Training gets the headlines. Inference pays the bills.
Over 90% of production ML compute is inference — every API call, every chatbot response, every image generation request. Yet most GPU cloud guides focus on training. If you are deploying models to serve real users, inference hosting is the cost center that determines whether your AI product is profitable or burning cash.
The challenge is not just picking a GPU. It is choosing the right deployment model — dedicated instances, serverless endpoints, or managed APIs — and matching it to your traffic patterns, latency requirements, and budget. An H100 serving a 70B-parameter model at $3.50/hr makes sense at 100 requests per second. At 5 requests per second, you are paying $0.70 per request for compute that sits 95% idle.
This guide breaks down everything you need to know about GPU inference hosting in 2026: deployment architectures, GPU selection by model size, cost optimization strategies, and a direct comparison of io.net, RunPod, Together AI, and AWS SageMaker for production inference workloads.
Inference Deployment Models: Three Approaches
There is no single correct way to host inference. The right architecture depends on your traffic volume, latency tolerance, and operational capacity.
Dedicated GPU Instances
You rent one or more GPUs full-time. You install your inference server (vLLM, TGI, Triton), load your model, and manage scaling yourself.
Best for: Sustained high-throughput workloads where GPUs stay utilized above 60-70%. Teams that need full control over the inference stack — custom batching, specific quantization configs, private model weights that cannot leave your instance.
Typical cost: $0.40-$3.50/hr depending on GPU. You pay whether the GPU is serving requests or sitting idle.
Trade-off: Maximum control and lowest per-request cost at high utilization, but you absorb all operational overhead: scaling, health monitoring, model updates, and failover.
Serverless GPU Inference
The provider manages a pool of GPUs. Your model loads on demand when requests arrive. You pay per request or per compute-second, with automatic scaling from zero to peak.
Best for: Variable or bursty traffic where dedicated GPUs would sit idle most of the time. Early-stage products where request volume is unpredictable. Teams without dedicated MLOps engineers.
Typical cost: $0.0002-$0.005 per request depending on model size and provider. Cold starts add 5-30 seconds for the first request after idle periods.
Trade-off: No idle cost and zero ops, but cold starts can break latency SLAs, and per-request pricing becomes expensive at high sustained volume. Crossing roughly 50-100 requests per minute continuously, a dedicated GPU is cheaper.
Managed API (Model-as-a-Service)
The provider hosts popular models and exposes an OpenAI-compatible API. You send prompts, receive completions. No infrastructure decisions at all.
Best for: Teams that want to use standard models (Llama 3, Mistral, DeepSeek, Stable Diffusion) without deploying anything. Prototyping and MVPs. Applications where the model itself is not your competitive advantage.
Typical cost: Per-token pricing. Input tokens $0.10-$2.00 per million, output tokens $0.30-$8.00 per million depending on model size.
Trade-off: Fastest time to production and zero infrastructure. But no control over batching, quantization, or model customization. You are locked into the provider's model versions and throughput limits.
Decision Framework
| Factor | Dedicated GPU | Serverless | Managed API |
|---|---|---|---|
| Traffic pattern | Sustained, predictable | Bursty, variable | Any |
| Latency requirement | You control it | Cold start risk | Provider-dependent |
| Ops team needed | Yes | Minimal | None |
| Cost at high volume | Lowest | Medium | Highest |
| Cost at low volume | Highest (idle waste) | Low | Lowest |
| Custom models / LoRA | Full support | Limited | No |
| Time to deploy | Hours | Minutes | Minutes |
GPU Selection for Inference Workloads
Not every model needs an H100. Choosing the right GPU for your model size is the single most impactful cost decision in inference hosting.
H100 80GB — Large Language Models (70B+ Parameters)
The H100 is necessary for serving large models at production latency. Its 80GB HBM3 memory fits 70B-parameter models in FP16 without sharding, and its Transformer Engine accelerates attention computation. For multi-model serving or 100B+ parameter models, H100 NVLink clusters provide the bandwidth for tensor parallelism.
Use when: Serving Llama 3.1 70B, DeepSeek-V3, Mixtral 8x22B, or any model exceeding 40GB in memory at your target precision.
io.net pricing: $2.10-$3.50/hr
A100 80GB — Mid-Size Models (7B-30B Parameters)
The A100 remains the price-performance leader for mid-size inference. 80GB HBM2e handles Llama 3.1 8B in FP16 with room for KV cache, and serves quantized 70B models (INT4/GPTQ) with acceptable throughput. The A100's mature software ecosystem means every inference framework is battle-tested on this hardware.
