GPU Requirements for LLM Deployment: Hardware Planning Guide
In this guide
Running a language model on your own infrastructure requires more than choosing the newest GPU. Hardware planning starts with the workload: model size, precision, context length, requests per second, latency target, availability, and whether the system performs inference, fine-tuning, or training.
Estimate memory before buying hardware
VRAM must hold model weights, runtime overhead, and the key-value cache used during generation. Longer contexts and more concurrent conversations increase that cache. Quantisation can reduce memory use, but it changes quality, speed, and supported operations. Measure with the exact model and serving stack you plan to use.
Match the hardware to the job
| Workload | Planning focus |
|---|---|
| Interactive inference | Low latency, stable throughput, VRAM for expected context and concurrency |
| Batch processing | Throughput, scheduling, queue management, and cost per completed task |
| Embeddings and retrieval | Predictable batch performance, storage locality, and indexing throughput |
| Fine-tuning or training | Memory, interconnect bandwidth, dataset pipeline, checkpoints, and long job reliability |
Plan the whole serving system
GPU capacity is only one constraint. CPU, RAM, storage bandwidth, networking, model loading time, observability, failover, and security can determine the actual user experience. Containerise the service, set resource limits, capture metrics, and test failure recovery before exposing it to users.
Benchmark with real conversations
- Use representative prompts, retrieved context, and expected output lengths.
- Test peak concurrency and longer-than-average sessions.
- Record time to first token, total latency, error rate, throughput, and memory use.
- Include model reloads, node failures, and rate-limit or queue behaviour.
- Re-test after model, quantisation, driver, or serving changes.
Further reading: containerising LLM services, scaling RAG infrastructure, and embedding fundamentals.
Conclusion
Choose hardware from measured workload requirements, not model popularity. Start with a benchmark, include operational headroom, and keep an alternative serving path for maintenance or failure.
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