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Best GPUs for AI Inference
Deploy AI models for inference, focus on cost-efficiency
LLM inference is more forgiving than training - you only need to fit the model weights, not gradients or optimizer states. Quantization (GGUF, AWQ, GPTQ) can reduce VRAM requirements by 50-75% with minimal quality loss. For production inference, throughput (tokens/second) and latency matter more than raw TFLOPS.
VRAM Requirements
Minimum:
8GB
Recommended:
24GB
Ideal:
48GB+
Software Requirements for AI Inference
GPU requirements vary by software. Here's what you need for popular applications:
| Software | Min VRAM | Recommended GPU | Notes |
|---|---|---|---|
| 7B FP16 | 14GB | RTX 4060 Ti 16GB | Full precision, good quality |
| 7B Q4_K_M | 6GB | RTX 3060 12GB | 4-bit quantized, ~95% quality |
| 13B Q4_K_M | 10GB | RTX 4070 12GB | Sweet spot for local LLMs |
| 70B Q4_K_M | 40GB | RTX 4090 + CPU offload | Partial GPU, rest on RAM |
| 70B FP16 | 140GB | 2x A100 80GB | Full precision, production quality |
Pro Tips
1
Use llama.cpp with GPU offloading for best performance on consumer hardware
2
vLLM and TGI provide optimized serving for production deployments
3
Speculative decoding can 2-3x throughput for compatible models
4
For chatbots, latency (time to first token) matters more than throughput
All Recommended GPUs
| GPU | Brand | VRAM | TFLOPS | Hardware | Cloud | Rating | Notes |
|---|---|---|---|---|---|---|---|
| L40S | NVIDIA | 48GB | 733 | $9k | $0.860/hr |
|
Top cloud inference choice |
| RTX 4090 | NVIDIA | 24GB | - | $2k | $0.235/hr |
|
Best price-performance for inference |
| H100 SXM | NVIDIA | 80GB | 1979 | $32k | $2.10/hr |
|
High-end inference |
| MI300X | AMD | 192GB | - | $18k | $1.99/hr |
|
High VRAM inference |
| A100 80GB | NVIDIA | 80GB | 312 | $12k | $1.15/hr |
|
General-purpose inference |