Find the cheapest way to run your AI workload
Calculate VRAM for your model, compare verified spot GPU pricing across 10+ clouds, and find zero-cost API endpoints.
| Provider | GPU & VRAM | Interconnect | Spot Rate | On-Demand | Monthly | Status | Action | |
|---|---|---|---|---|---|---|---|---|
Community | PCIe 4.0 (64 GB/s) | $0.34 / hr | $0.85 / hr | $208 / mo | Instant | |||
Bare Metal | PCIe 4.0 (64 GB/s) | $0.69 / hr | $1.73 / hr | $422 / mo | Instant | |||
Community | PCIe 4.0 (64 GB/s) | $0.69 / hr | $1.73 / hr | $422 / mo | Instant | |||
Cloud | PCIe 4.0 (64 GB/s) | $0.74 / hr | $1.85 / hr | $453 / mo | Instant | |||
Dedicated | PCIe 4.0 (64 GB/s) | $0.89 / hr | $2.23 / hr | $545 / mo | Instant | |||
Cloud | PCIe 4.0 (64 GB/s) | $1.09 / hr | $2.73 / hr | $667 / mo | Instant | |||
Bare Metal | PCIe 4.0 (64 GB/s) | $1.19 / hr | $2.97 / hr | $728 / mo | Instant | |||
Dedicated | PCIe 4.0 (64 GB/s) | $1.49 / hr | $3.73 / hr | $912 / mo | Instant | |||
Dedicated | N/A | $1.59 / hr | $3.98 / hr | $973 / mo | Instant | |||
Community | NVLink 4.0 (900 GB/s) | $1.89 / hr | $4.72 / hr | $1,157 / mo | Instant | |||
Bare Metal | NVLink 4.0 (900 GB/s) | $2.29 / hr | $5.73 / hr | $1,401 / mo | Instant | |||
Community | NVLink 4.0 (900 GB/s) | $2.79 / hr | $6.98 / hr | $1,707 / mo | Instant | |||
Dedicated | NVLink 4.0 (900 GB/s) | $2.99 / hr | $7.48 / hr | $1,830 / mo | Instant | |||
Bare Metal | NVLink 4.0 (900 GB/s) | $3.19 / hr | $7.98 / hr | $1,952 / mo | Instant | |||
Cloud | NVLink 4.0 (900 GB/s) | $3.49 / hr | $8.73 / hr | $2,136 / mo | Instant | |||
Community | NVLink 5.0 (1.8 TB/s) | $3.99 / hr | $9.98 / hr | $2,442 / mo | Instant | |||
Dedicated | NVLink 4.0 (900 GB/s) | $3.99 / hr | $9.98 / hr | $2,442 / mo | Instant | |||
Cloud | NVLink 4.0 (900 GB/s) | $4.31 / hr | $10.77 / hr | $2,638 / mo | Instant | |||
Bare Metal | NVLink 5.0 (1.8 TB/s) | $4.49 / hr | $11.23 / hr | $2,748 / mo | Instant | |||
Dedicated | NVLink 5.0 (1.8 TB/s) | $5.49 / hr | $13.73 / hr | $3,360 / mo | Instant | |||
Cloud | NVLink 5.0 (1.8 TB/s) | $5.99 / hr | $14.98 / hr | $3,666 / mo | Instant |
Can I Run It? Model GPU Requirements
Check VRAM requirements, quantization limits, and recommended cloud GPUs for every supported model.
DeepSeek R1
2x B200 192GB
Meta Llama 3.3 70B
1x H200 141GB
Qwen 2.5 Coder 32B
1x L40S 48GB or 1x A100 80GB
Mistral NeMo 12B
1x L40S 48GB
FLUX.1 [dev]
1x L40S 48GB
Llama 3.1 8B
1x L40S 48GB
GPU Comparisons
Side-by-side technical comparisons: interconnect latency, virtualization overhead, storage I/O, and spot eviction risk.
Spheron vs RunPod
Spheron offers dedicated bare-metal clusters with RDMA InfiniBand for multi-node distributed training.
Read Analysis →RunPod vs Vast.ai
Vast.ai offers the lowest spot pricing for constrained research and non-critical batch workloads.
Read Analysis →Spheron vs CoreWeave
CoreWeave provides massive-scale 1,000+ GPU HGX clusters for frontier foundation model training.
Read Analysis →Lambda Labs vs RunPod
Lambda Labs provides a pre-installed ML stack for PyTorch development.
Read Analysis →Hyperstack vs Verda
Verda is the standard deployment profile for EU data sovereignty requirements.
Read Analysis →Massed Compute vs RunPod
Massed Compute delivers predictable physical datacenter control.
Read Analysis →AWS EC2 P5 vs Specialist AI Clouds
AWS P5 is standard infrastructure if your pipeline relies on existing AWS IAM and S3.
