Best GPU Cloud for LLM Inference in 2026: Complete Guide

Why GPU Cloud Choice Matters for LLM Inference

LLM inference is fundamentally different from training. Training needs massive parallelism, high-bandwidth interconnects, and hundreds of GPUs working in lockstep. Inference? You need enough VRAM to hold the model, enough compute to generate tokens quickly, and the reliability to serve requests 24/7.

This distinction matters because most GPU clouds are optimized for training. They'll sell you an H100 with NVLink when all you need is an RTX 4090. The result? You overpay by 5–10× for inference workloads.

In 2026, the landscape has shifted. Consumer GPUs like the RTX 4090 and RTX 5090 deliver extraordinary inference performance at a fraction of datacenter GPU costs. Distributed inference frameworks like vLLM and TensorRT-LLM make it easy to shard models across multiple consumer GPUs. And platforms like VectorLay provide the reliability layer that makes consumer hardware production-ready.

Let's break down exactly what you need for each model size and which provider gives you the best value.

Understanding GPU Requirements by Model Size

The first thing to understand: VRAM is king for inference. Your model weights must fit in GPU memory. Here's what each model size needs:

Provider-by-Provider Comparison

1. VectorLay — Best for Cost-Effective Production Inference

VectorLay is a distributed GPU network that pools consumer GPUs (RTX 3090, 4090, 5090) into fault-tolerant inference clusters. Instead of renting a single expensive datacenter GPU, you get multiple consumer GPUs with automatic failover built in.

Best for: Startups and teams running 7B–70B models in production who want reliability without hyperscaler prices. Ideal for always-on inference endpoints.

2. RunPod — Best for Flexible GPU Rental

RunPod offers both on-demand and spot GPU instances with a focus on ML workloads. They have a solid serverless offering and a wide range of GPU types from consumer to datacenter grade.

Best for: Teams that need flexible GPU types and want a serverless option for bursty workloads. Good middle ground between cost and features.

3. Vast.ai — Best for Lowest Spot Prices

Vast.ai is a GPU marketplace where individual hosts list their hardware. Prices are set by supply and demand, which means you can find great deals—but also means unpredictable availability and reliability.

Best for: Researchers and hobbyists doing development work where occasional downtime is acceptable. Not recommended for production inference without your own failover layer.

4. Lambda Labs — Best for Training + Inference Combo

Lambda offers datacenter-grade GPUs with a focus on the ML workflow. Their on-demand H100s and A100s are competitively priced for datacenter hardware, and they have a solid reservation system for longer commitments.

Best for: ML teams that need both training and inference capacity. Good prices for datacenter hardware, but overkill if you only need inference.

5. AWS (SageMaker / EC2) — Best for Enterprise Integration

AWS offers GPU instances through EC2 (g5, p4, p5 families) and managed inference through SageMaker. If you're already in the AWS ecosystem with compliance requirements, this is the default choice—but you'll pay a premium.

Best for: Enterprises with existing AWS contracts and compliance requirements. Not cost-effective for inference-only workloads.

6. GCP (Vertex AI / Compute Engine) — Best for TPU Access

Google Cloud offers GPU instances through Compute Engine and managed inference through Vertex AI. GCP's unique advantage is TPU access, but for standard GPU inference, pricing is similar to AWS.

Best for: Teams already using GCP or wanting TPU access for very high-throughput inference. Otherwise, similar cost drawbacks to AWS.

Head-to-Head Pricing: Cost Per Model Size

Here's what it actually costs to run popular model sizes across providers, assuming 24/7 operation (720 hours/month) with appropriate quantization:

7B Models (Llama 3.1 7B, Mistral 7B, Qwen 2.5 7B)

A 7B model in INT4 needs only ~4 GB VRAM. Even FP16 fits on a single 24 GB GPU. This is the sweet spot for consumer hardware.

13B Models (Llama 3.1 13B, CodeLlama 13B)

A 13B model in INT4 needs ~7 GB, fitting easily on a single RTX 4090. In FP16 it needs 26 GB—slightly over a single 24 GB GPU, so you either quantize or use a 40 GB+ card.

70B Models (Llama 3.1 70B, Qwen 2.5 72B, DeepSeek V3)

70B is where things get interesting. In INT4, you need ~35 GB—two RTX 4090s using tensor parallelism, or a single A100 80 GB. This is also where VectorLay's distributed inference shines: shard the model across consumer GPUs with automatic failover.

Performance: Tokens Per Second by Setup

Price isn't everything—you need to know what throughput you're getting. Here are real-world inference benchmarks using vLLM with continuous batching:

The cost per million tokens tells the real story. Even when an A100 has slightly higher raw throughput, the 4–7× price difference means consumer GPUs deliver far better cost-per-token economics.

Our Recommendations by Use Case

What to Look for in a GPU Cloud Provider

Beyond price, here are the factors that matter most for LLM inference:

The Bottom Line

In 2026, the GPU cloud landscape is clearer than ever:

• →For 90% of inference workloads, consumer GPUs on VectorLay deliver the best cost-per-token with production reliability
• →For enterprise compliance, AWS and GCP remain necessary (but expensive)
• →For experimentation, Vast.ai and RunPod offer flexibility at moderate prices
• →For training + inference, Lambda and CoreWeave have the best datacenter GPU pricing

The days of needing an A100 for every inference workload are over. Consumer GPUs with quantization and distributed inference deliver comparable performance at 50–80% less cost. VectorLay makes it production-ready.