# Vetted Consumer

> Honest local-LLM hardware advice, cross-checked across reviews and the people who own it. We aggregate real owner reports, independent lab tests, and primary sources; we do not test first-hand and say so. Scores and verdicts are set before affiliate considerations.

Vetted Consumer is a focused authority on hardware for running large language models locally: unified-memory machines, GPUs, AI mini PCs and servers, edge accelerators, and AI laptops. We also publish free interactive tools for sizing hardware to a model.

## Tools
- [Can I run it? calculator](https://vettedconsumer.com/can-i-run-it/): model size, quantization, and context to the memory math plus the hardware that fits, including your own machine
- [Cost calculator](https://vettedconsumer.com/cost-calculator/): buy hardware vs rent cloud GPUs vs use an API, with break-even math for your usage
- [Quant picker](https://vettedconsumer.com/quant-picker/): which GGUF quant to download for your machine, with file size and context budget
- [Hardware finder](https://vettedconsumer.com/hardware-finder/): answer 4 questions, get 3 honest hardware picks
- [Latest open models](https://vettedconsumer.com/latest-models/): every lab's newest open-weight releases, auto-updated daily (raw JSON feed: /models-feed.json)
- [Master hardware chart](https://vettedconsumer.com/all-hardware/): all tracked machines in one sortable table
- [Used GPU price index](https://vettedconsumer.com/gpu-price-index/): typical used prices recorded monthly (raw JSON data: /gpu-price-history.json)

## For AI agents (callable tools, not just readable content)
- [MCP server](https://vettedconsumer.com/mcp): Model Context Protocol over Streamable HTTP. Tools: can_i_run_it, recommend_quant, cheapest_hardware_for_model, list_models, list_hardware, cost_compare (buy vs rent vs API + energy cost per 1M tokens), recommend_hardware (ranked buyable machines that run a model), get_used_gpu_prices, compare_hardware. Server card: /.well-known/mcp/server-card.json
- [REST API](https://vettedconsumer.com/api): the same tools over plain HTTP, e.g. /api/can-i-run-it?model=Llama%2070B&hardware=rtx-3090&context=8192 or /api/cost-compare?hardware=rtx-3090-used&hours=3&tokens=300000

## About this site
- [Methodology](https://vettedconsumer.com/methodology/): how we research, weight sources, and score hardware
- [Corrections](https://vettedconsumer.com/corrections/): a public, dated log of fixes
- [Disclosure](https://vettedconsumer.com/disclosure/): affiliate and referral relationships

