Specifications: RTX 4090 24GB vs L40S 48GB
The RTX 4090 24GB and L40S 48GB represent different generations of GPU architecture for AI workloads. Memory capacity ranges from 4090GB to 48GB, with significant differences in memory bandwidth, compute throughput (FP8/FP16/TF32), NVLink connectivity, and TDP. The L40S 48GB supports newer technologies like FP8 transformer engines, fourth-gen tensor cores, and higher-bandwidth HBM3e or HBM4 memory.
AI Inference Performance
For LLM inference, the L40S generally achieves 30-120% higher throughput than the RTX 4090 depending on model size and batch configuration. For a 7B model at FP8 with continuous batching, the RTX 4090 serves 1,800-2,400 tok/s while the L40S reaches 3,000-5,000 tok/s. For 70B models, tensor parallelism across 4 GPUs is needed on the RTX 4090 while the L40S may serve the same model with 2 GPUs due to higher VRAM. Prefill latency for 4K tokens ranges from 35-55ms for the RTX 4090 versus 18-35ms for the L40S.
Training Throughput Comparison
On training workloads, the L40S delivers 40-120% higher throughput for typical model sizes. For a 7B model at BF16 mixed precision, per-GPU throughput reaches 3,500-4,500 tok/s on the RTX 4090 versus 5,000-8,000 tok/s on the L40S. Model FLOPS utilization (MFU) ranges from 38-48% on the RTX 4090 and 42-55% on the L40S. Memory capacity constraints on the RTX 4090 require activation checkpointing for models larger than 13B, while the L40S accommodates larger models without checkpointing.
VRAM and Model Capacity Analysis
Memory capacity is the most critical differentiator. The RTX 4090 24GB has 4090 VRAM, while the L40S 48GB has 48GB VRAM. At FP16, a 7B model requires ~14 GB for weights, plus KV cache of ~1.5 GB per 128K context per request. INT4 quantization halves the weight memory requirement, enabling larger models or batch sizes. The VRAM gap is most impactful for long-context serving and large batch inference.
Cloud Pricing and TCO
On-demand cloud pricing for the RTX 4090 averages $0.50/hr while the L40S averages $1.00/hr. However, cost-per-token analysis often favors the L40S by 15-40% for sustained production workloads due to higher throughput. Reserved 12-month contracts provide 30-50% discounts. For a 64-GPU, 3-year TCO, the RTX 4090 cluster costs $549K-$947K while the L40S cluster costs $1148K-$868K including hardware, power, cooling, and maintenance.
Which GPU Should You Choose?
Choose the RTX 4090 for: budget-constrained deployments, models under 13B that fit in available VRAM, batch inference workloads where throughput per dollar is secondary to absolute cost, and development/staging environments. Choose the L40S for: production serving at scale, models larger than 13B parameters, workloads requiring FP8/FP4 precision, long-context inference beyond 128K tokens, and clusters above 128 GPUs where scaling efficiency matters.