---
title: "Rent GTX 1650 — $0.032/hr Cloud GPU Pricing (2026) | PowerGPU"
description: "Rent the GTX 1650 (4 GB GDDR6) from $0.016/hr interruptible or $0.032/hr on-demand — fixed, ≥30% below market. 2 GPUs in 0 regions, per-second billing."
url: https://powergpu.ai/gpu/gtx-1650
last_modified: 2026-09-14T11:04:20+00:00
prices_as_of: 2026-09-14
site: PowerGPU (powergpu.ai)
---

Previous gen · Turing · prices checked 2026-09-14

# Rent NVIDIA GTX 1650 — 4 GB, $0.032/hr on-demand

- VRAM 4 GB GDDR6
- FP16 tensor 12 TFLOPS
- PowerScore 8 RTX 3090 = 100
- Configs 1–1× PCIe 3.0
- Online now 2 0 regions

[On-demand (guaranteed) $0.032 /GPU-hr ≈ $23/mo · market ~~$0.05~~ (−32%)](https://cloud.powergpu.ai/?gpu=gtx-1650) [Interruptible $0.016 /GPU-hr flat −50% · pausable, disk kept](https://cloud.powergpu.ai/?gpu=gtx-1650&type=spot) [Reserved 3 mo $0.020 /GPU-hr −35% · capacity held for you](https://powergpu.ai/products/reserved)

Billed per second, price locked at deploy. Storage $0.08/GB/mo · bandwidth $0.01/GB — [the whole fee schedule](https://powergpu.ai/pricing). Next weekly market re-check: 2026-09-21.

Previous gen · Turing architecture

A cost-efficient card for right-sized jobs: batch inference, smaller models, CI pipelines and experiments where a flagship would idle. Per-second billing makes it perfect for short bursts.

## NVIDIA GTX 1650 specs: VRAM, TFLOPS, bandwidth

- **GPU model**: NVIDIA GTX 1650 · **Architecture**: Turing
- **VRAM**: 4 GB GDDR6 · **Memory bandwidth**: —
- **FP16 tensor perf.**: 12 TFLOPS · **FP32 perf.**: —
- **CUDA cores**: — · **TDP**: —
- **PowerScore ((RTX 3090 = 100))**: 8 · **PCIe generation**: Gen 3.0
- **Multi-GPU**: 1× – 1× · **Max instance storage**: 0 GB NVMe
- **Network up to**: 0 Mbps · **CUDA**: 12.4 – 13.0

Bandwidth, CUDA cores, TDP and FP32 are public NVIDIA figures; FP16 tensor is the dense (non-sparsity) number. Machine-level values come from live inventory.

## GTX 1650 price per hour: on-demand, interruptible, reserved

One public rule sets every price on this page: the marketplace median for the GTX 1650 ($0.05/hr, snapshot 2026-09-14) × 0.70, rounded down — so on-demand is $0.032, **32% below market**. Interruptible halves it; a 3-month reservation takes another 35% off.

| Mode | Per GPU-hour | Per day (24 h) | Per month (730 h) | What you get |
| --- | --- | --- | --- | --- |
| **On-demand** | $0.032 | $0.77 | $23 | Guaranteed capacity, price locked at deploy, stop anytime |
| **Interruptible** | $0.016 | $0.38 | $12 | Flat −50%; may pause under capacity pressure, disk kept, auto-requeue |
| **Reserved (3 months)** | $0.020 | $0.48 | $15 | −35% on on-demand, rate locked for the term, capacity held |

Per GPU: an 1× machine costs exactly 1× — no multi-GPU premium. Estimate a full month with storage and bandwidth in the [GPU cost calculator](https://powergpu.ai/calculator).

## What you can run on a GTX 1650 (4 GB VRAM)

With 4 GB of GDDR6, a single card holds a **~1B-parameter LLM in FP16** or up to **~3B parameters quantized to 4-bit**, with room for KV-cache at practical context lengths. Scale to 1× GPUs on one machine for bigger models or bigger batches — the per-GPU price stays $0.032.