## Guides: how local LLMs work
- [Serving a Local LLM as an API: From Ollama's Endpoint to vLLM Throughput (and When to Rent Instead)](https://vettedconsumer.com/serving-a-local-llm-as-an-api-from-ollamas-endpoint-to-vllm-throughput-and-when-to-rent-instead/): Running a model in a chat window is step one. To let your editor, scripts, or an app use it, you serve it as an API. Here's the spectrum from a one-line Ollama endpoint to production vLLM, and the honest point where renting a cloud GPU beats your own box.
- [RAG on a Local LLM, Explained: Give Your Model Your Documents Without Drowning in Context](https://vettedconsumer.com/rag-on-a-local-llm-explained-give-your-model-your-documents-without-drowning-in-context/): You want your local model to know your notes, codebase, or docs. You can stuff it all into context, slow and expensive, or you can use RAG. Here's what retrieval actually is, the local-hardware trade-offs, and when it beats brute-force long context.
- [Prompt Processing vs Generation: Why Your Box Is Fast at One and Slow at the Other](https://vettedconsumer.com/prompt-processing-vs-generation-why-your-box-is-fast-at-one-and-slow-at-the-other/): The same machine can rip through generating tokens yet crawl when it reads a long prompt, or the reverse. That's because local LLMs run in two phases with opposite bottlenecks. Understand them and you'll know exactly which hardware spec to buy for.
- [The KV Cache, Explained: Why Long Context Eats Your VRAM (and How to Fit More)](https://vettedconsumer.com/the-kv-cache-explained-why-long-context-eats-your-vram-and-how-to-fit-more/): You quantized the model and it fits, then it runs out of memory at long context. The culprit is the KV cache, and at 128k tokens it can dwarf the model itself. Here's the math, the fix, and what it means for buying local-LLM hardware.
- [How Much VRAM Do You Actually Need to Run a 70B Model Locally?](https://vettedconsumer.com/how-much-vram-do-you-actually-need-to-run-a-70b-model-locally/): A single 24GB GPU can't run a 70B model, you need ~40-48GB. Here's the simple quant math to size any model, plus the three real ways to get there: stack GPUs, a unified-memory box, or rent.
- [Ollama vs LM Studio vs llama.cpp: Which Local LLM Runtime Should You Actually Use?](https://vettedconsumer.com/ollama-vs-lm-studio-vs-llama-cpp-which-local-llm-runtime-should-you-actually-use/): They're not really rivals, Ollama and LM Studio are friendly wrappers around the llama.cpp engine. Here's which to pick: LM Studio for beginners, Ollama to serve a model, llama.cpp for max speed.
- [Mixture-of-Experts (MoE), Explained: Why “Active Parameters” Decide What Runs on Your Machine](https://vettedconsumer.com/mixture-of-experts-moe-explained-why-active-parameters-decide-what-runs-on-your-machine/): How does a 671B model run on a desktop while a 70B dense model chokes? The answer is Mixture-of-Experts, and the one number that actually predicts speed and memory. A plain-English guide with real benchmarks and the papers behind it.
- [GGUF vs GPTQ vs AWQ: The Plain-English Guide to LLM Quantization (and Which One to Pick)](https://vettedconsumer.com/gguf-vs-gptq-vs-awq-the-plain-english-guide-to-llm-quantization-and-which-one-to-pick/): GGUF, GPTQ, AWQ, Q4_K_M, NF4, the quantization alphabet soup, explained for people who just want to fit a bigger model in the VRAM they have. What each format is, the real VRAM math, and a decision table for which to use.

## Open model spotlights
- [Qwen3-30B-A3B: The Open Model Most People Should Actually Run](https://vettedconsumer.com/qwen3-30b-a3b-the-open-model-most-people-should-actually-run/): While everyone chased the 753B GLM-5.2 almost nobody can run, the model most people should actually run locally is small, fast and Apache-licensed, and it fits a single 24GB card. Inside Qwen3-30B-A3B: the creator claims, the research, what owners find, and exactly what runs it.
- [GLM-5.2: The Most Powerful Open-Weight Model Yet, and the Brutal Reality of Running It Locally](https://vettedconsumer.com/glm-5-2-the-most-powerful-open-weight-model-yet-and-the-brutal-reality-of-running-it-locally/): Z.ai’s GLM-5.2 is the new #1 open-weight model, 753B params, MIT license, a 1M-token context and a real architecture trick (IndexShare). But the weights are 1.51TB. What owners and the benchmarks actually say, and the honest hardware reality of running it at home.

