Benchmarks

These guides carry dated performance data — the hardware, runtime version, quantization, and the date each figure was collected. First-party results were measured by LocalRig; community-sourced figures inside a guide are labeled as such. How this testing works is on the methodology page.

• Cudo Compute Review 2026: Distributed GPU Cloud Without the Marketplace Roulette
  Data: Jul 9, 2026 Cudo Compute occupies the middle ground between Vast.ai's bargain chaos and hyperscaler lock-in: distributed GPU supply with SMB-grade account management. Honest assessment of who it fits, pricing anchored to H100 market rates, and when RunPod or Vast is the better choice.
• DigitalOcean GPU Droplets Review 2026: Beginner-Friendly, Not the Cheapest
  Data: Jul 9, 2026 DigitalOcean's GPU Droplets are the beginner-friendliest tier-1 cloud GPU option: predictable per-GPU-hour pricing, no spot preemption, and tight integration with existing DO infrastructure. Paperspace (acquired 2023) still runs as a separate subscription-gated product, not a merged one. For hobbyists escaping ChatGPT costs.
• Lambda Cloud Review 2026: The $4.29 H100 Standard-Bearer for Serious Training
  Data: Jul 9, 2026 Lambda Cloud is the reference point builders compare against: clean datacenter H100s at ~$4.29/hr on-demand (single GPU) or ~$4.09/hr per GPU in an 8x cluster, with no marketplace variance. Wins decisively for multi-hour training and fine-tuning where interruption risk is high. Overkill for inference workloads where cheaper marketplaces undercut, and pricier than it used to be.
• RunPod Review 2026: Secure Cloud vs Community Cloud, Pricing, and the Data-Loss Gotcha
  Data: Jul 9, 2026 RunPod is the only tier-1 provider renting a real consumer RTX 4090. This guide covers Secure Cloud vs Community Cloud, when interruptions cost more than savings, the network-volume data-loss failure mode, and whether RunPod makes sense for fine-tuning or inference.
• Is Vast.ai Safe? An Honest 2026 Review of the Cheapest GPU Marketplace
  Data: Jul 9, 2026 Vast.ai is legitimate, not a scam. But it is a peer-to-peer GPU marketplace with a variance problem: host reputations vary widely, pricing can creep past advertised rates, and unattended workloads silently fail. This guide separates real risks from rumors and tells you when Vast makes sense.
• Is the RTX 5090 Worth It for Local AI in 2026? $2,000 MSRP, $3,700+ Reality
  Data: Jun 29, 2026 The RTX 5090 lists at $1,999 but is selling near double that in mid-2026. This guide runs the actual VRAM-per-dollar math against used RTX 3090 pairs and Apple Silicon unified memory, plus the PSU and PCIe 5.0 platform costs nobody puts on the spec sheet.
• Two Used RTX 3090s vs One RTX 4090: The 48GB Question
  Data: Jun 29, 2026 For roughly the same spend, two used RTX 3090s buy 48GB of VRAM while one RTX 4090 buys 24GB at higher per-card speed. The real decision isn't value — it's whether your target model needs more than 24GB, because that single fact locks in a PSU, a motherboard, and a runtime you can't undo cheaply.
• Hardware to Run a 70B Model Locally: VRAM, the 48GB Wall, and Your Real Options
  Data: Jun 28, 2026 What it actually takes to run a 70B model at home: the VRAM math, why 48GB is the practical floor at Q4, and the four hardware paths (dual 3090, used A6000, Apple Silicon, or cloud).
• Best GPU for Local LLM Inference (2026): VRAM-per-Dollar Guide
  Data: Jun 28, 2026 The GPU decision for local LLM inference is set by VRAM (does the model fit) and memory bandwidth (how fast it decodes), not raw FLOPS. A constraint-first, VRAM-per-dollar guide: used RTX 3090 vs RTX 4090 vs RTX 3060, multi-GPU reality, and when to switch to Apple Silicon.
• Best Mac for Local LLM Inference (2026): The Unified-Memory Buying Guide
  Data: Jun 28, 2026 Two specs decide a Mac for local AI — unified memory (what fits) and memory bandwidth (how fast it decodes). The M-series ladder from Mac mini M4 to Mac Studio M3 Ultra, with first-party and community numbers and honest tradeoffs vs a discrete GPU.
• How to Run LLMs Locally: Which Inference Engine for Your Rig (2026)
  Data: Jun 28, 2026 A decision guide that picks the right local inference engine from your hardware, not hype. llama.cpp for CPU and portability, MLX on Apple Silicon, vLLM for CUDA serving — and why we don't recommend Ollama.
• How to Run llama.cpp on an RTX 3090 (CUDA, Step by Step)
  Data: Jun 28, 2026 A step-by-step guide to building llama.cpp with CUDA and running a GGUF model on an RTX 3090. Covers driver and toolkit prerequisites, the CUDA build, full GPU offload with -ngl, a throughput check, and an OpenAI-compatible server.
• Hardware to Run a 7B/8B Model Locally: RTX 3090, Apple M3 Max, and Budget Options
  Data: Jun 27, 2026 Benchmark-backed hardware guide for running 7B and 8B parameter models locally. Covers RTX 3090, Apple M3 Max, RTX 3060, and Apple M4 — with first-party Apple M4 benchmarks, community throughput data, VRAM requirements, and honest trade-offs.
• The Local-AI Hardware Buying Framework
  Data: Jun 27, 2026 A constraint-first framework for choosing hardware to run AI models locally. Covers VRAM, memory bandwidth, quantization, Apple Silicon, and budget paths — so you buy once and regret nothing.
• Quantization: What It Means for Local AI and Why It Matters
  Data: Jun 27, 2026 Quantization reduces the numerical precision of a model's weights to shrink its memory footprint — the single technique that determines whether a 7B or 70B model fits in your GPU's VRAM and how fast it will run.