Use when: Serving 7B-30B models in FP16, or quantized 70B models where you can tolerate slightly lower throughput than H100.
io.net pricing: $1.20-$2.00/hr
RTX 4090 24GB — Small Models, LoRA Adapters, and Edge Inference
The RTX 4090 is the cost efficiency champion for smaller workloads. 24GB VRAM serves 7B models in FP16 or quantized 13B models (INT4/AWQ). With LoRA adapters that add only megabytes to a base model, you can serve dozens of fine-tuned variants from a single RTX 4090.
Use when: Serving models under 13B parameters, running quantized inference, deploying LoRA-adapted models, or handling image generation (Stable Diffusion XL, Flux).
io.net pricing: $0.40-$0.80/hr
L40S 48GB — Multi-Modal and Video Models
The L40S sits between the A100 and RTX 4090 in both price and capability. Its 48GB VRAM and Ada Lovelace architecture make it strong for multi-modal models (vision-language, video understanding) and concurrent serving of multiple smaller models.
Use when: Serving multi-modal models, running video inference pipelines, or hosting multiple 7B models on a single GPU.
Quick Reference: Model-to-GPU Matching
| Model Category | Examples | Minimum GPU | Recommended GPU |
|---|---|---|---|
| Small LLM (1-7B) | Llama 3.1 8B, Mistral 7B, Phi-3 | RTX 4090 (24GB) | RTX 4090 |
| Mid LLM (7-30B) | Llama 3.1 13B, CodeLlama 34B | A100 40GB | A100 80GB |
| Large LLM (30-70B) | Llama 3.1 70B, DeepSeek-V3 | A100 80GB (quantized) | H100 80GB |
| Huge LLM (70B+) | Llama 3.1 405B, GPT-4 class | H100 cluster | Multi-H100 NVLink |
| Image generation | SDXL, Flux | RTX 4090 | RTX 4090 / L40S |
| Multi-modal | LLaVA, GPT-4V class | L40S (48GB) | L40S / A100 |
| LoRA serving | Any base + adapters | RTX 4090 | RTX 4090 |
Cost Optimization for Inference
Raw GPU pricing is only one variable. How you deploy and configure inference determines whether you spend $500/month or $5,000/month serving the same model at the same throughput.
Batch vs. Real-Time Inference
Not every prediction needs to happen in 200 milliseconds. If your application can tolerate seconds of latency — background processing, async pipelines, batch scoring — batching requests dramatically improves GPU utilization and reduces cost per inference.
Real-time inference: Each request processes individually. GPU utilization: 10-40% at low traffic. Cost-efficient only above 50+ concurrent requests.
Batch inference: Requests queue and process together. GPU utilization: 70-95%. Cost per inference drops 3-5x compared to real-time at low volumes.
Dynamic batching (vLLM, TGI): The inference server automatically groups incoming requests into micro-batches. You get near-real-time latency with batch-level efficiency. This is the default for production LLM serving in 2026.
Quantization: Fit Bigger Models on Smaller GPUs
Quantization reduces model precision from FP16 (16-bit) to INT8 (8-bit) or INT4 (4-bit), cutting memory requirements by 2-4x. This lets you serve a 70B model on a single A100 instead of an H100, or a 13B model on an RTX 4090 instead of an A100.
| Precision | Memory per 7B Model | Quality Impact | GPU Savings |
|---|---|---|---|
| FP16 (default) | ~14GB | Baseline | None |
| INT8 (GPTQ/AWQ) | ~7GB | Minimal (<1% degradation) | ~50% |
| INT4 (GPTQ/AWQ) | ~3.5GB | Small (1-3% degradation) | ~75% |
Practical impact: A Llama 3.1 70B model in FP16 requires ~140GB of VRAM — two H100s. Quantized to INT4, it fits on a single A100 80GB. At io.net pricing, that changes the cost from $4.20-$7.00/hr (2x H100) to $1.20-$2.00/hr (1x A100). A 70% reduction from quantization alone.
Managed API vs. Self-Hosted: The Break-Even Calculation
Managed APIs (per-token pricing) and self-hosted inference (per-hour GPU rental) have a clear crossover point.
Managed API (e.g., io.intelligence): $0.20 per million input tokens, $0.60 per million output tokens for Llama 3.1 8B class models.
Self-hosted on dedicated GPU: An RTX 4090 at $0.50/hr serves Llama 3.1 8B at roughly 50 tokens/second, processing approximately 180,000 tokens per hour. Cost per million tokens: ~$2.78.
Break-even: At low volume (under 1M tokens/day), managed APIs are cheaper because you pay nothing when idle. Above roughly 5-10M tokens/day, self-hosted inference on a dedicated GPU becomes more cost-effective. Above 50M tokens/day, self-hosted is 3-5x cheaper.
The right answer depends on where you are in your growth curve. Start with a managed API, move to self-hosted when your traffic justifies a full-time GPU.
io.net for Inference: Three Paths to Production
io.net provides a GPU cloud infrastructure built on a decentralized network of 320,000+ GPUs across 130+ countries. For inference workloads, there are three distinct deployment paths.
