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Why GPU Choice Matters for LLM Inference
Running large language models on dedicated GPU hosting means your GPU directly determines throughput, latency, and cost per token. A bad choice can mean 3x higher costs for the same workload. A good choice means you ship faster, spend less, and scale without re-architecting.
This guide cuts through marketing specs and gives you real tokens-per-second data from our UK-based servers. Every number here comes from actual inference runs on open source LLMs — LLaMA 3, Mistral 7B, and DeepSeek — using vLLM with identical system configurations.
For interactive GPU specs, see our GPU comparison tool.
Test Methodology
All GPUs tested on identical server configurations: AMD Ryzen 9 CPU, 64GB DDR5 RAM, 1TB NVMe, Ubuntu 22.04. Inference engine: vLLM 0.4.x with default settings. Each benchmark ran 1000 prompts of 256 input tokens, measuring output tokens per second at batch size 1 (latency) and batch size 8 (throughput).
We tested across three model families that represent the majority of production LLM deployments hosted on our dedicated GPU servers.
Tokens/sec Results by GPU
| GPU | VRAM | LLaMA 3 8B | Mistral 7B | DeepSeek 7B |
|---|---|---|---|---|
| RTX 3050 | 6 GB | 8 (4-bit) | 9 (4-bit) | 7 (4-bit) |
| RTX 4060 | 8 GB | 22 (4-bit) | 24 (4-bit) | 20 (4-bit) |
| RTX 4060 Ti | 16 GB | 31 | 33 | 29 |
| RTX 3090 | 24 GB | 42 | 45 | 40 |
| RTX 5080 | 16 GB | 68 | 72 | 65 |
| RTX 5090 | 32 GB | 95 | 100 | 88 |
Full benchmark data including batch throughput numbers is available on our tokens per second benchmark page.
VRAM Requirements by Model Size
| Model Size | FP16 VRAM | GPTQ 4-bit VRAM | Minimum GPU |
|---|---|---|---|
| 7B parameters | ~14 GB | ~4.5 GB | RTX 4060 Ti (FP16) / RTX 4060 (4-bit) |
| 13B parameters | ~26 GB | ~8 GB | RTX 5090 (FP16) / RTX 4060 (4-bit) |
| 34B parameters | ~68 GB | ~20 GB | Multi-GPU (FP16) / RTX 3090 (4-bit) |
| 70B parameters | ~140 GB | ~40 GB | Multi-GPU cluster |
VRAM is the single biggest constraint for LLM inference. A GPU with more VRAM lets you run larger models or serve more concurrent users. The RTX 3090’s 24GB remains the sweet spot for most 7B-13B deployments on open source LLM hosting setups.
Cost-Performance Rankings
Raw speed isn’t everything — you need to factor in monthly hosting cost. Using our cost per million tokens data:
| GPU | tok/s (LLaMA 3 8B) | Tokens/day | Cost per 1M tokens |
|---|---|---|---|
| RTX 3050 | 8 | ~691K | ~$0.043 |
| RTX 4060 | 22 | ~1.9M | ~$0.018 |
| RTX 3090 | 42 | ~3.6M | ~$0.008 |
| RTX 5080 | 68 | ~5.9M | ~$0.010 |
| RTX 5090 | 95 | ~8.2M | ~$0.011 |
The RTX 3090 wins on cost efficiency. For the full cost breakdown, use our LLM cost calculator.
Deploy Your LLM Today
RTX 3090 and RTX 5080 servers available for immediate deployment. Full root access, NVMe, 1Gbps — UK datacenter.
Browse GPU ServersRecommendations by Workload
Budget LLM inference (chatbots, internal tools):
- RTX 3090 — Best cost per token, 24GB VRAM handles most 7B-13B models
- See our RTX 3090 vs RTX 5090 comparison if you’re choosing between them
Low-latency AI APIs (real-time applications):
- RTX 5080 — Blackwell architecture delivers the fastest single-stream tokens/sec
- Ideal for AI chatbot hosting where response time matters
Large model serving (13B+ parameters):
- RTX 5090 — 32GB VRAM is the only single-GPU option for FP16 13B inference
- For 70B+ models, you’ll need multi-GPU clusters
All GPUs listed above are available on our dedicated GPU hosting platform with same-day deployment from our UK datacenter.
