USA
4 configs
1x
Nvidia T1000
Compact Turing professional card for basic visualization.
- Arch
- Turing
- Memory
- 4 GB GDDR6
- Bandwidth
- 160 GB/s
- Released
- Q2 2021
Price history
Weekly median price per GPU per hour
Aggregating historical prices...
At a glance Updated 2 hours ago
- Cheapest in stock
- --
- Median price (current)
- --
- 90-day trend
- --
- Coverage
- 1 provider
- 4 price points
Pricing & availability
How this list works
The two views
By provider shows one card per company, placed where its best offer ranked. By configuration lists every offer.
Order
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:
- Location: datacenter proximity, blended with provider HQ.
- Price: hourly price, per GPU and in total.
- Billing type: reserved ahead of spot, spot ahead of quote-only.
- Specs: more VRAM, vCPUs and RAM.
- Provider diversity: a provider's repeat rows rank slightly lower, so one company can't take all the top spots.
Search
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.
Transparency and funding
- Ads and sponsors: Paid placements sit at the top of the list and are always labeled as sponsored content. Sponsorship never influences the organic ranking itself.
- Affiliates: Affiliate links are marked. We may earn a commission if you click them, but commissions never affect the order.
- Prices: Shown in USD, converted at daily reference rates where a provider publishes in another currency. A month means 720 hours. How we estimate costs.
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.
What can the T1000 run?
One T1000 has 4 GB of VRAM. Even at 4-bit, the weights of most modern AI models exceed the card's usable VRAM, and context adds KV cache on top. Below are some open-weight LLMs, with the estimated memory and GPUs each one needs.
Multi-node
| Model | Memory (INT4 / FP4) | T1000s needed | Cost /hr | Cost /mo |
|---|---|---|---|---|
Qwen3.8-27B
27B
|
19 GB
|
6
|
–
|
–
|
Gemma 4 31B
30.7B
|
24 GB
|
7
|
–
|
–
|
GPT-OSS-120B
117B · 5.1B active
|
68 GB
|
19
|
–
|
–
|
GLM-5.3-Flash
320B · 18B active
|
178 GB
|
50
|
–
|
–
|
MiniMax-M3
428B · 23B active
|
241 GB
|
68
|
–
|
–
|
DeepSeek V4.1 Flash
552B · 16B active
|
306 GB
|
85
|
–
|
–
|
Kimi K3
2.8T · 104B active
|
1,545 GB
|
430
|
–
|
–
|
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. How we estimate costs.
Technical Specifications
Nvidia T1000 · Per GPU
| Compute · dense | |
|---|---|
| FP32 | 2.5 TFLOPS |
| Precision support | FP16FP32INT8 |
| Memory | |
|---|---|
| Capacity | 4 GB GDDR6 |
| Bandwidth | 160 GB/s |
| Bus width | 128-bit |
| Silicon | |
|---|---|
| Architecture | Turing |
| Shader cores | 896 CUDA cores |
| Compute units | 14 SMs |
| Fabric and host | |
|---|---|
| Host interface | PCIe 3.0 x16 |
| Power | |
|---|---|
| Board power | 50 W |
| Cooling | Active |
Source: official Nvidia T1000 datasheet.
Frequently Asked Questions
How much does the T1000 cost per hour?
As of September 14, 2026, we track 4 configs from 1 provider, none of them on demand. Prices are per GPU per hour.
| Billing type | Configs | Cheapest |
|---|---|---|
Reserved | 4 | $0.14 (24 mo) |
No median for reserved: we only show this when at least 3 providers list the GPU on that billing type.
Where can I rent a T1000?
GetDeploying currently tracks T1000 configs from 1 provider. See the full price comparison above for every provider and config.
How many T1000 GPUs do I need?
More than one: even at 4-bit, the weights of most modern AI models exceed one T1000's usable VRAM, and context adds KV cache on top. Larger models run across multiple GPUs: the model table above shows the estimated memory and GPU count for popular open-weight LLMs.
Why choose the T1000?
4GB or 8GB GDDR6. Compact Turing professional GPU for entry-level visualization and light compute.
When is the T1000 not a good fit?
Very limited compute. Only viable for multi-display setups, basic visualization, or lightweight video processing.
Alternatives to Nvidia T1000
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