Rent GPU for AI: VRAM Requirements Guide for Every Major Model (2026)

To rent GPU for AI, start with VRAM: a 70B model at Q4_K_M needs roughly 40 GB, covered by the packet.ai L40S at $0.92/hr. The same model at FP16 needs 140 GB, requiring the H200 (coming soon) or an H100 (coming soon). Pick the wrong GPU and you either OOM on launch or burn budget on every hour.

VRAM is the hard constraint in AI inference. Your model either fits or it does not. Picking the right GPU before you deploy saves you from OOM crashes mid-run, from oversizing a cluster and burning budget on idle memory, and from figuring out why a 70B model that should fit on a 48 GB GPU is failing at 60 GB of actual usage.

This guide maps every major model to exact VRAM requirements and the packet.ai GPU that covers it at the lowest cost. It sits alongside the L40S pricing and throughput deep-dive and the GPU selection guide for ComfyUI and image generation in the packet.ai inference cluster. To skip GPU management entirely, packet.ai Token Factory serves Llama 3, DeepSeek, and Qwen models on an OpenAI-compatible API — no VRAM sizing or GPU provisioning required. For a direct rate comparison against AWS, CoreWeave, and Lambda Labs, see the pricing page.

LLM VRAM Calculator: How to Size Any Model Before You Rent GPU for AI

The formula that covers most production cases:

Total VRAM = model weights + KV cache + framework overhead + 20% headroom

packet.ai L40S 48 GB starts at $0.92/hr dedicated versus $3.50/hr on AWS for equivalent capacity.

For a team running continuous 70B Q4 inference, the $0.92/hr versus $3.50/hr gap compounds to over $20,000 saved per GPU per year on packet.ai compared to AWS.

VRAM Requirements for Every Major LLM in 2026

All figures include a KV cache estimate at 4K context with 4 concurrent users. For high-concurrency or long-context workloads, add KV cache for your actual peak concurrency on top.

Sources: VRLA Tech LLM Requirements Guide (June 2026), Spheron GPU Memory Requirements (May 2026), Will It Run AI (April 2026). GPU prices verified against the packet.ai pricing page, July 16, 2026.

Llama 4 Scout and MoE Models: Why Active Parameters Do Not Equal VRAM

Llama 4 Scout has 109B total parameters but only 17B active per token. All 109B must load into VRAM at startup. This is why Scout at Q4 needs 55-60 GB, not the 9 GB you would expect from 17B active parameters.

DeepSeek V3 (671B total, 37B active) requires approximately 671 GB at FP8 for weights alone, making a full B200 cluster the minimum viable deployment. The distilled variant, DeepSeek-R1-Distill-32B, needs approximately 20 GB at Q4 and runs on a single L40S 48 GB, carrying most of full R1 reasoning capability at $0.92/hr.

vLLM GPU Requirements for Production LLM Inference Serving

vLLM holds model weights plus peak KV cache across all concurrent requests simultaneously. Teams using TGI or SGLang see similar VRAM profiles. Size for peak concurrency, not average load.

Which GPU to Rent for AI: Full packet.ai Lineup by Workload and VRAM

Live now: L40S, A100, RTX 6000 Pro, and B200. H100 SXM and H200 are coming soon — join the waitlists below.

Best GPU for LLM Inference: Workload-to-Hardware Matching Table

What It Costs to Rent GPU for AI: packet.ai vs AWS vs CoreWeave

Live on packet.ai: L40S $0.92/hr dedicated, A100 80 GB $1.43/hr dedicated, B200 $3.75/hr Dynamic / $5.90/hr Dedicated, RTX 6000 Pro $0.66/hr Dynamic. H100 SXM and H200 are coming soon. AWS EC2 charges approximately $6.88/hr per H100 GPU on P5 instances, based on the AIMultiple GPU Rental Price Index (July 2026). packet.ai charges no egress fees and no minimum commitment on on-demand instances.

VRAM Requirements for Fine-Tuning vs Inference: QLoRA vs Full Training

Fine-tuning needs approximately 3-4x more VRAM than inference of the same model. The training loop stores model weights, gradients (same size as weights at FP16), Adam optimizer states (2x weights), and activations simultaneously.

A 70B QLoRA run fits on a single A100 80 GB at $1.43/hr at approximately 41 GB peak — no cluster required. Full FP16 fine-tuning of 70B exceeds 570 GB and requires an 8x H100 or B200 cluster.

Don't want to size VRAM or provision GPUs? packet.ai Token Factory serves open LLMs on an OpenAI-compatible API — no infrastructure required.

Frequently asked questions

Last reviewed: July 16, 2026. All prices verified against the packet.ai pricing page. Live now: L40S $0.92/hr, A100 $1.43/hr, B200 $3.75/hr Dynamic / $5.90/hr Dedicated, RTX 6000 Pro $0.66/hr Dynamic. H100 SXM and H200 coming soon — join the waitlist. For multi-node deployments, see packet.ai GPU clusters.