Runpod
Our sponsor- On-Demand
- from $0.17
- Reserved
- on request
Compact single-slot professional GPU for entry-level AI and visualization.
Weekly median price per GPU per hour · Get the data
By provider shows one card per company, placed where its best offer ranked. By configuration lists every offer.
What you can rent comes first: in stock, then waitlist, then not reported, then out of stock. Priced offers rank ahead of quote-only, and on-demand ahead of other billing types.
Within a group, five factors set the order:
We match the provider, its country, GPU model, form factor, billing type, availability and instance name. Matching is partial and case-insensitive. Everyday words work too, so "interruptible" finds spot and "sold out" finds out of stock.
No providers match .
Heads up: A provider's own page may quote a different figure, for example on tax, region or a promotion. A monthly-only plan shows a derived hourly rate. Verify before provisioning. More on how we price.
One A4000 has 16 GB of VRAM. In practice, that's enough memory for roughly 8B parameters at 4-bit or 2B at 16-bit, assuming a 32K context. Below are some open-weight LLMs, with the estimated memory and GPUs each one needs.
| Model | Memory (INT4 / FP4) | A4000s needed | Cost /hr | Cost /mo |
|---|---|---|---|---|
|
|
19 GB
|
2
|
$0.38
|
$274
|
|
|
24 GB
|
2
|
$0.38
|
$274
|
|
|
68 GB
|
8
|
$1.52
|
$1,094
|
|
|
178 GB
|
13
|
–
|
–
|
|
|
241 GB
|
17
|
–
|
–
|
|
|
306 GB
|
22
|
–
|
–
|
|
|
1,545 GB
|
108
|
–
|
–
|
Estimates based on the median on-demand rate. Memory is weights plus FP16/BF16 KV cache at 32K context per request. GPU counts assume 90% of advertised VRAM is usable. No guarantee of runtime support, usable performance, or that a matching quantized build exists. Pricing methodology.
Nvidia A4000 · Per GPU
| Compute · dense | |
|---|---|
| FP16 / BF16 | 76.7 TFLOPS 153.4 with sparsity |
| FP32 | 19.2 TFLOPS |
| Precision support | FP16BF16TF32FP32INT4INT8 |
| Memory | |
|---|---|
| Capacity | 16 GB GDDR6 |
| Bandwidth | 448 GB/s |
| Bus width | 256-bit |
| ECC | Yes |
| Silicon | |
|---|---|
| Architecture | Ampere |
| Process | Samsung 8N |
| Transistors | 17.4 billion |
| Shader cores | 6,144 CUDA cores |
| Matrix cores | 192 Tensor cores |
| Compute units | 48 SMs |
| Fabric and host | |
|---|---|
| Host interface | PCIe 4.0 x16 |
| Power | |
|---|---|
| Board power | 140 W |
| Cooling | Active |
Source: official Nvidia A4000 datasheet.
As of October 3, 2026, the median on-demand price is $0.19 per GPU per hour across 8 providers with a priced on-demand config.
| Billing type | Configs | Median /GPU/hr | Cheapest |
|---|---|---|---|
On-demand | 41 | $0.19 | $0.08 (in stock, Vast.ai) |
Reserved | 31 | $0.18 | $0.06 (6 mo, in stock, Vast.ai) |
Spot | 9 | $0.06 (in stock, Vast.ai) |
No median for spot: we only show this when at least 3 providers list the GPU on that billing type. 1 provider also quotes the A4000 on a custom contract, priced per deal.
At 720 hours per month, one A4000 costs an estimated $137 at the median on-demand price. Cheapest verified in stock: $58 per month on-demand, $43 reserved (6 mo), $43 spot.
GetDeploying currently tracks A4000 configs from 13 providers. The cheapest verified in-stock on-demand configs come from Vast.ai, GPU.ai, Hyperstack and Runpod. See the full price comparison above for every provider and config.
As of October 3, 2026, the median on-demand price has fallen about 17% over the past 90 days to $0.19 per GPU per hour, though it is about the same as a year ago.
One A4000 runs models up to roughly 8B parameters at 4-bit quantization or 2B at 16-bit, assuming a 32K context. Larger models run across multiple GPUs: the model table above shows the estimated memory and GPU count for popular open-weight LLMs.
16GB GDDR6, compact single-slot professional GPU. Low power consumption. Suitable for entry-level AI development and visualization.
No NVLink. 16GB VRAM limits model sizes. For dedicated cloud AI inference, the L4 offers better throughput at lower cost.
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