By Sandeep Narahari, Contributor. Last updated: August 2026.
For a single GPU, the A100 PCIe and the A100 SXM perform almost identically: both have 80GB of HBM2e, both hit 19.5 TFLOPS FP32, and they sit within about 5% on memory bandwidth. For the full spec rundown, see the NVIDIA A100 GPU guide. The scaling gap only opens with multiple GPUs, where the SXM form factor’s NVLink and NVSwitch interconnect pull far ahead of PCIe.
TL;DR
- On a single card, A100 PCIe and A100 SXM are effectively the same chip (same GA100, same 80GB HBM2e, same 19.5 TFLOPS FP32). Memory bandwidth differs by ~5% (1,935 GB/s PCIe vs 2,039 GB/s SXM), per NVIDIA’s A100 datasheet.
- The A100 PCIe draws 300W; the A100 SXM draws 400W as standard (up to 500W in HGX custom-thermal configs). That extra power headroom lets SXM sustain higher real-world throughput under long, heavy loads, but the peak specs are the same chip.
- The real divergence is interconnect. SXM gives every GPU 600 GB/s of NVLink bandwidth through NVSwitch across up to 8 cards; PCIe caps NVLink at a 2-GPU bridge and otherwise falls back to PCIe Gen4 at 64 GB/s, roughly 10x slower.
- In one memory-bound HPL-AI benchmark at 4 GPUs, SXM ran about 4.4x faster than PCIe (485 vs 110 TFLOPS). “4.4x” here means SXM finished the work at roughly 4.4 times the throughput, a ratio on that one test, not a general rule: it shrinks when GPUs communicate less and is zero on a single GPU. See the NVIDIA developer-forum result.
- Rule of thumb: rent PCIe for single-GPU inference, fine-tuning, and data-parallel jobs; you need SXM (HGX/DGX) only when GPUs must exchange gradients or activations at high bandwidth, as in large tightly-coupled training runs. Check current A100 GPU pricing before you commit either way.
Are the A100 PCIe and SXM the same on a single GPU?
Yes. On a single GPU the A100 PCIe and A100 SXM share the same Ampere GA100 chip, the same 80GB of HBM2e, and the same peak compute: 19.5 TFLOPS FP32 and identical peak Tensor Core throughput (156 TFLOPS TF32 and 312 TFLOPS FP16/BF16 dense, doubled with sparsity). NVIDIA lists those compute figures once for both form factors, so the peak numbers do not differ between PCIe and SXM. The published differences are memory bandwidth and power.
The SXM variant delivers 2,039 GB/s versus 1,935 GB/s for PCIe, about a 5% edge, and carries a higher 400W standard TDP versus 300W (HGX custom-thermal configs go up to 500W). That extra power headroom and cooling let SXM sustain higher real-world throughput on long, heavy jobs, even though the two share the same peak specs. The gap is small and rarely worth a price premium on a single card.
The practical takeaway: if your workload fits on one A100, the cheaper of PCIe or SXM is almost always the better buy. The SXM form factor’s headline advantage stays dormant until you add a second, fourth, or eighth GPU.
What is the difference between A100 PCIe and SXM?
The A100 PCIe and A100 SXM differ in form factor, power, and how they connect to other GPUs, not in their core compute. Both use the same GA100 chip, the same 80GB of HBM2e, and the same peak compute (19.5 TFLOPS FP32 and identical peak Tensor Core throughput); for that full spec sheet, see the NVIDIA A100 GPU guide. What actually changes between the two form factors is the short list below.
| What differs | A100 80GB PCIe | A100 80GB SXM4 |
|---|---|---|
| Form factor | PCIe card, standard server | SXM module on HGX baseboard |
| Memory bandwidth (GB/s) | 1,935 | 2,039 |
| Max TDP (W) | 300 | 400 (up to 500 in HGX custom-thermal configs) |
| GPU-to-GPU interconnect | NVLink bridge for up to 2 GPUs (600 GB/s), else PCIe Gen4 (64 GB/s) | NVLink 600 GB/s across up to 8 GPUs via NVSwitch |
| Typical node size | 1 to 8 GPUs in standard servers | 4 or 8 GPUs on HGX baseboard |
Figures per the NVIDIA A100 datasheet. Takeaway: bandwidth and TDP barely move the needle; the interconnect row is the one that changes the multi-GPU story.
