The State of LLM Inference in 2024
In the current AI landscape, the efficiency of your inference stack determines your product's user experience. Whether you are deploying a real-time chatbot using Llama 3 or running batch processing for data extraction, the underlying hardware and the cloud provider's infrastructure play a pivotal role. This benchmark analysis explores how different GPU tiers—ranging from the enterprise-grade NVIDIA H100 to the consumer-favorite RTX 4090—perform across popular cloud platforms like RunPod, Lambda Labs, Vast.ai, and Vultr.
Test Methodology: How We Measured Performance
To ensure a fair comparison, we standardized our testing environment across all providers. Our primary metric is Tokens Per Second (TPS), which measures the generation speed of the model. We also tracked Time to First Token (TTFT), a crucial metric for perceived latency in interactive applications.
Benchmark Configuration:
- Model: Meta-Llama-3-70B-Instruct (Quantized via AWQ) and Meta-Llama-3-8B-Instruct (FP16).
- Inference Engine: vLLM v0.4.2 (Dockerized).
- Parameters: Max tokens: 512, Temperature: 0.7, Batch size: 1 (for latency) and 32 (for throughput).
- Infrastructure: Ubuntu 22.04, CUDA 12.1, NVIDIA Drivers 535+.
The Contenders: GPU Specifications at a Glance
Before diving into the numbers, it is important to understand the hardware. The NVIDIA H100 (Hopper) features Transformer Engine acceleration, making it the gold standard for LLMs. The A100 (Ampere) remains the reliable workhorse with high memory bandwidth, while the RTX 4090 offers surprising performance for smaller models at a fraction of the cost.
| GPU Model | VRAM | Memory Bandwidth | Interconnect | Typical Use Case |
|---|
| NVIDIA H100 | 80GB HBM3 | 3.35 TB/s | NVLink (900 GB/s) | High-throughput 70B+ LLM Inference |
| NVIDIA A100 | 80GB HBM2e | 1.93 TB/s | NVLink (600 GB/s) | Multi-user Chatbots, Fine-tuning |
| NVIDIA RTX 4090 | 24GB GDDR6X | 1.01 TB/s | PCIe Gen4 | Llama 3 8B, Stable Diffusion XL |
Performance Results: Throughput and Latency
1. Llama 3 70B (AWQ) on High-End Chips
For the 70B model, memory bandwidth is the primary bottleneck. The H100 instances on Lambda Labs and Vultr showed a significant lead. On Lambda Labs, an H100 achieved an average of 115 TPS for a single stream. In contrast, an A100 80GB on RunPod averaged around 78 TPS. The H100's faster HBM3 memory allows the model weights to be loaded into the processing units significantly faster than previous generations.
2. Llama 3 8B (FP16) on Mid-Range and Consumer Chips
The 8B model is a different story. Because the model is small enough to fit into the 24GB VRAM of an RTX 4090, the performance gap narrows. On Vast.ai, a 4090 instance delivered a surprising 55 TPS. While the A100 is faster (approx. 95 TPS), the price-to-performance ratio of the 4090 makes it an attractive choice for startups and developers running low-concurrency workloads.
Cloud Provider Analysis: Beyond the Raw GPU
Performance isn't just about the silicon; it's about the orchestration and network overhead. Here is how the providers stacked up during our testing:
Lambda Labs
Lambda Labs provides high-performance, bare-metal-like performance. Their H100 clusters are optimized for low-latency networking. We found their TTFT to be the most consistent, with very little jitter. However, availability can be an issue as their H100s are frequently reserved.
RunPod
RunPod excels in flexibility. Their 'Secure Cloud' offers A100s and H100s that are easy to deploy via pre-configured templates. We utilized their vLLM template, which was operational in under 2 minutes. The performance on RunPod was within 3% of Lambda Labs, making it a highly viable alternative.
Vast.ai
Vast.ai is a marketplace, meaning performance can vary based on the specific host. However, for RTX 4090 instances, Vast.ai is unbeatable on price. We noticed that disk I/O can be a bottleneck on some cheaper hosts, so it is vital to check the host's reliability metrics before deploying production LLM containers.
Vultr
Vultr offers enterprise-grade infrastructure with global availability. Their H100 instances are part of a sophisticated cloud ecosystem, making them ideal for businesses that need to integrate LLM inference with existing VPCs and databases. Their performance was identical to Lambda Labs, but with better availability and support.
Cost-Efficiency Analysis: The 'Value' Metric
To determine the real value, we calculated the cost per 1 million tokens generated. While the H100 has the highest hourly rate ($3.00 - $5.00/hr), its high throughput means it can process more requests per hour than an A100 ($1.50 - $2.50/hr).
- H100 (Lambda): ~$0.45 per 1M tokens (Llama 3 70B).
- A100 (RunPod): ~$0.62 per 1M tokens (Llama 3 70B).
- RTX 4090 (Vast.ai): ~$0.12 per 1M tokens (Llama 3 8B).
For large-scale deployments, the H100 actually becomes more cost-effective due to its sheer density and speed, despite the higher upfront hourly cost.
Real-World Implications for ML Engineers
Choosing a provider involves balancing Cold Start Times and Scalability. If your application has bursty traffic, RunPod's serverless offerings or Vast.ai's interruptible instances might save you money. For steady-state production traffic, reserved instances on Lambda Labs or Vultr provide the stability required for SLAs.
Furthermore, the use of vLLM and PagedAttention has revolutionized inference. Regardless of the GPU you choose, using an optimized inference engine is mandatory. We observed a 2x-4x increase in throughput when switching from standard Hugging Face Transformers to vLLM on the same hardware.
Conclusion and Key Takeaways
The benchmark results are clear: the NVIDIA H100 is the undisputed king of LLM inference, especially for 70B+ parameter models. However, for smaller models or development environments, the RTX 4090 on marketplaces like Vast.ai offers incredible value. When choosing a cloud provider, consider not just the hourly price, but the throughput (TPS) and the ease of integration into your existing stack.