Xincuan 서버 | 엔터프라이즈 서버 하드웨어 공급업체
L40S vs A100 vs RTX 6000 에이다：一张表看懂推理卡定位
AI 推理时代的 GPU 选型比训练时代复杂：训练看总量，推理看性价比和部署密度。NVIDIA L40S 恰好卡在 A100 与 RTX 6000 Ada 之间的甜区：
| 기인하다 | 엔비디아 L40S | 엔비디아 A100 80GB | RTX 6000 에이다 |
|---|---|---|---|
| 건축학 | Ada Lovelace | Ampere | Ada Lovelace |
| 메모리 | 48 GB GDDR6 ECC | 80 GB HBM2e | 48 GB GDDR6 ECC |
| Memory bandwidth | 864 GB/초 | 2,039 GB/초 | 960 GB/초 |
| FP16 (Tensor) | 733 테플롭스 | 312 테플롭스 | ~1,198 TFLOPS (FP16)* |
| FP8 (Tensor) | 1,466 테플롭스 | – | ~2,396 TFLOPS* |
| Max power | 350 승 | 400 승 | 300 승 |
| Primary role | Inference + 표현 | 훈련 (legacy) | Workstation graphics |
*RTX 6000 Ada Tensor figures per NVIDIA spec sheet; the practical takeaway is that L40S delivers data-center-class inference and graphics in one PCIe card at a power envelope any 2U server can feed.
핵심사양
| 기인하다 | 사양 |
|---|---|
| 건축학 | 엔비디아 에이다 러브레이스 |
| GPU memory | 48 ECC가 포함된 GB GDDR6 |
| Memory bandwidth | 864 GB/초 |
| CUDA / RT / Tensor cores | 18,176 / 142 / 568 |
| Tensor performance | FP8: 1,466 테플롭스; FP16: 733 테플롭스; TF32: 366 테플롭스 |
| RT core performance | 212 테플롭스 |
| Interconnect | PCIe Gen4 x16 (64 GB/s bidirectional) |
| Max power | 350 승 (수동적인, data center cooling) |
| 폼 팩터 | Dual-slot PCIe accelerator |
| Software stack | CUDA, TensorRT / TRT-LLM, NVIDIA AI Enterprise, vGPU |
Why 2026 Inference Deployments Pick the L40S
Three numbers drive the decision: 1,466 TFLOPS of FP8 for quantized inference (INT8/FP8 is where production LLM serving lives), 350 승 power draw that fits the air-cooled racks most enterprises already run, 그리고 PCIe Gen4 x16 compatibility that drops into existing server slots without an NVLink-scale chassis. Paired with TensorRT-LLM and vLLM, a single L40S serves production LLM and RAG workloads at a per-token cost well below H100-class hardware – which is why it became the default inference accelerator for on-prem deployments.
Graphics and Rendering: The Second Half of the Story
L40S is also a full graphics card: 212 TFLOPS of RT core performance covers CAD, product visualization and Omniverse/NeRF workloads, so one GPU tier serves both the inference cluster and the rendering farm. That dual role is rare at this price point – it collapses two hardware budgets into one SKU.
Deployment in Standard Servers
- 힘: 350 W per card – two cards fit a 2U node’s PSU budget (참조 Poweredge R760, 2 엑스 350 W double-width GPU support, or the 엑스퓨전 2288H V7 ~와 함께 4 x double-width slots).
- PCIe: Gen4 x16 card runs in Gen5 slots at full Gen4 bandwidth – no platform mismatch on 2026 서버.
- 냉각: 패시브 방열판, data center airflow required – plan server airflow direction before racking.
- 회로망: for multi-GPU inference, pair with a 100GbE NIC (E810-CQDA2) to feed model shards without host-CPU bottlenecks.
- 소프트웨어: vGPU licensing extends the card to VDI graphics pools; TRT-LLM profiles cover the major open models.
구매 FAQ
- Training or inference? L40S is inference- and graphics-optimized; training-heavy fleets should look at H200-class (우리를 보아라 AI hardware insights).
- ECC? 예 – 48GB GDDR6 with ECC for data integrity.
- NV링크? The L40S is a PCIe-scale card; multi-card scaling is via software (vLLM/TensorRT) and network, not NVLink mesh.
- 어떤 서버? Any Gen4/Gen5 PCIe x16 slot with 350W power delivery – R760, 2288H V7 and similar.
- 보증? 표준 하드웨어 보증이 제공됩니다.; contact us for volume pricing.
Building an on-prem inference tier? We configure L40S-ready nodes with power/cooling matched builds and 100GbE fabric – 공장 직접 가격, 3-1년 보증, global shipping and free AI-hardware consultation from our authorized partner team. 문의하기 for an inference TCO comparison.