One hard constraint worth stating plainly: PCIe and SXM4 A100s are not interchangeable. An SXM4 module cannot go into a standard PCIe server slot, and you cannot add NVLink to a PCIe box beyond a 2-GPU bridge. Choosing SXM means deploying an HGX-class baseboard (4, 8, or 16 GPUs) or a DGX A100, not slotting a card into a normal server.
Why does SXM scale better for multi-GPU?
SXM scales better because every GPU gets a dedicated 600 GB/s NVLink path to every other GPU through NVSwitch, while PCIe GPUs, beyond a 2-GPU bridge, fall back to the server’s PCIe Gen4 topology at up to 64 GB/s per x16 link. NVLink is roughly 10x the bandwidth of PCIe Gen4, per the NVIDIA HGX A100 datasheet.
NVLink is NVIDIA’s high-speed GPU-to-GPU interconnect, and NVSwitch is the on-board switch that connects all the NVLinks so an 8-GPU node behaves like one large GPU. On an HGX A100 baseboard, the aggregate GPU-to-GPU bandwidth is 2.4 TB/s for a 4-GPU node and 4.8 TB/s for an 8-GPU node. That fabric is what keeps gradient and activation exchange from becoming the bottleneck during distributed training.
PCIe A100s do not get this. NVLink on the PCIe card exists only as a bridge connecting up to 2 GPUs; a third or fourth PCIe GPU has to communicate over the server’s PCIe Gen4 topology, where each x16 link tops out at 64 GB/s and the effective path depends on PCIe switches, NUMA layout, and GPU placement. For workloads where GPUs constantly exchange data, that interconnect becomes the bottleneck while SXM keeps scaling.
The interconnect comparison, per NVLink path:
| Interconnect | A100 PCIe | A100 SXM (HGX) |
|---|---|---|
| Max GPUs with full NVLink | 2 (bridge) | 8 (NVSwitch) |
| Per-GPU NVLink bandwidth (GB/s) | 600 (2-GPU bridge only) | 600 (all GPUs) |
| Fallback beyond NVLink | PCIe Gen4, up to 64 GB/s per x16 link | not needed within node |
| 8-GPU aggregate bandwidth (TB/s) | not available | 4.8 |
Takeaway: on a single card the two are twins; across 8 cards, SXM has a 4.8 TB/s NVLink fabric while PCIe leans on the server’s PCIe Gen4 links at up to 64 GB/s each.
How big is the real multi-GPU scaling gap?
In a memory-bound benchmark, SXM ran about 4.4x faster than PCIe. On the HPL-AI benchmark, a 4-GPU A100 SXM4 (HGX) system delivered roughly 485 TFLOPS while a comparable 4-GPU A100 PCIe system managed roughly 110 TFLOPS, per an NVIDIA developer-forum thread.
To be clear about what “4.4x” means: it is a throughput ratio, 485 divided by 110, on that single test. The SXM system completed the same benchmark about 4.4 times faster than the PCIe system with the same number of GPUs. The result is consistent with the far higher GPU-to-GPU bandwidth of the SXM/NVLink system, though the outcome also depends on the complete system topology, so NVLink is not the sole proven cause. It is not a price difference, a per-GPU spec difference, or a figure that holds for every workload.
That 4.4x is the high end of the reality check, not the average. HPL-AI is memory-bound, and its multi-GPU performance is strongly affected by GPU interconnect and system topology, which is what punishes the PCIe setup hardest here. Workloads that keep most data resident on each GPU, or that shard along boundaries with little cross-GPU chatter, see a much smaller gap. Data-parallel training with infrequent gradient sync, batched inference across independent replicas, and embarrassingly parallel jobs can run on PCIe multi-GPU nodes with minimal penalty.
So the honest framing is a spectrum, not a verdict. Single GPU: no meaningful difference. Loosely-coupled multi-GPU: small difference. Tightly-coupled multi-GPU training and HPC: the gap grows toward that 4.4x figure, and it compounds as you scale past 4 GPUs to 8 or 16.
Which A100 should you rent: PCIe or SXM?
