What Are the Most Budget-Efficient Approaches to Operating AI Inference in Cloud Environments?

The biggest barrier to scaling AI from prototype to production isn't model quality. It's inference cost. For IT leaders deploying LLMs at enterprise scale, inference accounts for 80-90% of total AI compute spend, and the bill grows linearly with every new user, every new feature, and every additional model.

The challenge is reducing cost per inference request without degrading response quality or latency.

That requires a full-stack approach: optimizing the model itself (compression techniques that reduce resource consumption), optimizing the inference runtime (engines that maximize throughput per GPU dollar), and scaling efficiently with distributed systems.

This article covers model optimization methods (quantization and sparsity), runtime optimization (vLLM's continuous batching and PagedAttention), distributed inference at scale (the llm-d project), Red Hat's enterprise AI portfolio for simplifying large-scale inference, and how managed GPU platforms like GMI Cloud provide the infrastructure layer that makes all of these optimizations practical.

Why Inference Costs Demand a Full-Stack Solution

The Problem: Costs Scale Linearly, Budgets Don't

A single GPT-5 API call at $10.00/M output tokens seems manageable. A million daily requests at 500 output tokens each costs $5,000/day, or $150,000/month. Even with cheaper models like GLM-5 at $3.20/M output (68% less), you're still looking at $48,000/month at that volume.

The math only works if you attack cost at every layer of the stack simultaneously.

Full-Stack Optimization: Two Layers

Budget-efficient inference requires optimizing both the model and the runtime. Model optimization reduces resource consumption per inference (less VRAM, fewer FLOPs, faster decode). Runtime optimization maximizes how efficiently the GPU executes those inferences (higher utilization, better batching, less memory waste).

Doing one without the other leaves significant savings on the table.

Model Optimization: Compression Techniques That Cut Costs

Quantization: Reducing Precision to Reduce Cost

Quantization converts model weights from higher-precision formats (FP32, FP16) to lower-precision formats (FP8, INT8, INT4). A 70B parameter model in FP16 requires ~140 GB VRAM. In FP8, it needs ~70 GB, fitting on a single H100 (80 GB) instead of requiring two GPUs.

In INT4, it drops to ~35 GB, potentially fitting on a single consumer GPU for development.

The cost impact is direct: fewer GPUs per model means lower per-hour infrastructure spend. On GMI Cloud, serving a 70B model on 1x H100 at ~$2.10/GPU-hour versus 2x H100 at ~$4.20/GPU-hour cuts your GPU bill in half.

FP8 quantization on H100/H200 with TensorRT-LLM typically delivers 1.5-2x throughput improvement versus FP16 with minimal quality degradation for most LLM tasks. The key is testing output quality at your target precision before deploying to production.

Sparsity: Skipping Unnecessary Computation

Sparsity techniques identify and skip zero or near-zero weight values during computation.

Structured sparsity (like NVIDIA's 2:4 sparsity pattern on Ampere and Hopper GPUs) zeros out 50% of weights in a structured format that the hardware can accelerate natively, delivering up to 2x speedup for compatible layers without custom kernels.

Unstructured sparsity offers higher compression ratios but requires specialized runtime support.

Mixture-of-Experts (MoE) architectures like DeepSeek R1 (671B total parameters, ~37B active per token) achieve sparsity at the architecture level: only a fraction of the model activates per request, dramatically reducing compute per inference while maintaining full-model capacity.

On GMI Cloud, DeepSeek R1 is available at $0.50/M input and $2.18/M output via Deploy endpoints.

Runtime Optimization: Getting More from Every GPU

vLLM: The Open-Source Throughput Multiplier

vLLM is the most widely adopted open-source LLM serving engine, and its two core innovations directly reduce inference cost.

PagedAttention manages the KV-cache (the memory that stores attention state for each active request) using a paging system inspired by OS virtual memory.

