What Is AI Inference Hosting and Why Does It Matter?
AI inference hosting is the operational side of serving machine learning models to users or applications. It is the difference between a model that works on a Jupyter notebook and a model that powers a production API serving thousands of requests per second.
Inference hosting has different requirements than training. Training is throughput-bound: you want to process as many tokens as possible per dollar. Inference is latency-bound: users expect responses in under 500 milliseconds. The infrastructure choices that optimize training often hurt inference performance.
How Do Inference Hosting Options Compare?
| Option | Latency | Cost | Control | Best For |
|---|---|---|---|---|
| Managed API (OpenAI, Anthropic, Together) | 200-800ms | $0.50-15.00 per 1M tokens | None (black box) | Prototyping, variable workloads |
| Serverless GPU (Replicate, Banana, Modal) | 500ms-2s (cold start) | $0.50-3.00 per 1M tokens | Model choice, limited config | Low-traffic production, demos |
| Self-hosted on GPU cloud (BHK Cloud) | 50-200ms | $0.15/hr (fixed) | Full control | High-traffic, custom models, cost-sensitive |
| On-premise | 10-50ms | High upfront, low marginal | Full control | Ultra-low latency, data residency |
For production inference workloads with predictable traffic, self-hosted GPU cloud offers the best cost-performance ratio. A single RTX 3090 on BHK Cloud can serve a 7B-parameter model at 50-100 tokens per second with sub-200ms time-to-first-token. At $0.15/hr, that is $108/month for 24/7 inference capacity — equivalent to processing approximately 100 million output tokens per month.
How to Optimize Inference Performance on Cloud GPUs
Five techniques to maximize inference throughput and minimize latency on a single GPU:
- Model quantization. 4-bit quantization (GPTQ, AWQ) reduces VRAM usage by 75% with minimal accuracy loss. A 7B model that requires 14 GB in FP16 fits in 4 GB with 4-bit quantization, enabling larger batch sizes and higher throughput.
- Continuous batching. Frameworks like vLLM and TensorRT-LLM combine requests dynamically, maximizing GPU utilization. Continuous batching can increase throughput by 5-10x compared to static batching.
- KV cache optimization. The key-value cache for attention layers grows linearly with sequence length. PagedAttention (vLLM) and FlashAttention reduce KV cache memory by 50-80%, enabling longer context windows on the same hardware.
- Speculative decoding. A small draft model predicts tokens, and the large model verifies them in parallel. This can increase throughput by 2-3x for latency-bound workloads without quality loss.
- Warm starts and model caching. Keep the model loaded in GPU memory between requests. BHK Cloud's persistent instances mean you pay for the GPU whether it is serving or idle, so keeping the model warm is free.
What Does It Cost to Serve a Production Inference Endpoint?
| Traffic Level | Requests/Day | Tokens/Day | BHK Cloud Cost | Managed API Cost (OpenAI GPT-4o-mini) |
|---|---|---|---|---|
| Development | 100 | 50,000 | $3.60/day (1 GPU) | $0.01/day |
| Small production | 10,000 | 5,000,000 | $3.60/day (1 GPU) | $0.75/day |
| Medium production | 100,000 | 50,000,000 | $3.60/day (1 GPU) | $7.50/day |
| Large production | 1,000,000 | 500,000,000 | $7.20/day (2 GPUs) | $75.00/day |
| Scale (10M+ requests) | 10,000,000 | 5,000,000,000 | $36.00/day (10 GPUs) | $750.00/day |
The crossover point where self-hosted inference becomes cheaper than managed APIs is around 10,000 requests per day for a 7B model. Below that, managed APIs are cheaper because you pay per token. Above that, the fixed cost of GPU rental wins. At 1 million requests per day, self-hosted inference on BHK Cloud is 10x cheaper than managed API pricing.
Frequently Asked Questions
What inference servers are compatible with BHK Cloud?
Any inference server that runs on Linux with NVIDIA GPUs works: vLLM, TensorRT-LLM, Text Generation Inference (TGI), llama.cpp, Ollama, and custom FastAPI/PyTorch servers. BHK Cloud instances provide full root access — install and configure any software stack.
How do I handle scaling for variable traffic?
For variable traffic, combine a baseline of self-hosted GPU instances with a managed API fallback for traffic spikes. BHK Cloud instances handle your baseline load at $0.15/hr. When traffic exceeds capacity, route overflow to a managed API. This hybrid approach gives you the cost savings of self-hosting with the elasticity of serverless.
What about load balancing and failover?
BHK Cloud instances can be placed behind any HTTP load balancer (NGINX, HAProxy, Traefik). For multi-instance deployments, configure health checks and automatic failover. The low cost of additional instances ($0.15/hr) makes N+1 redundancy affordable.
Does BHK Cloud offer GPU instances optimized for inference?
BHK Cloud's RTX 3090 GPUs are well-suited for inference workloads. The 24 GB VRAM accommodates 7B-13B parameter models with quantization, and the GPU's 936 GB/s memory bandwidth provides ample throughput for high-traffic endpoints. For larger models, multiple GPUs can be combined with tensor parallelism.