Efficiency profile
NVIDIA L4
Ada Lovelace · 24 GB PCIe
Memory
24 GB GDDR6 ECC
24 GB GDDR6 ECC
Bandwidth
300 GB/s
300 GB/s
FP32
30.3 TFLOPS
30.3 TFLOPS
TDP
72W
72W
Independently verified GPU comparison Updated July 15, 2025
NVIDIA L4 vs RTX A6000
Architecture, memory, AI performance, rendering, video capabilities and rental pricing compared using NVIDIA’s official specifications and AceCloud’s current cloud GPU configurations.
High-memory graphics profile
NVIDIA RTX A6000
Ampere · 48 GB PCIe
Memory
48 GB GDDR6 ECC
48 GB GDDR6 ECC
Bandwidth
768 GB/s
768 GB/s
FP32
38.7 TFLOPS
38.7 TFLOPS
TDP
300W
300W
The honest take: Choose L4 for efficient AI inference, video processing and cloud applications that fit within 24 GB. Choose RTX A6000 when you need 48 GB memory, professional graphics, larger rendering scenes or two-GPU NVLink. In comparable 16-vCPU, 64-GB RAM AceCloud configurations, RTX A6000 costs only about 7% more.
Quick answer
Which GPU fits your workload?
See where L4 delivers efficient inference and video, while RTX A6000 leads in memory and professional graphics.
Efficient AI inference
FP8 inference for models fitting within 24 GB
L4 L4 supports FP8, fourth-generation Tensor Cores and a 72W power envelope. It is better suited to cost-efficient inference when the complete model and runtime state fit within 24 GB.
AI development and fine-tuning
Larger models and training states requiring 48 GB
RTX A6000 RTX A6000’s 48 GB memory provides more room for model weights, activations, optimizer states and larger batches. It is the safer choice when 24 GB creates memory limitations.
AI video and transcoding
High-density AV1 streaming, transcoding and video AI
L4 L4 includes two video encoders, four video decoders and four JPEG decoders. It also supports AV1 encoding and decoding, making it the stronger option for dense video pipelines.
3D rendering and visualization
Complex scenes, large assets and professional rendering
RTX A6000 RTX A6000 provides 48 GB ECC memory, professional application certifications, four DisplayPort outputs, NVLink and strong RT Core performance. It is better suited to large scenes and professional visualization.
Low-cost production deployment
Efficient inference when 24 GB is sufficient
L4 L4 has the lower starting price, newer inference precision and a much lower power envelope. It is the practical choice when 24 GB is enough and the workload does not require NVLink.
Virtual workstations
Certified graphics applications and multi-display workflows
RTX A6000 Both GPUs support NVIDIA vGPU software, but RTX A6000 is specifically designed as a professional workstation GPU with enterprise drivers, multi-display support and ISV-certified applications.
Side by side
Full technical specifications
Every technical figure below is based on NVIDIA’s official L4 and RTX A6000 specifications.
NVIDIA L4 and NVIDIA RTX A6000 technical specification comparison
| Specification | NVIDIA L4 | NVIDIA RTX A6000 |
|---|---|---|
| Architecture | Ada Lovelace | Ampere |
| GPU memory | 24 GB GDDR6 ECC | 48 GB GDDR6 ECC |
| Memory interface | 192-bit | 384-bit |
| Memory bandwidth | 300 GB/s | 768 GB/s |
| CUDA Cores | 7,424 | 10,752 |
| Tensor Cores | 232 fourth-generation | 336 third-generation |
| RT Cores | 58 third-generation | 84 second-generation |
| FP32 | 30.3 TFLOPS | 38.7 TFLOPS |
| TF32 Tensor Core, dense | 60 TFLOPS | Not separately listed |
| TF32 Tensor Core, sparse | 120 TFLOPS | Not separately listed |
| BF16 Tensor Core, dense | 121 TFLOPS | Not separately listed |
| BF16 Tensor Core, sparse | 242 TFLOPS | Not separately listed |
| FP16 Tensor Core, dense | 121 TFLOPS | Not separately listed |
| FP16 Tensor Core, sparse | 242 TFLOPS | Not separately listed |
| FP8 Tensor Core, dense | 242 TFLOPS | Not supported |
| FP8 Tensor Core, sparse | 485 TFLOPS | Not supported |
| Listed Tensor performance | Precision-specific figures above | 309.7 TFLOPS with sparsity |
| RT Core performance | 73.1 TFLOPS | 75.6 TFLOPS |
| Video encoders | 2 | 1 |
| Video decoders | 4 | 2 |
| JPEG decoders | 4 | Not listed |
| AV1 encoding | Supported | Not supported |
| AV1 decoding | Supported | Supported |
| NVLink | No | Two-way, up to 112.5 GB/s |
| Combined NVLink memory | Not applicable | Up to 96 GB across two GPUs |
| MIG support | No | No |
| vGPU support | Yes | Yes |
| Display outputs | None | 4× DisplayPort 1.4a |
| PCIe interface | PCIe Gen4 x16 | PCIe Gen4 x16 |
| Form factor | Single-slot, low-profile | Dual-slot, full-height |
| Cooling | Passive | Active |
| Maximum power | 72W | 300W |
| AceCloud configurations | 1×, 2× and 4× GPUs | 1× and 2× GPUs |
The lowest L4 plan is ₹14,681 cheaper, but it also includes half the vCPUs and system RAM of the RTX A6000 starting plan.
With both instances configured at 16 vCPUs and 64 GB RAM, RTX A6000 costs only ₹3,218 more per month. For workloads needing more than 24 GB, the additional cost is likely easier to justify than splitting the workload across multiple L4 GPUs.
Straight comparison
How do L4 and RTX A6000 differ in performance?
Raw specifications translated into simple relative performance. Longer bars indicate higher output.
GPU memory capacity
GB L4
1.0×
RTX A6000
2.0×
Memory bandwidth
GB/s L4
1.0×
RTX A6000
2.56×
FP32 performance
TFLOPS L4
1.0×
RTX A6000
1.28×
RT Core performance
TFLOPS L4
RTX A6000
Maximum power
Watts L4
RTX A6000
Comparable monthly rental price
₹/month L4
RTX A6000
Disclaimer: These are theoretical peak specifications. Real-world performance depends on model architecture, precision, batch size, framework, optimization, CPU and RAM allocation and storage throughput.
What you pay
NVIDIA L4 vs RTX A6000 rental pricing
Current Noida Linux pricing, excluding taxes:
NVIDIA L4 24 GB
Starting configuration
- 1× L4
- 8 vCPUs
- 32 GB RAM
- ₹36,730/month
NVIDIA RTX A6000 48 GB
Starting configuration
- 1× RTX A6000
- 16 vCPUs
- 64 GB RAM
- ₹51,411/month
The lowest L4 plan is ₹14,681 cheaper, but it also includes half the vCPUs and system RAM of the RTX A6000 starting plan.
With both instances configured at 16 vCPUs and 64 GB RAM, RTX A6000 costs only ₹3,218 more per month. For workloads needing more than 24 GB, the additional cost is likely easier to justify than splitting the workload across multiple L4 GPUs.
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