CPU inference works, but the throughput gap is an order of magnitude. A 7B or 8B parameter language model running on a standard CPU server typically generates single-digit tokens per second; on an RTX 4090, that number climbs to 80โ100+ tokens per second. As model size increases, memory bandwidth becomes the binding constraint. A 70B model running against system RAM carries latency that makes real-time serving impractical โ GPU VRAM is essentially a requirement at that scale.
Inference and training pull on different GPU characteristics. Inference demands high memory bandwidth and low latency, where consumer-grade cards like the RTX 4090 deliver strong price-performance. Training and fine-tuning require raw tensor throughput, large VRAM capacity, and fast inter-GPU communication โ the domain where data center accelerators like the A100 and H100 justify their cost.
GPU Model Comparison
| GPU | VRAM | Best For | Value |
|---|---|---|---|
| RTX 4090 | 24GB | Local inference, Stable Diffusion, Ollama | High |
| L40S | 48GB | Inference services, mid-scale model deployment | Medium-high |
| A100 40GB/80GB | 40/80GB | Fine-tuning, full training, large model deployment | Medium |
| H100 | 80GB | Large-scale distributed training, frontier model research | Low (high unit cost) |
| MI300X (AMD) | 192GB | Oversized model inference without sharding | Medium |
The RTX 4090 is the most cost-effective inference card available right now. Its 24GB VRAM handles quantized 7Bโ14B models and Stable Diffusion XL comfortably, and hourly rental rates on RunPod and Vast.ai run roughly one-tenth of H100 pricing. The H100's advantage is in multi-GPU distributed training via NVLink โ it's genuinely the right tool for that workload, but the barrier to entry keeps it in enterprise territory.
Provider Breakdown
Vultr GPU Cloud
Vultr was among the first general-purpose cloud providers to offer GPU instances to individual developers. The hardware lineup includes NVIDIA L40S and A100, with hourly billing across multiple global regions. The practical advantage is ecosystem integration โ object storage, VPC networking, Kubernetes, and load balancing all live in the same console, so GPU instances plug directly into existing infrastructure. Marketplace images support quick environment setup. Pricing sits in the mid-tier standard cloud range, higher than compute marketplaces but with more predictable network performance and SLA backing.
DigitalOcean GPU Droplets
DigitalOcean launched GPU Droplets after integrating Paperspace compute resources, with NVIDIA H100 as the primary offering. The DigitalOcean experience translates well here โ clean console, solid documentation, and low friction for teams already running other services on DO. Integration with DOKS (managed Kubernetes) is mature, making containerized AI inference deployment reasonably straightforward. Pricing is in the standard range for H100 providers; check the console for current rates.
Lambda Cloud
Lambda is AI compute only โ no general-purpose VPS hosting. Instance types span RTX 4090, A100, and H100, with H200 and B200 availability during certain periods. The platform has built meaningful adoption in the AI developer and research community, primarily because it ships with CUDA, cuDNN, and PyTorch pre-installed, eliminating driver configuration overhead, and hardware pricing is competitive. The main friction is inventory: popular GPU types frequently show waitlists, and regional coverage is narrower than general-purpose cloud providers. Best suited for fine-tuning and research experimentation.
CoreWeave
CoreWeave is enterprise AI infrastructure at scale, with large H100 and A100 cluster capacity backed by NVIDIA investment. The business model is built around long-term contracted capacity for organizations with large cluster requirements, not on-demand hourly access for individual developers. For small teams in an exploratory phase, the entry barrier โ both process and pricing โ doesn't match the use case.
RunPod
RunPod operates between a standard cloud and a compute marketplace, consistently offering lower per-hour rates across RTX 4090, A100, and H100 instances. It supports both standard Pod VMs and Serverless containers, where Serverless suits inference APIs that bill per invocation. A large community template library covers ComfyUI, SD WebUI, Ollama, and more for quick deployment. The caveat: some lower-cost resources are hosted by third-party data centers with variable SLA performance. For production workloads, multi-node redundancy is worth planning for. For personal projects and experimentation, the value is real.
Vast.ai
Vast.ai is a decentralized compute marketplace where third-party providers and individuals rent out idle GPUs. This drives prices to the lowest available levels for RTX 3090, 4090, and occasionally A100 instances. The trade-off is stability entirely dependent on the host: bandwidth varies significantly, and machines can go offline or be reclaimed without warning. Good for batch jobs, model testing, and learning projects. Not suitable for high-availability production services.
TensorWave
TensorWave's focus is AMD Instinct MI300X hardware. The MI300X carries 192GB of HBM3 VRAM per card, which means 70B FP16 unquantized models load onto a single card without tensor parallelism or aggressive quantization. The right fit for teams running high-precision large model inference at long context lengths, or those building on the AMD ROCm software stack.
Crusoe Cloud
Crusoe runs H100 and H200 hardware powered by oil field flare gas and renewable energy. Like CoreWeave, it's a serious enterprise AI cloud provider oriented toward long-term contracted capacity and compliance-driven deployments rather than individual developer access.
Matching Workload to Provider
| Workload | Recommended GPU | Recommended Provider |
|---|---|---|
| Ollama inference | RTX 4090 | RunPod / Vast.ai |
| Stable Diffusion | RTX 4090 | RunPod / Vultr |
| ComfyUI | RTX 4090 | RunPod |
| LLM fine-tuning | A100 80GB | Lambda / RunPod |
| Large-scale training | H100 | Lambda / CoreWeave |
| Oversized model inference | MI300X / H100 | TensorWave / CoreWeave |
| Development and testing | RTX 4090 / L40S | Vultr / DigitalOcean |
Renting vs. Buying
An RTX 4090 purchased outright runs $1,500โ$2,000. At typical cloud rental rates for a 4090 including associated storage, that maps to roughly 1,500โ2,000 hours of equivalent compute cost. Running the card at full utilization for 8+ hours daily, the hardware pays for itself in roughly 6โ8 months.
Ownership carries upfront capital requirements, electricity and cooling costs, hardware depreciation, and residual value risk as new architectures ship. The practical split: renting suits short-duration intensive fine-tuning, proof-of-concept validation, elastic inference, and intermittent generation workloads. Owning makes sense for stable, continuous compute needs with the operational infrastructure to support it.
Choosing by Budget
Maximum price sensitivity, stability optional: Vast.ai โ individual experiments and open-source model exploration.
Independent developers and early-stage teams: RunPod โ template ecosystem, flexible pricing, Serverless API support.
Existing cloud deployments needing GPU capacity: Vultr or DigitalOcean โ network topology, storage, and authentication integrate cleanly with existing infrastructure.
Professional ML research and mid-scale training: Lambda Cloud โ deep learning stack ready out of the box, stable hardware performance.
Enterprise-scale clusters with SLA requirements: CoreWeave or Crusoe โ contracted dedicated capacity with the operational guarantees to match.