Quick answer: The RTX 4090 is the best consumer GPU for LLM inference serving. For multi-user production workloads, dual RTX 4090s or a single RTX 5090 give you the VRAM and throughput to handle concurrent requests without degraded response times.
NVIDIA GeForce RTX 4090
24GB GDDR6XBest throughput-per-dollar for LLM serving — 24GB handles concurrent 7B-13B requests in production.
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Single-user vs multi-user serving
Running a model for yourself is different from serving it to a team or production environment. Concurrent users multiply VRAM and compute demands:
| Users | KV Cache Overhead | Recommended VRAM | Min GPU |
|---|---|---|---|
| 1 | ~1-2GB | Model + 4GB | RTX 4060 Ti 16GB |
| 2-5 | ~3-8GB | Model + 10GB | RTX 4090 24GB |
| 5-10 | ~8-16GB | Model + 20GB | RTX 5090 32GB |
| 10-20 | ~16-32GB | Model + 40GB | Dual GPU or A6000 |
| 20+ | 32GB+ | 48GB+ | Datacenter GPU |
Each concurrent request maintains its own KV cache. A 7B model that needs 5GB for one user might need 15GB to serve five users simultaneously.
Best GPUs for LLM serving
| GPU | VRAM | Bandwidth | Concurrent 7B Users | Concurrent 13B Users | Price |
|---|---|---|---|---|---|
| RTX 5090 | 32GB GDDR7 | 1,792 GB/s | 8-12 | 4-6 | ~$4,900 |
| RTX 4090 | 24GB GDDR6X | 1,008 GB/s | 5-8 | 2-4 | ~$2,200 |
| RTX 5080 | 16GB GDDR7 | 960 GB/s | 3-5 | 1-2 | ~$1,400 |
| RTX 4070 Ti Super | 16GB GDDR6X | 672 GB/s | 3-4 | 1-2 | ~$800 |
| RTX 3090 (used) | 24GB GDDR6X | 936 GB/s | 5-8 | 2-4 | ~$850 |
| 2x RTX 4090 | 48GB total | 2,016 GB/s | 12-18 | 6-10 | ~$4,400 |
| A6000 | 48GB GDDR6 | 768 GB/s | 10-15 | 5-8 | ~$3,500 |
Concurrent user estimates assume Llama 3 8B (Q4_K_M) or Llama 2 13B (Q4_K_M) with 2048 context length.
Serving frameworks compared
The framework you choose affects GPU utilization and throughput significantly:
| Framework | Best For | Multi-GPU | Continuous Batching | GPU Utilization |
|---|---|---|---|---|
| vLLM | Production serving | Yes | Yes (PagedAttention) | Excellent |
| TGI | Hugging Face models | Yes | Yes | Very good |
| Ollama | Personal/small team | Limited | No | Good |
| llama.cpp server | Lightweight deploy | No | Basic | Good |
| ExLlamaV2 | Maximum speed | No | Yes | Excellent |
For production multi-user serving, vLLM is the clear winner. Its PagedAttention algorithm manages GPU memory efficiently, allowing more concurrent users per GB of VRAM than any other framework. For framework-specific GPU sizing, see our best GPU for vLLM guide.
vLLM advantages for serving
- PagedAttention reduces KV cache waste by 60-80%, fitting more concurrent requests
- Continuous batching processes new requests without waiting for current ones to finish
- Tensor parallelism splits models across multiple GPUs seamlessly
- OpenAI-compatible API makes it a drop-in replacement for cloud endpoints
Server hardware beyond the GPU
| Component | Recommendation | Why |
|---|---|---|
| CPU | 8+ cores, Xeon or EPYC | Request handling and preprocessing |
| RAM | 64GB+ DDR5/DDR4 | Model loading buffer and OS overhead |
| Storage | 1TB+ NVMe SSD | Fast model loading, multiple model storage |
| Network | 1Gbps+ | Low-latency response delivery |
| PSU | 1000W+ (dual GPU: 1600W) | Headroom for sustained GPU load |
| Cooling | Server chassis or open rack | 24/7 operation generates sustained heat |
Unlike desktop LLM use, servers run GPUs at sustained load for hours. Cooling and PSU quality matter much more. If you also plan to fine-tune LLMs on the same hardware, size your VRAM for training — it far exceeds inference overhead.