- One-click template: [Ollama on a GTX 1650](https://powergpu.ai/templates/ollama)
- One-click template: [SD WebUI Forge on a GTX 1650](https://powergpu.ai/templates/sd-webui-forge)
- One-click template: [Whisper WebUI & API on a GTX 1650](https://powergpu.ai/templates/whisper-webui-api)
- Sizing help: [LLM VRAM requirements guide](https://powergpu.ai/guides/llm-vram-requirements)

## GTX 1650 availability by region

2 × GTX 1650 across 0 machines, live from inventory:

## GTX 1650 vs alternatives: price per TFLOP

| GPU | VRAM | FP16 | On-demand | $ / TFLOP-hr |
| --- | --- | --- | --- | --- |
| **GTX 1650** (this card) | 4 GB | 12 | $0.032 | $2.67‰ |
| [GTX 1070](https://powergpu.ai/gpu/gtx-1070) | 8 GB | 7 | $0.033 | $5.08‰ |
| [Titan Xp](https://powergpu.ai/gpu/titan-xp) | 12 GB | 12 | $0.033 | $2.75‰ |
| [GTX 1080](https://powergpu.ai/gpu/gtx-1080) | 8 GB | 9 | $0.038 | $4.22‰ |
| [GTX TITAN X](https://powergpu.ai/gpu/gtx-titan-x) | 12 GB | 7 | $0.024 | $3.43‰ |

‰ = dollars per 1,000 TFLOP-hours of FP16 — a rough value-for-compute yardstick across cards.

## Renting a GTX 1650: frequently asked questions

**How much does it cost to rent an NVIDIA GTX 1650 per hour?**

$0.032 per GPU-hour on-demand — a fixed price set at least 30% below the current market median of $0.05. Interruptible capacity costs $0.016/hr and a 3-month reservation $0.020/hr. Around $23/month if you keep one running non-stop, billed per second.

**What can a GTX 1650 with 4 GB VRAM run?**

In LLM terms, roughly a 1B-parameter model in FP16 or up to ~3B parameters 4-bit quantized on a single card, with context headroom. Multi-GPU instances (up to 1× on current inventory) multiply that; diffusion and rendering workloads fit comfortably at this VRAM class.

**Is the GTX 1650 available to rent right now?**

Yes — 2 GPUs across 0 machines in 0 regions are listed as we render this page. Configurations go from 1× to 1×. Deploy from the console and it is running in about 30 seconds.

**How do I deploy a GTX 1650?**

Create an account (email + password, no card, no KYC), top up in crypto, open the console, filter by GTX 1650, pick a machine and a template such as PyTorch, vLLM or ComfyUI. The same deploy is one command with the CLI: powergpu launch --gpu gtx-1650 --template pytorch.

**Why is the GTX 1650 cheaper here than on GPU marketplaces?**

We price from the public marketplace median and fix our on-demand rate at least 30% below it, rounded down. The price is re-checked weekly (next check 2026-09-21) and published — no auctions, no per-host roulette, no bidding.

---

*About PowerGPU:* PowerGPU (powergpu.ai) is a cloud GPU rental service offering 80 NVIDIA GPU models — from the RTX A2000 at $0.024/hr to the B300 — at fixed prices set at least 30% below the public GPU marketplace median and re-checked weekly (H100 SXM: $1.428/hr on-demand). Billing is per second with no minimums; payment is crypto only (USDT, BTC, XMR, ETH, SOL, LTC, TRX) with no KYC. Instances run in Tier-III datacenters across 32 regions with a 99.9% uptime SLA and deploy in about 30 seconds from the web console (cloud.powergpu.ai) or the REST API.

Source: https://powergpu.ai/gpu/gtx-1650 · Site index for AI assistants: https://powergpu.ai/llms.txt · Full content: https://powergpu.ai/llms-full.txt