Choose the Tesla V100 PCIe for a conventional server with standard PCIe x16 slots, adequate auxiliary power, full-height/full-length clearance and strong front-to-back airflow. Choose the Tesla V100 SXM2 only when your chassis and baseboard were designed for SXM2 modules and NVLink. An SXM2 V100 is not a PCIe expansion card, so it cannot be installed in an ordinary PCIe slot.
Both versions use NVIDIA’s Volta architecture, provide 5,120 CUDA cores, 640 Tensor Cores and 16GB or 32GB of HBM2 at 900GB/s. The meaningful differences are the host interface, GPU-to-GPU topology, peak figures, power envelope and physical installation requirements.
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The short decision
Start with the server, not the benchmark. If the machine exposes standard PCIe x16 slots, the PCIe model is the compatible choice. If it has an OEM-specific SXM2 socket, carrier board, heatsink and NVLink wiring, the SXM2 model can deliver substantially more inter-GPU bandwidth and slightly higher published peak compute.
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| Situation | Suitable version | Reason |
|---|---|---|
| Existing general-purpose PCIe server | Tesla V100 PCIe | Installs as a full-height/full-length PCIe card and uses the server’s PCIe slot and auxiliary power system. |
| Purpose-built multi-GPU NVLink server | Tesla V100 SXM2 | Uses an SXM2 module and a matching baseboard with direct NVLink connections. |
| Single-GPU inference or CUDA work | Usually PCIe | Local GPU memory and compute are the same class, while PCIe is easier to deploy in standard hardware. |
| Training with frequent GPU-to-GPU transfers | SXM2 when supported by the platform | The published interconnect figure is 300GB/s for SXM2 NVLink versus 32GB/s for PCIe. |
| Memory-bound single-GPU job | Either, matching capacity | Both are offered with 16GB or 32GB HBM2 and 900GB/s local memory bandwidth. |
The performance figures above are NVIDIA’s published specifications, not a guarantee that every application will scale by the same ratio. Software, collective-communication libraries, model partitioning and the server topology determine how much an application benefits from NVLink.
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What is different physically?
Tesla V100 PCIe
The PCIe version is a discrete accelerator card. It occupies a standard PCIe slot, is specified as full-height and full-length, and uses a passive thermal solution. “Passive” means the card relies on the host server’s fans and ducting rather than having its own blower. A workstation or lightly ventilated tower can therefore be unsuitable even when it has a mechanically compatible slot.
Before ordering, confirm that the chassis has enough length and height clearance, that neighboring slots do not block the card, and that the platform can provide the required auxiliary power. NVIDIA rates the PCIe board for up to 250W.
Tesla V100 SXM2
SXM2 is a module mounted to a compatible baseboard; it is not a card with an edge connector. The module, socket, heatsink, retention hardware, firmware and NVLink wiring are part of an OEM platform design. A server may have the right number of CPUs and memory channels yet still be unable to accept an SXM2 V100 if its baseboard was built for PCIe accelerators.
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Does V100 SXM2 work in a PCIe slot?
No. An SXM2 V100 has no PCIe card edge intended for a normal slot. It requires an SXM2 socket and the associated OEM baseboard. No adapter turns an ordinary PCIe slot into a supported SXM2 platform because the electrical interface, mechanical mounting, power delivery, cooling and NVLink topology all belong to the server design.
Conversely, a PCIe V100 can be installed only where the server supplies a suitable PCIe slot, power and airflow. Putting a PCIe card in a server marketed as “NVLink capable” does not create the direct module-to-module NVLink fabric used by SXM2 systems.
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Specification comparison
| Specification | Tesla V100 PCIe | Tesla V100 SXM2 |
|---|---|---|
| Architecture | NVIDIA Volta | NVIDIA Volta |
| CUDA cores | 5,120 | 5,120 |
| Tensor Cores | 640 | 640 |
| HBM2 capacity | 16GB or 32GB | 16GB or 32GB |
| HBM2 bandwidth | 900GB/s | 900GB/s |
| FP64 peak | 7 TFLOPS | 7.8 TFLOPS |
| FP32 peak | 14 TFLOPS | 15.7 TFLOPS |
| Tensor peak | 112 TFLOPS | 125 TFLOPS |
| Host/interconnect | PCIe Gen3, 32GB/s | NVLink, 300GB/s |
| Form factor | Full-height/full-length PCIe card | SXM2 module |
| Maximum power | 250W | 300W |
| Thermal solution | Passive | Passive |
These values are from NVIDIA’s Tesla V100 specifications and performance guidance published in 2018. The equal HBM2 capacity and 900GB/s local bandwidth mean that changing from PCIe to SXM2 does not increase the amount or speed of memory attached to one GPU. SXM2’s advantage is the path between GPUs and its modestly higher published peak compute figures.
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PCIe Gen3 on the PCIe card
The PCIe V100 communicates with the host and other devices through PCIe Gen3. NVIDIA lists 32GB/s for this interface. That is adequate for workloads in which the GPU spends most of its time operating on data already in its own HBM2, such as many inference, image-processing and single-GPU CUDA jobs.