Rent PCIe for single-GPU and loosely-coupled work; rent SXM only when GPUs must exchange data at high bandwidth. The decision follows the workload, not the label.
Choose A100 PCIe for single-GPU inference, LoRA or QLoRA fine-tuning, Stable Diffusion and image generation, data processing, and data-parallel training where GPUs sync gradients infrequently. You get the same 80GB and 19.5 TFLOPS at 300W, usually at a lower hourly rate. Compare live rates on the Akash GPU pricing page.
Choose A100 SXM (HGX or DGX) for large model-parallel or tensor-parallel training, multi-node runs, and HPC workloads that are communication-bound, where the 600 GB/s NVLink fabric is the whole point. If your job would spend most of its time waiting on GPU-to-GPU transfers over PCIe, SXM pays for itself in wall-clock time.
A useful cross-check: a 70B-class model can serve on a single A100 80GB with sufficiently aggressive quantization (roughly INT8 or 4-bit), while higher-precision inference, long context, or large batches can push it onto multiple GPUs. When one card is enough, the cheaper of PCIe or SXM wins. If you are weighing capacity instead of form factor, see A100 40GB vs 80GB. For training that needs frequent GPU-to-GPU communication across 4 or more GPUs, SXM is generally the better architecture; note that 4 GPUs on their own do not require SXM if the work is loosely coupled. For how the newer generations change NVLink bandwidth, see NVIDIA B300 vs B200 vs H200.
A100 rental rates change constantly and vary by form factor, commitment, and provider, so any figure here would go stale fast. For current A100 hourly pricing across providers, see the NVIDIA A100 GPU guide on The Bid and the live Akash GPU pricing page.
FAQ
Is the A100 SXM faster than the A100 PCIe?+
On a single GPU, barely: per the NVIDIA A100 datasheet, the A100 SXM has about 5% more memory bandwidth (2,039 vs 1,935 GB/s) and a higher 400W TDP that lets it sustain throughput better under long, heavy loads. Peak compute, including peak Tensor Core throughput, is identical. The meaningful speed advantage appears only across multiple GPUs, where SXM’s 600 GB/s NVLink outruns PCIe’s slower Gen4 links.
Does the A100 PCIe support NVLink?+
Per the NVIDIA HGX A100 datasheet, the A100 PCIe supports NVLink only through a bridge connecting up to 2 GPUs at 600 GB/s. Beyond two cards, PCIe A100s communicate over the server’s PCIe Gen4 topology, where each x16 link tops out at 64 GB/s. Full NVLink across 4 or 8 GPUs requires the SXM form factor on an HGX baseboard with NVSwitch.
Can I use A100 SXM in a normal server?+
No. The A100 SXM4 is a module that mounts on an NVIDIA HGX A100 baseboard, not a card for a standard PCIe slot. PCIe and SXM4 A100s are not interchangeable, so choosing SXM means deploying an HGX-class baseboard or a DGX A100 rather than adding a card to an existing server.
Which A100 is better for LLM inference?+
For single-GPU LLM inference, the A100 PCIe is usually the better choice because it matches the SXM’s 80GB VRAM and 19.5 TFLOPS FP32 at lower power and typically lower cost. SXM’s NVLink advantage does not help single-GPU inference, so paying the SXM premium for it is wasted unless you are sharding one model across several GPUs.
How much faster is A100 SXM for multi-GPU training?+
It depends on how much the GPUs communicate. In a memory-bound HPL-AI benchmark, a 4-GPU SXM4 system was about 4.4x faster than 4-GPU PCIe (~485 vs ~110 TFLOPS), consistent with SXM’s far higher GPU-to-GPU bandwidth, though the result also depends on system topology. See the NVIDIA developer-forum result. Communication-light workloads see far smaller gaps, and single-GPU workloads see none.
Can I run multi-GPU training on Akash?+
You can run data-parallel and loosely-coupled multi-GPU jobs on Akash using standard GPU nodes, which suits most fine-tuning and inference scaling. For tightly-coupled training that needs NVLink, confirm the provider lists a genuine SXM/HGX A100 node before deploying. Akash’s Starcluster initiative targets aggregating HGX-class clusters for this use case.
