A100 40GB PCIe Price & Used Prices: Which Local LLMs Can It Run?
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The A100 40GB PCIe is NVIDIA's 2020 data center card, running any model up to 35B at 8-bit. Used ones may be SXM4 modules on adapter boards.
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A100 40GB PCIe price history
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Full A100 40GB PCIe specifications
- Memory40 GB · HBM2
- Bandwidth1,555 GB/s
- FP16 / BF16312 TFLOPS
- INT8624 TOPS
- Bus width
- 5,120-bit
- Architecture
- Ampere (GA100)
- Cores
- 6,912
- Tensor cores
- 432
- Ecosystem
- CUDA
- Interface
- PCIe 4.0 x16
- NVLink
- 600 GB/s
- Power
- 250 W
- Power connector
- 8-pin EPS
- Slots
- 2
- Length
- 267 mm
- Type
- Data center
- Launch date
- Jun 22, 2020
Best A100 40GB PCIe deals on eBay
Hand-picked deals from trusted eBay sellers. Shop with confidence.
As an eBay Partner, VRAMGlass may be compensated if you buy through the eBay links on this page. The price you pay stays the same.
UsedNVIDIA Tesla A100 40gb GDDR6 PCI-e Graphics Card 900-21001-0000-000$6,500Buy on eBay
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Used699-21001-0200-400 NVIDIA 40GB A100 PCIE GPUTop Rated$6,995Buy on eBay
UsedNvidia Tesla A100 40GB PCIE HBM2e GPU Accelerator Graphics CardTop Rated$6,999Buy on eBay
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UsedNvidia Tesla A100 40GB PCIE HBM2e GPU Accelerator Graphics DELL # 0RH1X7 RH1X7Top Rated$7,750Buy on eBay
What LLMs can a single A100 40GB PCIe run?
A single A100 40GB fits every model up to 35B at 8-bit, Qwen3.8 27B at about 39 tokens/s with 190K of context. 70B-class won't fit even at 4-bit.
| Model | Size | Runs? | Speed t/s | Max context |
|---|---|---|---|---|
| 4.1 GB | Runs | 202 | 256K | |
| 4.7 GB | Runs | 180.6 | 256K | |
| 10.5 GB | Runs | 161.7 | 256K | |
| 11.8 GB | Runs | 86.8 | 256K | |
| 9.8 GB | Runs | 101.6 | 256K | |
| 11.9 GB | Runs | 170.1 | 198K | |
| 11.8 GB | Runs | 83.9 | 256K | |
| 12.3 GB | Runs | 177.6 | 256K |
| Model | Size | Runs? | Speed t/s | Max context |
|---|---|---|---|---|
| 5.7 GB | Runs | 158.9 | 256K | |
| 7.1 GB | Runs | 132.5 | 256K | |
| 16.9 GB | Runs | 143 | 256K | |
| 16.8 GB | Runs | 63.6 | 256K | |
| 16.5 GB | Runs | 64.7 | 256K | |
| 18.3 GB | Runs | 155.8 | 198K | |
| 18.3 GB | Runs | 57.6 | 256K | |
| 22.1 GB | Runs | 158.3 | 256K |
| Model | Size | Runs? | Speed t/s | Max context |
|---|---|---|---|---|
| 9.5 GB | Runs | 105.5 | 256K | |
| 12.7 GB | Runs | 81.6 | 256K | |
| 26.9 GB | Runs | 121.1 | 256K | |
| 28.6 GB | Runs | 39 | 197K | |
| 29 GB | Runs | 38.5 | 191K | |
| 31.8 GB | Runs | 132.5 | 181K | |
| 32.6 GB | Runs | 34 | 100K | |
| 36.9 GB | Runs | 136 | 237K |
RunsFits fully in VRAM; speed is estimated from VRAM bandwidth.
Needs RAM offloadMoE only: expert weights sit in system RAM, so speed is estimated from 70 GB/s RAM bandwidth.
Too bigWon’t fit on one card at this quantization, even with RAM offload.
Full calculation rules are on the Methodology page.
What LLMs can multiple A100 40GB PCIe cards run?
