Best GPU for Inference: A Complete Guide

Inference -- running a trained model to generate predictions -- has very different requirements from training. You need low latency, high throughput, and cost efficiency per token or per request. This guide helps you pick the best GPU for your inference workload in 2026.

Inference vs Training: Key Differences

| Factor | Training | Inference |

|--------|----------|-----------|

| Compute pattern | Sustained, batch | Bursty, real-time |

| VRAM usage | Maximum (gradients + optimizer) | Lower (model weights only) |

| Key metric | Time to convergence | Latency + throughput |

| Cost metric | $/training run | $/1M tokens or $/1K requests |

| Uptime requirement | Can tolerate interruptions | Must be reliable |

Inference Benchmarks (March 2026)

LLM Inference: LLaMA 3 8B (vLLM, FP16)

| GPU | Latency (first token) | Throughput (tok/s, batch 16) | $/1M tokens |

|-----|----------------------|------------------------------|-------------|

| H100 80GB | 18ms | 3,200 | $0.22 |

| A100 80GB | 32ms | 1,600 | $0.33 |

| RTX 4090 | 28ms | 1,100 | $0.11 |

| L40S 48GB | 25ms | 1,400 | $0.18 |

| A10G 24GB | 45ms | 620 | $0.29 |

LLM Inference: LLaMA 3 70B (vLLM, INT4)

| GPU | Latency (first token) | Throughput (tok/s, batch 8) | $/1M tokens |

|-----|----------------------|----------------------------|-------------|

| H100 80GB | 35ms | 980 | $0.71 |

| A100 80GB | 68ms | 480 | $1.09 |

| 2x RTX 4090 | 55ms | 520 | $0.47 |

| L40S 48GB | 48ms | 640 | $0.58 |

Image Generation: Stable Diffusion XL (1024x1024)

| GPU | Time per image | Images/hour | $/1K images |

|-----|---------------|-------------|-------------|

| H100 80GB | 1.2s | 3,000 | $0.83 |

| A100 80GB | 2.4s | 1,500 | $1.26 |

| RTX 4090 | 3.1s | 1,161 | $0.38 |

| L40S 48GB | 2.0s | 1,800 | $0.69 |

Best GPU by Use Case

Low-Traffic API (< 100 requests/min)

**Recommendation: RTX 4090 ($0.39-0.44/hr)**

For low-traffic APIs serving models up to 13B parameters, the RTX 4090 offers the lowest cost per token. Its 24GB VRAM handles quantized 70B models or full-precision 7B-13B models easily.

Medium-Traffic API (100-1,000 requests/min)

**Recommendation: L40S or A100 80GB ($1.25-1.89/hr)**

When you need higher throughput and concurrent request handling, the A100 80GB or L40S provide the best balance. The L40S is particularly good for mixed inference workloads.

High-Traffic API (1,000+ requests/min)

**Recommendation: H100 80GB ($2.49/hr)**

For maximum throughput, the H100 with TensorRT-LLM delivers unmatched tokens per second, especially at high batch sizes. The cost per token actually becomes competitive at scale.

Batch Inference (Offline Processing)

**Recommendation: Vast.ai RTX 4090 spot ($0.14-0.19/hr)**

If latency does not matter and you are processing large datasets, use the cheapest available GPU. Spot RTX 4090s on Vast.ai are the most cost-efficient option.

Inference Optimization Tips

1. Use Quantization

Quantizing models to INT4 or INT8 reduces VRAM by 50-75% and often increases throughput:

```python

from transformers import AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained(

"meta-llama/Llama-3-8B",

load_in_4bit=True,

device_map="auto"

)

```

2. Use vLLM for LLM Serving

vLLM uses PagedAttention for 2-4x higher throughput than naive serving:

```bash

pip install vllm

python -m vllm.entrypoints.openai.api_server \

--model meta-llama/Llama-3-8B \

--dtype float16 \

--max-model-len 4096

```

3. Use TensorRT-LLM for Maximum Performance

For production H100 deployments, TensorRT-LLM can deliver 2-3x better throughput than vLLM.

4. Batch Requests

Accumulate requests and process them in batches. Even a batch size of 8 dramatically improves throughput per dollar.

Provider Recommendations for Inference

| Scenario | Provider | Why |

|----------|----------|-----|

| Serverless inference | RunPod Serverless | Pay-per-request, auto-scaling |

| Dedicated endpoint | RunPod or Lambda | Reliable uptime, good pricing |

| Budget inference | Vast.ai | Lowest hourly rates |

| Enterprise SLA | AWS SageMaker | 99.9% SLA, managed service |

The Bottom Line

For inference in 2026, the **RTX 4090 is king for cost efficiency** at low to medium traffic. The **H100 wins at high concurrency** where its massive memory bandwidth shines. Always use quantization and optimized serving frameworks like vLLM to maximize your throughput per dollar.

Find the cheapest inference GPU --> →