RunPod GPUs vs VectorLay GPUs: Full Comparison (2026)
Which GPUs can you actually rent on RunPod vs VectorLay? The answer matters more than you might think. GPU availability, VRAM capacity, and pricing per model directly impact which models you can run and how much you'll pay. This guide compares every GPU on both platforms side by side.
GPU Availability: Which GPUs Can You Rent?
RunPod and VectorLay take different approaches to GPU selection. RunPod focuses on data-center hardware with a broad range of enterprise GPUs. VectorLay specializes in high-performance consumer GPUs that deliver exceptional price-to-performance for inference workloads.
| GPU | VRAM | VectorLay | RunPod |
|---|---|---|---|
| RTX 4090 | 24GB GDDR6X | ||
| RTX 4080 | 16GB GDDR6X | ||
| RTX 4070 Ti | 12GB GDDR6X | ||
| RTX 3090 | 24GB GDDR6X | ||
| RTX 3080 | 10GB GDDR6X | ||
| H100 | 80GB HBM3 | ||
| A100 | 80GB HBM2e | ||
| A40 | 48GB GDDR6 | ||
| L40 | 48GB GDDR6 | ||
| RTX A6000 | 48GB GDDR6 |
RunPod has the broader GPU catalog, especially in the data-center tier. If you need an H100 or A100 for large model training or 70B+ parameter inference, RunPod is the obvious choice. VectorLay focuses on consumer GPUs where the price-to-performance ratio is highest for inference—and offers mid-range options like the RTX 4080, RTX 4070 Ti, and RTX 3080 that RunPod doesn't carry.
Performance Benchmarks: Tokens per Second
For inference workloads, the metric that matters most is throughput: how many tokens per second can a GPU generate for a given model? Both platforms provide bare-metal GPU access (VectorLay via VFIO passthrough, RunPod via Docker), so performance on the same GPU model is effectively identical. The differences come from the GPU hardware itself.
Below are representative benchmarks for common inference workloads using vLLM. These numbers reflect single-GPU performance with typical batch sizes.
| Model | RTX 4090 | RTX 3090 | H100 | A100 |
|---|---|---|---|---|
| Llama 3.1 8B (FP16) | ~95 tok/s | ~62 tok/s | ~165 tok/s | ~120 tok/s |
| Llama 3.1 70B (AWQ 4-bit) | ~28 tok/s | ~18 tok/s | ~72 tok/s | ~52 tok/s |
| Mistral 7B (FP16) | ~105 tok/s | ~68 tok/s | ~180 tok/s | ~130 tok/s |
| SDXL (512x512, steps=30) | ~4.2 img/s | ~2.8 img/s | ~6.5 img/s | ~4.8 img/s |
| Whisper Large v3 | ~32x RT | ~22x RT | ~48x RT | ~38x RT |
Benchmarks are approximate and vary based on batch size, quantization, and framework version. "tok/s" = output tokens per second (single request). "RT" = realtime factor for audio transcription. All benchmarks use vLLM or optimized inference engines.
Key Takeaway
On the same GPU model, performance is identical between VectorLay and RunPod—a 4090 is a 4090 regardless of platform. The question is: why pay more for the same performance? VectorLay's RTX 4090 delivers the same throughput at 34% lower cost.
Pricing Per GPU Model
Here's how pricing compares across every GPU available on both platforms. For GPUs exclusive to one platform, pricing is shown only for that provider.
| GPU | VectorLay | RunPod | Savings |
|---|---|---|---|
| RTX 4090 (24GB) | $0.49/hr | $0.74/hr | 34% |
| RTX 4080 (16GB) | $0.39/hr | -- | -- |
| RTX 4070 Ti (12GB) | $0.29/hr | -- | -- |
| RTX 3090 (24GB) | $0.29/hr | $0.44/hr | 34% |
| RTX 3080 (10GB) | $0.19/hr | -- | -- |
| H100 (80GB) | -- | $3.49/hr | -- |
| A100 (80GB) | -- | $1.64/hr | -- |
| A40 (48GB) | -- | $0.76/hr | -- |
| L40 (48GB) | -- | $0.89/hr | -- |
| RTX A6000 (48GB) | -- | $0.79/hr | -- |
Prices as of February 2026. RunPod on-demand pricing shown; community cloud may be lower. VectorLay pricing is flat-rate with no hidden fees.
VRAM Guide: Which GPU for Your Workload?
The most important spec for inference is VRAM—it determines which models you can load. Here's a practical guide to choosing the right GPU based on what you're running.
10-12GB VRAM (RTX 3080, RTX 4070 Ti)
Best for: Stable Diffusion, Whisper, small LLMs (up to 7B at 4-bit quantization)
Available on: VectorLay only. These budget-friendly options are ideal when your model fits in limited VRAM and you want the lowest possible cost.
16GB VRAM (RTX 4080)
Best for: SDXL with high resolution, 7-13B LLMs at FP16, medium-complexity image generation
Available on: VectorLay only. A sweet spot between cost and VRAM for workloads that don't need a full 24GB.
24GB VRAM (RTX 4090, RTX 3090)
Best for: LLMs up to 34B at FP16, 70B at 4-bit quantization, high-resolution image generation, most production inference
Available on: Both platforms. The RTX 4090 is the most popular inference GPU and VectorLay offers it at 34% less than RunPod.
48GB VRAM (A40, L40, RTX A6000)
Best for: 70B LLMs at FP16, large multimodal models, batch inference with high concurrency
Available on: RunPod only. If you need 48GB of VRAM without stepping up to the H100/A100 price tier, these workstation-class GPUs are the sweet spot.
80GB VRAM (H100, A100)
Best for: 70B+ LLMs at FP16, model training, massive batch inference, multi-tenant serving
Available on: RunPod only. The H100 and A100 are the only option for workloads that truly need 80GB of high-bandwidth memory.
The 24GB Sweet Spot
The vast majority of production inference workloads—Stable Diffusion, Whisper, LLMs up to 34B parameters, and quantized 70B models—fit comfortably within 24GB of VRAM. If your workload fits in 24GB, the RTX 4090 on VectorLay gives you the best combination of performance and price. No need to pay data-center GPU premiums for inference that runs perfectly on consumer hardware.
This is a GPU-focused comparison. Read the full VectorLay vs RunPod comparison for details on pricing, reliability, security, and feature differences.
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GPU availability and pricing accurate as of February 2026. Cloud pricing changes frequently—always verify current rates on provider websites. RunPod is a trademark of RunPod, Inc. Benchmarks are representative and may vary based on configuration.