## Unified-memory machines (run 70B-class models locally)
- [Your Local AI Model Folder Is a Mess: Taming a Multi-Terabyte Model Hoard on Apple Silicon](https://vettedconsumer.com/your-local-ai-model-folder-is-a-mess-taming-a-multi-terabyte-model-hoard-on-apple-silicon/): Run local LLMs long enough and you drown in models, multi-quant variants, image and video weights, RAG datasets, all scattered across drives and HuggingFace cache hell. Here's the real problem, what Alex Ziskind built to fix it, and how to organize your own hoard.
- [Mac Studio M3 Ultra vs DGX Spark for Local LLMs: What Owners of Both Measured](https://vettedconsumer.com/mac-studio-m3-ultra-vs-dgx-spark-for-local-llms-what-owners-of-both-measured/): Same ~$10K, opposite strengths. An owner ran Qwen 397B on both: Mac Studio M3 Ultra ~30-40 tok/s on 800GB/s bandwidth; dual DGX Spark ~27-28 tok/s but far faster prompt processing. Who should buy which.
- [Strix Halo vs DGX Spark: Running 70B Locally, According to People Who Own Both](https://vettedconsumer.com/strix-halo-vs-dgx-spark-running-70b-locally-according-to-people-who-own-both/): Same 128GB unified memory, so which actually runs a 70B model better? Owners who benchmarked both find token generation is a tie; the DGX Spark wins prompt processing and CUDA, for ~2x the price.
- [Mac Studio M3 Ultra: The Local-AI Workhorse, Buy Now or Wait for M5?](https://vettedconsumer.com/mac-studio-m3-ultra-the-local-ai-workhorse-buy-now-or-wait-for-m5/): Up to 512GB of fast unified memory makes the Mac Studio M3 Ultra the turnkey way to run huge local models. Real r/LocalLLaMA owner takes, and whether to wait for M5.
- [Is the RTX Spark a 'Marketing Trap'? The Skeptic's Case (and What Owners Say)](https://vettedconsumer.com/is-the-rtx-spark-a-marketing-trap-the-skeptics-case-and-what-owners-say/): Digital Spaceport argues the RTX Spark is more hype than substance for local AI. We pair that with real DGX Spark owner reports from r/LocalLLaMA, and the better-value alternatives.
- [Framework Desktop Buyer's Guide: The Repairable Strix Halo Box That Runs 70B Models](https://vettedconsumer.com/framework-desktop-buyers-guide-the-repairable-strix-halo-box-that-runs-70b-models/): AMD Ryzen AI Max+ 395, 128GB unified memory, fully repairable. Who the Framework Desktop is actually for, with real owner cost-math and caveats from r/LocalLLaMA.
- [GMKtec EVO-X2 Guide: The 128GB Mini PC That Runs 70B Models Locally](https://vettedconsumer.com/gmktec-evo-x2-guide-the-sub-1-500-mini-pc-that-runs-70b-models-locally/): AMD's Strix Halo in a mini PC with up to 128GB unified memory, a ~$2,199 box that runs 70B local models. Who it's for, and the one tradeoff.
- [A Top Reviewer Put the Mac Studio M4 Max to the Test, Here's Who Should Actually Buy It](https://vettedconsumer.com/a-top-reviewer-put-the-mac-studio-m4-max-to-the-test-heres-who-should-actually-buy-it/): Creator Karl Conrad reviewed Apple's most powerful desktop. The honest verdict: a compact powerhouse for pros, and overkill for nearly everyone else.
- [NVIDIA's RTX Spark Puts DGX-Class AI Silicon in a Laptop, Should You Wait?](https://vettedconsumer.com/nvidias-rtx-spark-puts-dgx-class-ai-silicon-in-a-laptop-should-you-wait/): NVIDIA's new RTX Spark chip brings DGX-class AI performance to laptops like the Surface Laptop Ultra. Here's what it means if you're deciding between a Mac Studio and a DGX Spark today.
- [The NVIDIA DGX Spark, According to the People Who Own One](https://vettedconsumer.com/the-nvidia-dgx-spark-according-to-the-people-who-own-one/)

