NVIDIA A40 GPU for AI Inference

NVIDIA A40 GPU for AI Inference | Generated by AI

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Question: What is the NVIDIA A40 GPU?

Answer: Ampere-architecture data center GPU (2020), positioned as a “prosumer/visual computing” card but commonly used for AI inference/fine-tuning since it’s cheap-ish and has decent VRAM.

Key specs:

Architecture: Ampere (GA102, same die family as RTX 3090)
CUDA cores: 10,752
Tensor cores: 336 third-generation Tensor Cores
RT cores: 84 second-generation RT cores
Memory: 48GB GDDR6 ECC memory
FP32: double-rate vs prior gen — double-speed processing for single-precision floating point (FP32) operations
Interconnect: NVLink to scale from 48GB to 96GB across two GPUs
Bus: PCIe Gen4, passive cooling (server card, needs chassis airflow)
No NVSwitch, no SXM — it’s a PCIe-only card, unlike A100/H100

Where it fits for your use case:

vs A100 (40/80GB HBM2e): A40 has way less memory bandwidth (~696 GB/s GDDR6 vs A100’s ~1.5-2TB/s HBM2e), so it’s much worse for large-batch training/inference throughput. Memory bandwidth is usually the bottleneck for LLM inference, not raw FLOPs.
vs 3090 (24GB GDDR6X): A40 basically = 3090 die + double VRAM + ECC + passive cooling + NVLink, at a big price premium — you’re paying for VRAM and reliability, not compute.
Good for: fitting a 30B-ish model in bf16 for inference on a single card, or QLoRA fine-tuning where VRAM capacity matters more than bandwidth.
Bad for: training throughput at scale — you’d get much better $/token on A100/H100 or even 4090 clusters if VRAM isn’t the constraint.

Note: Lenovo’s product guide already lists it as withdrawn from marketing — the NVIDIA A40 GPU is withdrawn from marketing — so it’s EOL/cloud-inventory territory now, not something you’d buy new. Check current cloud pricing (RunPod/Lambda/Vast) before committing rather than buying hardware.

References:

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