When a workload repeatedly exchanges large tensors between GPUs, PCIe can become the limiting path. Multiple cards may also share CPU-root-complex or switch resources, so the nominal interface figure should not be interpreted as guaranteed end-to-end bandwidth for every pair of devices.
NVLink on SXM2 platforms
NVIDIA lists 300GB/s of NVLink interconnect bandwidth for the SXM2 variant. In a correctly wired and supported server, direct links let GPUs exchange data without routing every transfer through the host CPU and PCIe fabric. This is why SXM2 is attractive for tightly coupled multi-GPU training, distributed HPC kernels and other jobs dominated by peer-to-peer traffic.
NVLink is a topology feature, not simply a label on the GPU. The number and arrangement of links, bridge wiring, firmware and software support vary by OEM platform. A server advertised as containing V100 SXM2 modules should therefore be checked for the actual GPU-to-GPU topology rather than judged from the module name alone.
Peak compute: how much faster is SXM2?
The SXM2 specification lists 7.8 TFLOPS FP64, 15.7 TFLOPS FP32 and 125 TFLOPS tensor performance. The PCIe specification lists 7 TFLOPS FP64, 14 TFLOPS FP32 and 112 TFLOPS tensor performance. CUDA-core and Tensor-Core counts are identical, so the difference comes from the operating configuration represented by NVIDIA’s peak ratings, not from a larger core count.
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These are theoretical maxima. An application limited by memory capacity, memory bandwidth, input pipelines, synchronization or PCIe transfers may see little benefit from the peak-rate difference. For a single-GPU job, both versions expose the same 16GB or 32GB HBM2 choices and 900GB/s local bandwidth, making the SXM2 premium difficult to justify unless the platform already exists or the job is interconnect-heavy.
Which version suits common workloads?
Single-GPU inference
Choose PCIe when the model fits in 16GB or 32GB and the server has the required slot, power and airflow. Inference usually benefits from predictable deployment and sufficient local memory more than from an NVLink fabric that a single GPU does not use. SXM2 is reasonable only when it is already part of a validated server and the purchase does not impose an unnecessary platform constraint.
CUDA development and batch jobs
For ordinary CUDA jobs, the PCIe board is the practical default in an existing server. It retains the Volta instruction and Tensor Core capabilities while avoiding a specialized baseboard. Confirm that the intended CUDA toolkit, driver branch and operating system support the V100 in the way your application requires.
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High-performance computing
HPC workloads that keep data local to one accelerator can use either form factor. Applications that perform frequent peer-to-peer exchanges, domain decomposition or collective operations can benefit from SXM2’s 300GB/s NVLink specification, provided the application’s communication library recognizes the topology and the server exposes direct links.
Multi-GPU deep-learning training
SXM2 is the stronger architectural fit for tightly coupled training because gradients and activations can move over NVLink. It is not automatically faster for every training run: data-loader speed, batch size, model parallelism, collective algorithms and link topology still determine scaling. PCIe remains appropriate for loosely coupled jobs, independent processes, or systems where each GPU handles a separate workload.
Memory-bound jobs
If the bottleneck is the capacity or local bandwidth of one GPU, neither form factor changes the published memory options. Select 16GB or 32GB based on the model and working set, then choose the physical version that your server can support.
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Will a 32GB V100 fit your server?
“32GB” identifies HBM2 capacity, not a universal physical configuration. A 32GB PCIe V100 still needs a full-height/full-length slot, up to 250W of board power, the specified auxiliary connectors and server-grade airflow. Check the OEM’s mechanical drawing and power specification rather than assuming that any x16 slot is sufficient.
A 32GB SXM2 V100 must match an SXM2 baseboard and heatsink assembly that support that module. The module cannot be substituted into a PCIe-only chassis, and a PCIe chassis cannot be made SXM2-compatible with a passive adapter. For used hardware, obtain the exact server model and accelerator part number before treating a “32GB V100” listing as compatible.
Power, cooling and chassis checklist
- Identify the interface. Inspect the server documentation or motherboard: you need a standard PCIe x16 slot for the PCIe card or an explicitly supported SXM2 socket/baseboard for the module.
- Validate power delivery. Budget for up to 250W per PCIe V100 or 300W per SXM2 V100, in addition to CPUs, memory, storage and fans. Confirm the required auxiliary connectors and the platform’s per-slot limits.
- Check airflow. Both variants use passive cooling. Verify the server’s fan direction, pressure and baffle design; do not rely on a desktop case fan arrangement unless the accelerator manufacturer documents it.
- Measure clearance. For PCIe, confirm full-height/full-length space and clearance around neighboring slots. For SXM2, verify the exact heatsink, retainer and module envelope specified by the OEM.
- Confirm the NVLink topology. For SXM2 multi-GPU use, check which modules are directly linked and whether the firmware exposes the intended topology.
- Check software support. Confirm the planned NVIDIA driver, CUDA version, Linux or other guest environment, and virtualization features before deployment.