Two A100 40GB cards run 70B-class dense models and gpt-oss 120B at 8-bit; 2026's large MoE models need four. NVLink joins only pairs.
| Model | Size | Cards | Total price | Total VRAM | Speed t/s | Total power |
|---|---|---|---|---|---|---|
| 78.9 GB | 4 cards | $31,000 | 160 GB | 137.2 | 1,000 W | |
| 96.8 GB | 4 cards | $31,000 | 160 GB | 117.8 | 1,000 W | |
| 107.2 GB | 4 cards | $31,000 | 160 GB | 64.5 | 1,000 W | |
| 108.7 GB | 4 cards | $31,000 | 160 GB | 100.2 | 1,000 W | |
| 253.9 GB | 8 cards | $62,000 | 320 GB | 57.6 | 2,000 W |
| Model | Size | Cards | Total price | Total VRAM | Speed t/s | Total power |
|---|---|---|---|---|---|---|
| 111.3 GB | 4 cards | $31,000 | 160 GB | 121.7 | 1,000 W | |
| 155.1 GB | 8 cards | $62,000 | 320 GB | 93.2 | 2,000 W | |
| 182.2 GB | 8 cards | $62,000 | 320 GB | 50 | 2,000 W | |
| 199.7 GB | 8 cards | $62,000 | 320 GB | 70 | 2,000 W |
| Model | Size | Cards | Total price | Total VRAM | Speed t/s | Total power |
|---|---|---|---|---|---|---|
| 161.9 GB | 8 cards | $62,000 | 320 GB | 91 | 2,000 W | |
| 188.2 GB | 8 cards | $62,000 | 320 GB | 96 | 2,000 W |
Full calculation rules are on the Methodology page.
Who it suits
Good fit
- You want 30B-class models at 8-bit
24GB cards can only run these models at 4-bit, while this card can step up to 8-bit
- You plan to use an inference engine like vLLM
Software support for the Ampere generation is complete, and speedups such as FlashAttention 2 all work
- Several people will share one card
MIG can split it into two 20GB slices, each running a 4-bit 27B
Not a good fit
- Running a 70B-class model on one card
Llama 3.3 70B needs 46.3 GB even at 4-bit with only 8K of context, so it doesn't fit
- Counting on FP8 for speed
This generation of chips has no FP8 compute units, so FP8 models load but don't run any faster
- Fitting it into a regular desktop
The card has no fan and relies on server airflow for cooling, and it has no video output
Before you buy
Watch out for converted SXM4 cards
On the used market, some sellers mount server-only SXM4 modules on third-party adapter boards and sell them as PCIe cards, often with “SXM4 converted” in the listing title. Cooling, power delivery and stability on these cards depend entirely on how well the adapter board is built, and there is no factory warranty. To tell them apart, install the card and check the model name in nvidia-smi: the factory PCIe version shows “A100-PCIE-40GB”, while an SXM4 module shows “A100-SXM4-40GB”. In lspci, the device IDs are 20F1 and 20B0 respectively.
Power needs an adapter cable, and cooling is up to you
The power input is an 8-pin CPU connector (EPS), which has a different plug from the 8-pin PCIe connector graphics cards usually use. With a regular power supply, you need an adapter cable that combines two PCIe 8-pin leads into one CPU 8-pin. The card has no fan, just a very dense heatsink designed to be cooled by powerful server fans. In a regular case, you need to add a high-static-pressure fan or switch to a water block, because ordinary case fans can't push air through it.
When MIG is useful
MIG is the A100's partitioning feature: it splits one card into several “mini GPUs” whose memory and compute don't affect each other, which suits several people sharing a card. Practical splits are two 20GB slices, each running a 4-bit 27B, or three 10GB slices, each running a 4-bit 12B. Splitting all the way to seven slices leaves only 5GB each, enough only for small models like Gemma 4 E4B at 2-bit. If one person is running one model, you won't need it.
Compared with similar GPUs
A100 40GB PCIe vs A100 80GB PCIe, CMP 170HX 40GB, and L40S: price, VRAM, bandwidth, compute and local LLM speed compared.
| Spec | ||||
|---|---|---|---|---|
| Memory | 40 GB | 80 GB100% more | 40 GBSame | 48 GB20% more |
| Bandwidth | 1,555 GB/s | 1,935 GB/s24% higher | 1,560 GB/sSame | 864 GB/s44% lower |
| BF16 / FP16 compute | 312 TFLOPS | 312 TFLOPSSame | 202 TFLOPS35% lower | 362 TFLOPS16% higher |
| INT8 compute | 624 TOPS | 624 TOPSSame | 404 TOPS35% lower | 733 TOPS17% higher |
| Qwen3.8 27B speed (2-bit) | 101.6 t/s | 122.2 t/s20% faster | 101.9 t/sSame | 60.3 t/s41% slower |
| Largest dense model (4-bit) | 65B | 136B109% more | 65BSame | 79B22% more |
| Power | 250 W | 300 W20% higher | 250 WSame | 350 W40% higher |
| GPU interconnect | NVLink 600 GB/s | NVLink 600 GB/s | Not supported | Not supported |
| Interface | PCIe 4.0 x16 | PCIe 4.0 x16 | PCIe 2.0 x4 | PCIe 4.0 x16 |
| Released | 2020-06 | 2021-06 | 2021-09 | 2023-08 |
| eBay median |
As an eBay Partner, VRAMGlass may be compensated if you buy through the eBay links on this page. The price you pay stays the same.