Read Analysis →GCP A3 vs Lambda Labs
GCP A3 provides larger scale for Google ecosystem users, but Lambda Labs delivers lower overhead and better $/FLOP for independent teams..
Read Analysis →H100 SXM5 vs H200 SXM5
The H200 eliminates 2-GPU tensor sharding for Llama 70B at 128k context. The H100 remains the lower-cost option for sub-32k workloads at $1.89/hr vs $2.50/hr..
Read Analysis →H100 SXM5 vs B200 Blackwell
The B200 delivers 2x inference throughput via FP4 but requires liquid-cooled infrastructure at 1000W TDP. The H100 is deployment-ready in air-cooled datacenters today..
Read Analysis →RTX 4090 vs L40S
The RTX 4090 offers lower hourly cost for INT4 inference. The L40S leads on FP8 precision, memory capacity, enterprise reliability, and ECC memory for production workloads..
Read Analysis →Hardware Radar & Price Trackers
Dedicated pages for every tracked GPU model with live pricing, architecture breakdowns, and benchmark data.
NVIDIA H100 SXM5
NVIDIA H200 SXM5
NVIDIA B200 Blackwell
NVIDIA B300 Blackwell Ultra
NVIDIA A100 80GB SXM4
NVIDIA L40S
NVIDIA GeForce RTX 4090
NVIDIA GH200 Grace Hopper
NVIDIA RTX 6000 Ada Generation
NVIDIA RTX A6000
AI Compute 101 — GPU Fundamentals for LLM Deployment
Learn VRAM sizing, quantization formats, KV-cache scaling, and multi-GPU parallelism from first principles.
Model Hosting & VRAM Sizing
Find the cheapest cloud GPU for your specific model. VRAM sizing, quantization overhead, and production runbooks.
DeepSeek V2 / V2.5
236B MoE (21B active)
View Sizing Guide →Meta Llama 3.3 70B Instruct
70.6B Dense
View Sizing Guide →Mistral NeMo 12B
12.2B Dense (128k context)
View Sizing Guide →Qwen 2.5 Coder 32B Instruct
32.5B Dense
View Sizing Guide →FLUX.1 [dev]
12B Rectified Flow Transformer
View Sizing Guide →Whisper Large v3
1.55B Audio Speech-to-Text
View Sizing Guide →DeepSeek R1 Reasoning
671B MoE (37B active)
View Sizing Guide →BGE / E5 Vector Embedding Fleet
Multi-Model Dense Vector Batch
View Sizing Guide →Engineering Guides
Production architecture runbooks: network topologies, inference deployment, spot fault tolerance, and market telemetry.
Cloud GPU Pricing Index 2026
Comprehensive market benchmark evaluating spot rates, reserved tiers, and true cost-per-FLOP across verified enterprise and neo-cloud providers.
Do You Need InfiniBand? NVLink vs InfiniBand NDR for Multi-Node LLM Training
Technical breakdown of AllReduce gradient synchronization bottlenecks, rail-optimized switching topologies, and when 3.
Production vLLM Cluster Setup
Step-by-step engineering runbook for deploying high-concurrency vLLM clusters with dynamic KV-cache management, speculative decoding, and health checks.
Zero-Loss Spot GPU Training
Slashing compute burn rates by 60% without losing training progress: Implementing robust SIGTERM interceptors, asynchronous S3 state flushes, and cluster resumption.
LLM Token Pricing & Economics
Compare API token costs, self-hosted break-even thresholds, and routing gateway economics.
Token Economics Index
Model pricing, break-even calculators, self-host vs API analysis.
Explore Index →SPOTLIGHTOpenRouter Deep-Dive
Routing overhead, batch savings, fallback latency analysis.
Read Analysis →TOOLVRAM Calculator
Estimate VRAM, cluster cost, and compare GPU configs.
Open Calculator →DOCSMethodology
Pricing telemetry pipeline, benchmark standards, data provenance.
Read Docs →Latest Compute Intelligence & Guides
Curated how-to guides and research reports for AI infrastructure deployment.
How-To & Deployment Guides
How to Run Llama 3.3 70B Locally: VRAM, Quantization & Deployment
Run Llama 3.3 70B at home with INT4 quantization on a dual RTX 4090 setup, or deploy FP8 on a single H100 SXM5 via vLLM.
Production vLLM Deployment: PagedAttention, KV-Cache & Continuous Batching
To run Llama 3.3 70B in production with vLLM, allocate 2x 80GB GPUs (Tensor Parallelism = 2) or a single H200 (141GB) using FP8 precision. Configure --gpu-memory-utilization 0.92 and --kv-cache-dtype fp8 to maximize concurrent batch slots.