Best GPU for LlamaIndex Workloads

Why LlamaIndex Workloads Need Dedicated GPUs

LlamaIndex turns your data into queryable knowledge bases powered by LLMs and embedding models. The two GPU-intensive operations are index construction, where thousands of documents are embedded, and query execution, where retrieved context is fed to an LLM for answer synthesis. Running both on a dedicated GPU server keeps your data private and eliminates per-query API costs.

GigaGPU’s LlamaIndex hosting platform provides bare-metal GPUs pre-configured for local LLM inference and embedding. Whether you are building a simple vector index or a complex multi-document agent, the GPU handles the heavy lifting while LlamaIndex manages the orchestration. For a direct framework comparison, see LangChain vs LlamaIndex.

Indexing Throughput Benchmarks

We indexed a 50,000-document corpus using LlamaIndex’s VectorStoreIndex with BGE-large-en-v1.5 embeddings. Throughput measures documents embedded and indexed per minute. Faster indexing means quicker iteration when rebuilding knowledge bases.

The RTX 3090 indexes 50K documents in under 10 minutes at a total compute cost of about $0.07. Even large corpora are cheap to process on dedicated hardware.

Query Engine Benchmarks by GPU

Query latency is the time from user question to final answer, including embedding the query, retrieving top-k results, and LLM generation. We used LlamaIndex’s RetrieverQueryEngine with vLLM serving LLaMA 3 8B and Qdrant for vector retrieval. Output length averaged 400 tokens.

For full token-level data across models, see our LLaMA 3 8B benchmark and Mistral 7B benchmark.

Cost per LlamaIndex Query

Self-hosted LlamaIndex queries cost a fraction of API-based alternatives. The table below shows per-query cost at sustained throughput, compared with typical OpenAI API pricing for equivalent token volumes.

See our cost per million tokens calculator and GPU vs API cost comparison for interactive breakdowns.

VRAM Guide for LlamaIndex Stacks

LlamaIndex deployments typically co-locate the embedding model and LLM on the same GPU. The table below shows common stack configurations and their VRAM footprints.

For multi-model setups, explore running multiple AI models simultaneously and multi-GPU cluster hosting.

LlamaIndex vs LangChain GPU Requirements

Both frameworks have similar GPU demands since the compute bottleneck is identical: LLM inference and embedding. LlamaIndex tends to be slightly more efficient for pure document QA because its query engines are optimised for retrieval-synthesis patterns with fewer intermediate LLM calls. LangChain excels at complex multi-tool agent chains that may require more sequential LLM invocations. See our full LangChain vs LlamaIndex comparison and best GPU for LangChain guide.

GPU Recommendations

Best overall: RTX 3090. Indexes 50K documents in 9 minutes and answers queries in under 7 seconds. The 24 GB VRAM supports FP16 7-8B models plus embedding models on a single card. At $0.45/hr, it is the cost-efficiency champion for LlamaIndex deployments.

Best for large knowledge bases: RTX 5090. The 32 GB VRAM and 2.2x throughput advantage make the 5090 ideal for production LlamaIndex deployments with concurrent users and sub-4-second query targets.

Best budget: RTX 4060. Handles quantised models for prototyping and internal tools. Indexing 50K docs takes 20 minutes, which is fine for development iteration.

Best mid-range: RTX 5080. With 16 GB VRAM and fast generation, the 5080 handles FP16 models and delivers query responses in around 5 seconds.

For vector storage options to pair with LlamaIndex, see our vector database comparison and dedicated hosting for ChromaDB, FAISS, and Weaviate.