NVIDIA A100 80GB Custom PCIe Tensor Core GPU

The "Custom" designation and sub-market pricing ($7,900 to $8,200 versus $15,000+ for genuine new NVIDIA A100 80GB PCIe) indicates a non-standard retail configuration. Common possibilities: SXM4 module converted to PCIe form factor with aftermarket heatsink, data center OEM variant with custom cooling, or reconfigured card. Same GA100 silicon and compute output in all cases. Confirm exact provenance, thermal design, firmware compatibility, and warranty terms with MillionMiner before purchasing.

Yes. Same GA100 chip regardless of form factor origin. 6,912 CUDA cores, 432 Tensor Cores, 80GB HBM2e at 1,935 GB/s, 19.5 TFLOPS FP32, all standard A100 80GB specifications. CUDA code, ML frameworks, and all software behave identically. The "Custom" affects physical packaging and potentially thermal design, not silicon compute capability.

The 40GB Original carries genuine NVIDIA retail/OEM provenance with full enterprise warranty. The 80GB Custom carries a non-standard designation that affects warranty status and resale value, even though it delivers more compute capability. Buyers prioritizing verified NVIDIA warranty chain pay the premium for "Original." Buyers prioritizing compute per dollar get more memory and bandwidth from the 80GB Custom at lower price.

The 80GB extends practical capabilities significantly. LoRA fine-tuning on models up to 30B at FP16 (40GB caps at 13B). Inference on quantized 70B models at 4-bit GPTQ/AWQ (40GB caps at ~25B at INT8). Larger batch sizes across all workloads improving throughput. MIG instances at 10GB each versus 5GB, doubling usable memory per isolated tenant. Two cards with NVLink: 160GB unified pool for full fine-tuning of 30B+ models or inference on 70B at FP16 without quantization.

Depends on the physical configuration. Standard NVIDIA A100 80GB PCIe cards support NVLink bridge for 2-GPU 600 GB/s interconnect. SXM4-to-PCIe conversions may or may not retain NVLink bridge compatibility depending on the conversion design. Confirm NVLink support with MillionMiner for this specific Custom variant.

Yes. MIG is a GA100 silicon feature, not a form factor feature. Up to 7 fully isolated instances at 10GB each with dedicated memory, cache, and compute. The 10GB per instance (versus 5GB on the 40GB) is the meaningful difference, supporting larger inference models per MIG slice and more practical multi-tenant deployments.

Confirm the card runs standard NVIDIA A100 firmware compatible with CUDA 12.x toolkit and current NVIDIA enterprise drivers. Non-standard or outdated firmware can cause driver stability issues under sustained load. Ask MillionMiner for the firmware version, NVIDIA part number or device ID (10DE:20B5 for A100 PCIe 80GB), and confirmation of CUDA 12.3+ compatibility testing.

A100 80GB Custom: 19.5 TFLOPS FP32, 80GB HBM2e at 1,935 GB/s, NVLink (if supported), 7 MIG instances at 10GB, 300W, $7,900 to $8,200. RTX PRO 6000 Workstation: 125 TFLOPS FP32, 96GB GDDR7 at 1,792 GB/s, no NVLink, 4 MIG instances, 600W, $10,000 to $11,000. The RTX PRO 6000 crushes on raw FP32 compute. The A100 80GB wins on HBM bandwidth per dollar, NVLink interconnect, MIG granularity, and power efficiency. Different tools for different priorities.

If NVLink bridge compatibility is confirmed, two cards create a unified 160GB pool at 600 GB/s bidirectional. That 160GB supports inference on 70B models at FP16 without quantization, full fine-tuning of 30B+ models, and training configurations that exceed single-GPU memory constraints. Verify NVLink support specifically for this Custom variant.

Yes. Jarvislabs, RunPod, and every major cloud provider still run A100 fleets. Software support continues across CUDA 12.x, PyTorch, TensorFlow, and TensorRT. The A100 handles most practical AI inference workloads at a fraction of H100/B200 pricing. It is not cutting-edge for training, but for production inference, fine-tuning, and HPC, the A100 80GB remains the proven workhorse.