Architecture patterns
Single GPU, single model (small team)
Best for 2-5 users sharing one model:
- RTX 4090 or RTX 5090
- Ollama or vLLM serving one model
- Simple, low maintenance
Single GPU, multiple models (internal tool)
Best for teams that need different models for different tasks:
- RTX 5090 (32GB) for model swapping headroom
- vLLM or Ollama with model switching
- Only one model active at a time
Dual GPU, high concurrency (production)
Best for 10+ concurrent users:
- 2x RTX 4090 with vLLM tensor parallelism
- Handles 13B models with many concurrent users
- Or run different models on each GPU
Cost comparison: self-hosted vs cloud
| Setup | Monthly Cost | Handles | Break-even |
|---|---|---|---|
| RTX 4090 server | ~$30 electricity | 5-8 concurrent users | ~3 months vs cloud |
| RTX 5090 server | ~$40 electricity | 8-12 concurrent users | ~6 months vs cloud |
| Cloud A100 (RunPod) | ~$800-1,200/month | Similar concurrency | Never (recurring) |
| OpenAI API (7B equiv) | ~$200-500/month | Pay per token | Never (recurring) |
Self-hosted breaks even fast if you have consistent usage. For occasional or bursty workloads, cloud may still make sense — and for a detailed breakdown of which cloud platform to choose, see RunPod vs Vast.ai for LLM. For a full break-even analysis with monthly cost tables, see our cloud GPU TCO vs self-hosted LLM guide.
Which GPU should you buy for your server?
- Serving 2-5 users on a budget? Get a used RTX 3090 ($850). The 24GB VRAM handles 7B models for a small team with Ollama, and self-hosted pays for itself in under 3 months versus cloud.
- Serving 5-15 users in production? Get an RTX 5090 ($4,900). The 32GB VRAM and 1,792 GB/s bandwidth handle concurrent 13B inference without KV cache pressure.
- Serving 15+ users or need high availability? Get dual RTX 4090s ($4,400) with vLLM tensor parallelism. The 48GB combined VRAM and continuous batching handle serious production traffic.
- Already have an RTX 4090? Add a second one instead of replacing it. Dual 4090s with vLLM outperform a single 5090 for concurrent workloads.
Common mistakes to avoid
- Underestimating KV cache overhead. A model that needs 5GB for one user can need 15-20GB for five concurrent users. Always plan VRAM for peak concurrency, not average.
- Using Ollama for production multi-user serving. Ollama lacks continuous batching and efficient KV cache management. Switch to vLLM once you exceed 3-5 concurrent users.
- Skipping PSU headroom for 24/7 operation. Desktop PSUs rated for peak loads can degrade under sustained server use. Budget 20-30% extra wattage above your GPU’s TDP.
- Choosing a 16GB card for a server. At 16GB, KV cache for even 3-4 concurrent users on a 7B model leaves almost no headroom. Server workloads demand 24GB minimum.
Our recommendation
| Scenario | GPU | Framework |
|---|---|---|
| Small team (2-5 people) | RTX 4090 | Ollama or vLLM |
| Medium team (5-15 people) | RTX 5090 or dual 4090 | vLLM |
| Production API | 2x RTX 4090 or A6000 | vLLM |
| Budget server | RTX 3090 (used) | Ollama or vLLM |
NVIDIA GeForce RTX 5080
16GB GDDR7Best new 16GB server card — 960 GB/s GDDR7 bandwidth and 250W TDP for efficient small-team serving.
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The RTX 4090 remains the best value for LLM serving in 2026. Its 24GB VRAM handles production workloads that 16GB cards cannot, and two of them in vLLM provide datacenter-class throughput at a fraction of A100 pricing.
For single-user setups, see our best GPU for Ollama guide. If you are building a server to back autonomous AI agents that send many sequential requests, concurrency headroom matters even more than for chatbot serving. For VRAM planning, check how much VRAM you need. Still deciding between owning the server or renting cloud capacity? Our cloud vs local GPU for LLM guide covers the trade-offs beyond just price.
The best inference server GPU is the one that fits your model, your user count, and your budget — in that order. Overprovisioning VRAM is cheaper than underprovisioning and hitting latency walls.