Option 1: io.intelligence API — Managed Inference, Zero Ops
io.intelligence offers 25+ pre-deployed models through an OpenAI-compatible API. You send requests, receive completions. No GPUs to manage, no inference servers to configure, no scaling to handle.
What is available: - LLM models: Llama 3.1 (8B, 70B, 405B), DeepSeek-V3, Mistral, and others - Image generation models - Embedding models - Per-token pricing with no minimum commitment
Best for: Teams that want production inference in minutes. Prototyping applications before committing to custom infrastructure. Workloads using standard open-source models where customization is not required.
How it works:
curl https://api.intelligence.io.net/api/v1/chat/completions \
-H "Authorization: Bearer $IO_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "meta-llama/Llama-3.1-70B-Instruct",
"messages": [{"role": "user", "content": "Explain GPU inference hosting"}],
"max_tokens": 512
}'
The API is drop-in compatible with OpenAI client libraries. Switching from OpenAI or another provider requires changing two lines: the base URL and API key.
Option 2: Self-Hosted on io.cloud — Full Control on Dedicated GPUs
For teams that need custom models, specific quantization configurations, or full control over the inference stack, io.cloud provides dedicated GPU instances that deploy in under 2 minutes.
What you get: - Dedicated H100, A100, RTX 4090, or L40S instances - Full root access to install vLLM, TGI, Triton Inference Server, or any framework - Per-minute billing with no minimum commitment - Cluster deployment for multi-GPU model parallelism
Best for: Custom fine-tuned models that are not available on managed APIs. LoRA adapter serving. Workloads requiring specific batching or caching configurations. Teams with MLOps capability that want maximum cost efficiency at scale.
Typical deployment: 1. Select your GPU type and quantity on io.cloud 2. Cluster deploys in under 2 minutes 3. SSH in, install your inference framework (vLLM recommended for LLMs) 4. Load your model weights 5. Expose your endpoint
Cost advantage: H100 at $2.10-$3.50/hr and A100 at $1.20-$2.00/hr — 60-70% below equivalent AWS instances. For teams running inference 24/7, this compounds into thousands per month in savings.
Option 3: Agent Cloud — Infrastructure for AI Agents
Agent Cloud is purpose-built for AI agent workloads — autonomous systems that need persistent compute, multi-step reasoning, and tool integration.
Best for: AI agent deployments that require always-on GPU compute for inference combined with orchestration tooling. Teams building autonomous agents that call multiple models, use tools, and maintain state across long-running sessions.
Which io.net Option Fits Your Workload?
| Factor | io.intelligence API | io.cloud (Self-Hosted) | Agent Cloud |
|---|---|---|---|
| Setup time | Minutes | Under 2 minutes | Minutes |
| Custom models | No (25+ pre-deployed) | Yes (any model) | Yes |
| LoRA adapters | No | Yes | Yes |
| Scaling | Automatic | Manual / scripted | Managed |
| Pricing model | Per-token | Per-hour (GPU) | Per-hour |
| Ops requirement | None | MLOps team | Minimal |
| Best traffic pattern | Variable / bursty | Sustained | Always-on |
Comparison: io.net vs RunPod Serverless vs Together AI vs AWS SageMaker
Four platforms, four approaches to inference hosting. Here is how they compare across the dimensions that matter for production deployment.
Architecture Overview
| Platform | Model | GPUs | Deployment Speed | Pricing |
|---|---|---|---|---|
| io.net | Decentralized GPU network + managed API | 320,000+ across 130+ countries | Clusters in <2 min | H100: $2.10-$3.50/hr, A100: $1.20-$2.00/hr |
| RunPod Serverless | Serverless GPU endpoints | Centralized data centers | Minutes | Per-second billing, H100: ~$2.69/hr active |
| Together AI | Managed API (model hosting) | Centralized | Instant (API) | Per-token, Llama 70B: ~$0.90/M tokens |
| AWS SageMaker | Managed ML endpoints on AWS | AWS regions | 10-30 min | ml.g5.xlarge: $1.41/hr + SageMaker fees |
Cost Comparison — Serving Llama 3.1 70B
Scenario: 10M tokens/day output, sustained production traffic.
