 = $2,480

However: L4 supports 3-5x higher throughput, so per-inference cost strongly favors L4.

TCO per 1 billion inferences:

• L4 (1,000 inferences/sec): 11 days processing = 264 hours × $0.44/hr = $116
• T4 (300 inferences/sec): 37 days processing = 888 hours × $0.20/hr = $178

L4 saves $62 per 1 billion inferences despite higher hardware cost.

Facility Constraints:

L4: 72W, single-slot form factor, fits standard rack

T4: 70W, single-slot form factor, fits standard rack

No difference for most data centers. Both fit in standard NVIDIA A100 PCIe form factor slots.

Real-World Comparisons from Production Systems

Use Case: Search-as-a-Service Platform

Company: Medium startup, 1M searches/day

Initial deployment (2023): 40x T4 GPUs, 15-second average response, $640/month (0.20/hr)

Evaluation period (2024): Move to L4, 5-second average response, need 15x L4 = $198/month

Decision: Migrate to L4. Faster responses improve user retention by estimated 8%. $442/month cost reduction funds additional infrastructure (database, cache, monitoring).

Use Case: Content Moderation

Company: Large platform, 100M contents/day to moderate

T4 approach: 200x T4 GPUs in batch mode, process 1K/batch, 24-hour queue

• Cost: $200 × 0.20 × 730 = $29,200/month
• Latency: 24 hours to moderation decision (unacceptable for live moderation)

L4 approach: 60x L4 GPUs in streaming mode, real-time moderation

• Cost: 60 × 0.44 × 730 = $19,140/month
• Latency: sub-second moderation decisions

L4 cheaper AND meets latency requirements that T4 cannot.

FAQ

Q: Is L4 worth the upgrade from T4? A: Depends on workload. For latency-sensitive (chat): absolutely. For batch processing: maybe. Calculate your specific throughput needs and costs.

Q: Can I use T4 for production? A: Yes, if latency tolerance allows 15-20 second responses. Works for batch, transcoding, classification. Not ideal for real-time chat.

Q: What's the VRAM limitation at scale? A: T4's 16GB limits model sizes to 7B and smaller (or heavily quantized 13B). L4's 24GB supports 13B natively. For 70B+ models, both insufficient; need H100.

Q: Does sparsity on T4 matter in practice? A: Rarely. 50% sparsity means 50% of weights must be exactly zero. Post-quantization, most models don't have clean 50% sparsity. Actual speedup: 5-20% vs theoretical 2x.

Q: Which GPU should I use for fine-tuning? A: L4 preferred for 7B-13B LoRA fine-tuning. T4 works for 7B only. Full fine-tuning: both marginal, need H100/A100. Choose L4 for faster iteration.

Q: Is T4 completely obsolete? A: No. Suitable for batch processing, non-time-critical inference, and workloads where cost trumps latency. T4 remains in cloud offerings due to legacy and cost.

Q: Can I mix T4 and L4 in same cluster? A: Yes. Route latency-sensitive requests to L4, batch jobs to T4. Adds operational complexity but optimizes costs.

Q: What about newer GPUs (H100, B200)? A: Different tier. H100 ($1.99-3.78/hr) for large models or training. B200 ($5.98/hr) for latest-generation scaling. Compare L4 vs T4 only for inference workloads under 24GB.

Q: Is cloud rental cheaper than buying? A: Depends on utilization. <50% utilization: cloud cheaper. >70% continuous: consider purchasing or committed instances. For prototyping: always cloud.

Q: What about ARM-based GPU alternatives? A: NVIDIA dominates cloud offerings (99% of cloud GPU market). AMD MI cards (MI300X) available, similar pricing, smaller ecosystem.

Related Resources

Compare GPUs and optimize infrastructure choices:

• NVIDIA L4 Specifications full technical details
• NVIDIA Tesla T4 Details legacy specs
• A100 vs H100 Comparison for large model inference
• RTX 4090 for Local Inference consumer GPU alternative
• GPU Rental Pricing Directory with RunPod, Lambda, AWS rates

Sources

• NVIDIA L4 Datasheet: https://www.nvidia.com/content/PDF/nvidia-ampere-ga-102-gpu-architecture-whitepaper-v2.pdf
• NVIDIA T4 Datasheet: https://www.nvidia.com/content/PDF/nvidia-turing-gpu-architecture-whitepaper-v02.pdf
• RunPod GPU Pricing: https://www.runpod.io/gpu-instance-types
• Lambda GPU Pricing: https://www.lambdalabs.com/service/gpu-cloud
• NVIDIA CUDA Compute Capability: https://docs.nvidia.com/cuda/cuda-c-programming-guide/