Traditional serving engines pre-allocate contiguous memory blocks for each request's maximum possible sequence length, wasting 60-80% of KV-cache memory on padding. PagedAttention allocates memory in small, non-contiguous pages, virtually eliminating this waste.

The result: you can serve 2-4x more concurrent requests on the same GPU, directly reducing cost per request.

Continuous batching dynamically adds new requests to in-progress batches as soon as GPU capacity becomes available, instead of waiting for a full batch to accumulate. Traditional static batching leaves the GPU idle between batch windows.

Continuous batching keeps GPU utilization at 80-95% under load, compared to 30-50% with static batching. Higher utilization means more requests served per GPU-hour, which means lower cost per request.

How This Connects to Infrastructure

vLLM's optimizations deliver the biggest gains on GPUs with high memory bandwidth. An H100 SXM at 3.35 TB/s bandwidth (source: NVIDIA H100 Datasheet, 2023) benefits more from PagedAttention than a consumer GPU at 1 TB/s because the memory system can feed tokens faster once waste is eliminated.

GMI Cloud's Deploy endpoints come pre-configured with vLLM on H100/H200 clusters, so you get these optimizations without building the serving stack yourself.

Distributed Inference at Scale: The llm-d Project

What llm-d Adds Beyond Single-Node Optimization

Once you've optimized the model (quantization, sparsity) and the runtime (vLLM, continuous batching), the next cost frontier is multi-node efficiency. The llm-d project builds on vLLM to enable distributed LLM inference across GPU clusters with three key capabilities.

Disaggregated prefill and decode: llm-d separates prompt processing (prefill, compute-intensive) from token generation (decode, memory-bandwidth-intensive) onto different GPU pools. This lets you right-size hardware for each phase instead of provisioning for the more demanding one across the board.

KV-cache-aware routing: when a request arrives that shares a prefix with an already-cached prompt (common in RAG pipelines and chat applications), llm-d routes it to the node that holds the relevant cache, avoiding redundant computation.

At high request volumes with shared prefixes, this can reduce compute costs by 30-50%.

Elastic scaling with orchestration: llm-d integrates with Kubernetes for automated scaling based on queue depth, latency targets, and GPU utilization. It scales GPU nodes up and down without manual intervention, preventing the over-provisioning that wastes budget during off-peak hours.

Infrastructure Requirements

llm-d's disaggregated architecture requires high-bandwidth GPU interconnects. NVLink 4.0 (900 GB/s bidirectional aggregate per GPU on HGX/DGX platforms) handles intra-node KV-cache transfer efficiently, while InfiniBand (3.2 Tbps inter-node) supports cross-node routing without becoming a bottleneck.

GMI Cloud's H100/H200 cluster topology (8 GPUs per node, NVLink 4.0, InfiniBand) aligns with llm-d's architecture requirements.

Red Hat AI: Enterprise Orchestration for Large-Scale Inference

The Red Hat AI Portfolio

Red Hat's AI product suite addresses a specific enterprise need: running inference workloads on Kubernetes with enterprise-grade support, security, and lifecycle management. The portfolio includes several components working together.

Red Hat OpenShift AI provides a managed platform for deploying, monitoring, and scaling AI models on OpenShift clusters. It integrates model serving, GPU scheduling, and monitoring into the existing OpenShift operations model, so teams don't need separate tooling for AI infrastructure.

Red Hat Enterprise Linux AI (RHEL AI) bundles an optimized RHEL kernel with pre-configured GPU drivers, CUDA libraries, and inference runtimes, reducing the time from bare-metal GPU to serving endpoint from days to hours.

InstructLab enables teams to customize and fine-tune models using a contribution-based workflow, then deploy directly through the Red Hat AI stack. This is relevant for cost optimization because fine-tuned smaller models can often replace larger general-purpose models at a fraction of the inference cost.