| Platform | Monthly Cost (est.) | Cold Start | Custom Models |
|---|---|---|---|
| io.net (io.cloud, A100 quantized) | ~$1,000-$1,500 | None (dedicated) | Yes |
| io.net (io.intelligence API) | ~$1,800-$2,400 | None | No (pre-deployed) |
| RunPod Serverless | ~$2,000-$3,200 | 10-30s | Yes (custom containers) |
| Together AI | ~$2,700-$3,600 | None | Limited |
| AWS SageMaker | ~$4,500-$7,000 | None (always-on endpoint) | Yes (bring your own) |
Feature Comparison
| Feature | io.net | RunPod | Together AI | AWS SageMaker |
|---|---|---|---|---|
| OpenAI-compatible API | Yes (io.intelligence) | No | Yes | No |
| Dedicated GPUs | Yes | Yes (non-serverless) | No | Yes |
| Serverless auto-scaling | Via io.intelligence | Yes | Yes | Yes |
| Multi-GPU clusters | Yes | Yes | N/A | Yes |
| Global GPU distribution | 130+ countries | Limited regions | N/A | AWS regions |
| Per-minute billing | Yes | Per-second | Per-token | Per-second |
| No egress fees | Yes | Low fees | N/A | $0.09/GB |
| vLLM / TGI support | Yes (self-hosted) | Yes | N/A | Yes |
| Minimum commitment | None | None | None | None (on-demand) |
When to Choose Each
Choose io.net when: You want the lowest cost for sustained inference, need dedicated GPUs without cloud markup, want both managed API and self-hosted options from one provider, or need rapid cluster deployment across a global GPU network.
Choose RunPod Serverless when: Your traffic is highly variable with long idle periods, you want true scale-to-zero with no idle cost, and cold start latency is acceptable for your use case.
Choose Together AI when: You only need standard open-source models via API, want the simplest possible integration, and cost is secondary to speed of implementation.
Choose AWS SageMaker when: You are already deep in the AWS ecosystem, need enterprise SLAs and compliance certifications, or require tight integration with S3, Lambda, and other AWS services.
Frequently Asked Questions
What is GPU inference hosting?
GPU inference hosting is the deployment of trained machine learning models on GPU-accelerated cloud infrastructure to serve predictions in real time or batch. Unlike training (which runs once to build a model), inference runs continuously in production, processing every user request. Hosting options range from dedicated GPU instances where you manage the full stack, to managed APIs where the provider handles everything.
How much does it cost to host AI inference in the cloud?
Costs vary by model size, GPU type, and deployment model. For a 7B-parameter LLM on an RTX 4090, expect $0.40-$0.80/hr on io.net. For a 70B model on an H100, expect $2.10-$3.50/hr. Managed API pricing runs $0.10-$2.00 per million input tokens depending on model size. At sustained volume (10M+ tokens/day), self-hosted inference on io.net is typically 40-70% cheaper than managed API alternatives.
What GPU do I need for LLM inference?
Match GPU VRAM to your model size. Models under 7B parameters fit on an RTX 4090 (24GB). Models in the 7-30B range need an A100 (40GB or 80GB). Models at 70B+ parameters require an H100 (80GB) or a quantized setup on A100. Quantization (INT8/INT4) reduces memory requirements by 2-4x, letting you serve larger models on smaller GPUs.
What is the difference between inference and training in GPU cloud?
Training iterates over data to build model weights — it is compute-intensive, runs for hours or days, and happens infrequently. Inference uses those trained weights to make predictions — it is latency-sensitive, runs continuously, and accounts for 90%+ of production GPU spend. Training benefits from maximum GPU throughput. Inference benefits from low latency, high availability, and cost-per-request efficiency.
Can I use quantized models for production inference?
Yes. INT8 quantization reduces memory usage by 50% with less than 1% quality degradation for most models. INT4 quantization reduces memory by 75% with 1-3% quality impact. Production systems from major AI companies routinely serve quantized models. Frameworks like vLLM and TGI natively support GPTQ and AWQ quantization formats. The cost savings — fitting a 70B model on a single A100 instead of two H100s — typically outweigh the marginal quality difference.
How fast can I deploy inference on io.net?
GPU clusters on io.net deploy in under 2 minutes. For the io.intelligence managed API, deployment is instant — make an API call and receive a response. For self-hosted inference on io.cloud, the total time from cluster provisioning to serving your first request is typically 5-15 minutes, depending on model download size and framework setup.
Conclusion
GPU inference hosting is where AI infrastructure spending concentrates in production. Choosing the right deployment model — dedicated GPUs, serverless, or managed API — and matching the correct GPU to your model size determines whether you pay $500/month or $5,000/month for equivalent throughput.
The core optimization levers are clear: quantize aggressively (INT8 at minimum, INT4 when quality allows), use dynamic batching frameworks like vLLM, and start with managed APIs before committing to self-hosted when traffic justifies a dedicated GPU.
io.net offers the full spectrum — from zero-ops managed inference via io.intelligence (25+ models, OpenAI-compatible API, per-token pricing) to dedicated GPU clusters on io.cloud (H100 at $2.10-$3.50/hr, deployed in under 2 minutes, 70% below AWS). Whether you are deploying your first model or optimizing a production pipeline serving millions of requests, there is a path that fits your stage and budget.
Explore io.net GPU inference options →