Best GPU VPS for LLM Inference in 2026: A VRAM-First Buyer's Guide
How to pick a GPU server for running open-weight LLMs: VRAM math, quantization trade-offs, and what monthly GPU rentals actually cost in 2026.
Renting a GPU server to run an open-weight language model is one of the few hosting decisions where a single spec dominates everything else: VRAM. Get it right and everything works; get it wrong and the model simply won't load. This guide walks through the decision in the order that actually matters.
Step 1: Start From the Model, Not the GPU
The VRAM a model needs is mostly arithmetic:
VRAM ≈ parameters × bytes-per-weight × 1.2
The 1.2 factor covers KV-cache and activation overhead at typical context lengths. Bytes-per-weight depends on precision: 2 bytes for FP16, roughly 0.5 bytes for 4-bit quantization.
That gives you a simple table:
| Model class | FP16 | 4-bit |
|---|---|---|
| 7–9B (Llama 3 8B class) | ~20GB | ~5GB |
| 13–14B | ~34GB | ~9GB |
| ~34B | ~82GB | ~21GB |
| 70B (Llama 3.3 70B class) | ~168GB | ~42GB |
Our GPU-for-LLM guide keeps this table wired to the live database, so each row shows the cheapest server currently clearing the bar.
Step 2: Decide on Quantization Honestly
4-bit quantization (GPTQ, AWQ, GGUF Q4 variants) cuts VRAM needs roughly 4× versus FP16, and for chat, summarization and RAG workloads the measurable quality loss is small. If your workload is precision-sensitive — heavy code generation, math, long-chain agent loops — benchmark 8-bit or FP16 against your own tasks before committing to the smaller card.
The practical consequence: a single 24GB card (RTX 4090 class) comfortably serves 4-bit 13B models and squeezes in some 34B variants, while 70B-class models at 4-bit want ~42GB — either one large-VRAM card or two 24GB cards with tensor parallelism in vLLM.
Step 3: Check the Supporting Cast
VRAM decides whether the model runs; the rest decides how well:
- CPU and RAM — model loading, tokenization and request queuing run on CPU. A strong GPU behind 2 weak cores will bottleneck on data movement. Look for at least 8 cores and RAM ≥ the model's disk size.
- Disk — model checkpoints are tens of GB each. NVMe matters at load and swap time.
- Bandwidth — for a private assistant it's irrelevant; for a public API endpoint, check for unmetered plans.
- OS images — Linux with recent NVIDIA drivers is the default; if you need Windows for tooling, filter for it explicitly.
Step 4: What This Actually Costs in 2026
Across the GPU plans we track, entry cards (GT/Quadro class, 1–4GB VRAM) start around $20–50/month — fine for CUDA experiments, too small for modern LLMs. The interesting band for inference is 16–24GB VRAM, and multi-GPU plans push effective VRAM into 70B territory for less than most people expect. Rather than quote numbers that will drift, two live views stay current:
- Best $/GB VRAM ranking — the value view across every tracked plan
- All GPU plans with VRAM filters — set
min VRAMto your target from Step 1
For a broader market view, the monthly price snapshot aggregates median prices per hosting type across the whole database.
Renting vs Paying Per Token
If your volume is spiky or small, LLM APIs will beat a rented GPU on cost — compare against the live LLM API pricing table. The crossover comes with sustained volume: a flat monthly server running a 4-bit 70B model serves tokens at a marginal cost of zero once rented. Estimate your monthly token throughput, price it against API rates, and the decision usually makes itself.
The Short Version
- Pick the model, compute VRAM at 4-bit and FP16.
- Filter GPU plans by that VRAM floor — do it here.
- Sanity-check CPU/RAM/disk.
- If the monthly rental beats your projected API bill, self-host; otherwise stay on APIs and revisit quarterly — GPU pricing moves fast.