Where Red Hat AI Fits in the Cost Stack

Red Hat AI doesn't replace the model optimization (quantization, sparsity) or runtime optimization (vLLM, llm-d) layers. It sits above them, providing enterprise orchestration, lifecycle management, and support.

For organizations that run on OpenShift and need enterprise SLAs for their inference infrastructure, it's the orchestration layer that ties the optimization stack together. For more details and learning resources, visit the Red Hat AI website.

Putting It All Together: A Cost Optimization Stack

Layer (Technique / Cost Impact)

• Model — Technique: FP8 quantization — Cost Impact: 50% GPU memory reduction, 1.5-2x throughput
• Model — Technique: Structured sparsity (2:4) — Cost Impact: Up to 2x speedup on compatible layers
• Model — Technique: MoE architectures — Cost Impact: 5-10x fewer active params per request
• Runtime — Technique: PagedAttention (vLLM) — Cost Impact: 2-4x more concurrent requests per GPU
• Runtime — Technique: Continuous batching — Cost Impact: 80-95% GPU utilization (vs 30-50%)
• Distributed — Technique: Disaggregated prefill/decode (llm-d) — Cost Impact: Right-sized hardware per phase
• Distributed — Technique: KV-cache-aware routing — Cost Impact: 30-50% compute savings on shared prefixes
• Orchestration — Technique: Red Hat OpenShift AI / RHEL AI — Cost Impact: Enterprise lifecycle + auto-scaling
• Infrastructure — Technique: GMI Cloud H100/H200 clusters — Cost Impact: Pre-optimized stack, $2.10-2.50/GPU-hr

The compounding effect is significant. Quantization alone might cut your GPU bill by 50%. Add PagedAttention and continuous batching and you're serving 2-4x more requests per GPU. Layer in KV-cache-aware routing for shared-prefix workloads and costs drop another 30-50%.

On GMI Cloud's pre-configured infrastructure, you can also bypass the engineering cost of setting up these tools yourself.

For teams that prefer API access over GPU management, GMI Cloud's Model Library offers GLM-5 (by Zhipu AI) at $1.00/M input and $3.20/M output, 68% cheaper than GPT-5 ($10.00/M), with zero infrastructure management. Check console.gmicloud.ai for current pricing.

FAQ

Q: Does quantization always degrade model quality?

Not always. FP8 quantization on H100/H200 typically produces outputs that are indistinguishable from FP16 for most LLM tasks (chat, summarization, code generation). INT4 quantization introduces more noticeable degradation and should be tested carefully against your quality benchmarks.

The general rule: test at each precision level with your actual use-case prompts before deploying.

Q: Can I use vLLM on any GPU?

vLLM runs on any CUDA-compatible NVIDIA GPU. But its optimizations deliver the biggest cost savings on high-bandwidth GPUs like H100 (3.35 TB/s) and H200 (4.8 TB/s). On consumer GPUs with lower bandwidth, the throughput gains from PagedAttention are smaller because the memory system can't feed tokens as fast.

GMI Cloud's Deploy endpoints run vLLM pre-configured on H100/H200 clusters.

Q: When should I consider llm-d over standard vLLM?

Standard vLLM handles single-node inference well for most workloads up to ~100K daily requests.

llm-d adds value when you're scaling beyond a single node, when your workload has high prefix overlap (RAG, chat), or when you need disaggregated prefill/decode for workloads with very different compute profiles between prompt processing and token generation.

Q: What's the cheapest way to run production LLM inference today?

Combine model optimization (FP8 quantization) with runtime optimization (vLLM continuous batching) on cost-efficient GPU infrastructure. On GMI Cloud, that means running FP8 models on H100 at ~$2.10/GPU-hour with the pre-configured vLLM stack.

For teams that prefer API access, GLM-4.7-Flash at $0.07/M input and $0.40/M output is 33% cheaper than GPT-4o-mini ($0.60/M). Check console.gmicloud.ai for current rates.

Which AI Inference Provider Is Most Suitable