Virtualization and passthrough limits
NVIDIA lists both PCIe and SXM2 V100 variants in validated platforms, but that does not mean their virtual-machine capabilities are identical. NVIDIA’s virtualization guidance limits NVLink peer-to-peer support to supported SXM2 configurations, Linux guests and direct connections. PCIe is not supported for that NVLink peer-to-peer feature.
Therefore, a virtualized PCIe V100 can still be useful for GPU compute or inference, but you should not design a VM cluster around SXM2-style direct GPU communication unless the host platform, guest operating system and connection topology are explicitly supported. Treat validation as a platform combination: GPU model, server, firmware, hypervisor, guest and driver all matter.
Installation planning for a PCIe V100
- Record the exact V100 memory variant and card dimensions.
- Power down the server and follow its electrostatic-discharge procedure.
- Install the card in the documented PCIe x16 slot, retaining it so its full-length board is supported.
- Attach only the auxiliary power connectors specified for that card and server.
- Restore the airflow shroud and fan configuration; a passive accelerator must receive the server’s designed airflow path.
- Boot into the system firmware and operating system, then install a driver and CUDA stack supported for the V100.
- Verify that the operating system detects the GPU, reports the expected 16GB or 32GB memory and exposes the intended compute capability before placing it into production.
If the server cannot provide the card’s power or airflow requirements, do not compensate by lowering expectations or running with the cover removed. Use a validated chassis or select a platform designed for the accelerator.
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- Exact baseboard and socket compatibility for the V100 SXM2 memory variant.
- Correct heatsink, thermal interface materials and retention hardware.
- Power distribution sized for up to 300W per module.
- Fan curves, ducts and baffles designed for the passive module.
- NVLink bridge or cable topology supplied by the platform manufacturer.
- Firmware and operating-system support for the installed module count.
Without those parts, an SXM2 module is not a field-replaceable equivalent of a PCIe card. A complete validated server is normally the safer acquisition path than sourcing a loose module and attempting to assemble a platform around it.
Common buying and deployment mistakes
Buying by memory size alone
A listing that says “V100 32GB” omits the interface that determines compatibility. Ask whether it is PCIe or SXM2, obtain the part number, and match it to the server’s slot or baseboard.
Assuming NVLink is present on every V100
The PCIe model’s host interface is PCIe Gen3 at 32GB/s. NVLink at 300GB/s is the SXM2 interconnect specification and depends on a supported platform topology. A PCIe card does not acquire NVLink merely because the GPU family supports it.
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Ignoring passive cooling
Both cards and modules are passive. A card that fits mechanically can still overheat if the chassis does not deliver the required airflow. Validate fan direction and the server’s accelerator cooling design before installation.
Using peak TFLOPS as a workload promise
SXM2’s higher published FP64, FP32 and tensor peaks matter only when the application can feed the arithmetic units. Communication, memory behavior and software support can dominate real throughput.
Overlooking virtualization topology
NVLink peer-to-peer support is constrained by platform, direct connections and Linux guest requirements. Treat those constraints as design requirements, not optional tuning.
A practical selection workflow
- Inventory the server. Record slot type, available power, chassis dimensions, fan layout and firmware.
- Classify the workload. Decide whether it is single-GPU, independent multi-GPU, or tightly coupled with frequent peer transfers.
- Choose memory capacity. Select 16GB or 32GB from the model’s working-set requirement; capacity is the same choice on both versions.
- Match the topology. Use PCIe for a PCIe host path; select SXM2 only when the baseboard and NVLink links are documented for the intended GPU count.
- Validate the software stack. Check driver, CUDA, operating-system and virtualization support for the exact platform.
- Budget sustained power and cooling. Use the 250W PCIe or 300W SXM2 maximum as the accelerator design point, then add the rest of the server’s load.
- Obtain a return path. For used or refurbished hardware, require the exact part number and a compatibility guarantee because the two form factors are not interchangeable.
Final recommendation by workload
| Your priority | Recommendation | Why |
|---|---|---|
| Upgrade an existing standard server | V100 PCIe | It is the only version designed as a conventional PCIe accelerator card. |
| Maximum GPU-to-GPU communication in a supported chassis | V100 SXM2 | Its NVLink interconnect specification is 300GB/s versus 32GB/s for PCIe. |
| Single-GPU memory capacity | Either 16GB or 32GB variant | Both versions offer the same capacity options and 900GB/s HBM2 bandwidth. |
| Lowest platform complexity | V100 PCIe | No SXM2 socket, module heatsink or NVLink baseboard is required. |
| Purpose-built multi-GPU training appliance | V100 SXM2 | Use it when the OEM platform supplies direct NVLink links, cooling, power and firmware support. |
The decisive question is compatibility first, then topology. A PCIe V100 is the practical accelerator for a conventional server. An SXM2 V100 is the specialist option for a server engineered around NVLink and high-density multi-GPU communication. Neither form factor changes the GPU’s 16GB/32GB HBM2 choices or 900GB/s local bandwidth; the value of SXM2 comes from its platform-level interconnect and higher power envelope.
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