## GPUs for local AI
- [Three RTX 3060s vs One RTX 3090 for Local AI: What a $1,500 Build Actually Measured](https://vettedconsumer.com/three-rtx-3060s-vs-one-rtx-3090-for-local-ai-what-a-1-500-build-actually-measured/): A builder put three used RTX 3060s against a single RTX 3090 on the same $1,500 rig, then benchmarked Gemma 4 and Qwen 3.6. The cheap cards hung on far better than expected, until the dense model showed up. Here is the real data, and which one to actually buy.
- [The Local-LLM Hardware Cheat-Sheet: Which Box Runs Which Model](https://vettedconsumer.com/the-local-llm-hardware-cheat-sheet-which-box-runs-which-model/): A one-page cheat-sheet: the cheapest hardware that actually runs each model size (8B to 235B), with real owner-reported speeds, free for subscribers.
- [The Used RTX 3090 in 2026: Why a Five-Year-Old GPU Is Still Local AI's Best Deal](https://vettedconsumer.com/used-rtx-3090-2026-local-ai-best-deal/): 24 GB of VRAM and 936 GB/s for around $1,100 used (up sharply this year, but still the cheapest 24GB-with-CUDA card for local AI). Real owner reports on why the 3090 remains r/LocalLLaMA’s default answer, the dual-3090 70B rig, and how not to get burned buying one.
- [RTX 5090: A 32GB AI Powerhouse, or an Expensive Way to Game?](https://vettedconsumer.com/rtx-5090-a-32gb-ai-powerhouse-or-an-expensive-way-to-game/): The fastest single card for local AI: 32GB at 1,792 GB/s runs a 32B model at ~40–60 tok/s. But it's not the cheapest path or the way to run the biggest models, who should buy it, who shouldn't, and what owners report.
- [RX 9060 XT 16GB Buyer's Guide: The Budget Value Champ (Buy the 16GB)](https://vettedconsumer.com/rx-9060-xt-16gb-buyers-guide-the-budget-value-champ-buy-the-16gb/): 16GB of VRAM for ~$349 makes the RX 9060 XT the budget value king, even for local AI. Real r/radeon owner takes on where it wins, and the price/FSR catches.
- [RTX 5090 vs RTX Pro 6000 for AI: A Benchmark Deep-Dive (and Why VRAM Wins)](https://vettedconsumer.com/rtx-5090-vs-rtx-pro-6000-for-ai-a-benchmark-deep-dive-and-why-vram-wins/): A developer ran the RTX 5090, a 1,000W Lightning 5090, and the 96GB RTX Pro 6000 through real AI workloads. The surprising takeaway for buyers: raw VRAM beats raw wattage.

## AI mini PCs and servers
- [Beelink SER10 Max (Ryzen AI 9 HX 470): It “Caught” the M4 Pro, But Local-AI Buyers Should Read the Fine Print](https://vettedconsumer.com/beelink-ser10-max-ryzen-ai-9-hx-470-it-caught-the-m4-pro-but-local-ai-buyers-should-read-the-fine-print/): Alex Ziskind says AMD’s new “Gorgon Point” mini PC finally caught Apple’s M4 Pro. On one benchmark it did, but for running local LLMs, the memory bandwidth (DDR5-5600 vs the old SER9’s faster LPDDR5X) tells a more cautionary story.
- [Panther Lake's First Mini PC: Wendell Tests the MSI Cubi NUC AI+ (and What Owners Think)](https://vettedconsumer.com/panther-lakes-first-mini-pc-wendell-tests-the-msi-cubi-nuc-ai-and-what-owners-think/): Level1Techs put Intel's new Core Ultra 9 386H Panther Lake in the MSI Cubi NUC AI+ through its paces. Our buyer's summary + real Reddit sentiment on Panther Lake.
- [Mac mini M4 Buyer's Guide: Is Apple's $599 Mini the Best-Value Mac?](https://vettedconsumer.com/mac-mini-m4-buyers-guide-is-apples-599-mini-the-best-value-mac/): At $599, the redesigned Mac mini M4 might be the best-value Mac ever. Here's who should buy the M4, who needs the M4 Pro, and the one spec to watch.

## Edge AI and accelerators
- [NVIDIA Jetson Orin Nano Super: The $249 Way Into Edge AI](https://vettedconsumer.com/nvidia-jetson-orin-nano-super-the-249-way-into-edge-ai/): NVIDIA's $249 Jetson Orin Nano Super packs 67 TOPS for edge AI and robotics. Who it's perfect for, and why it's a learning board, not a 70B-model machine.

## News and analysis
- [Why Everything Got More Expensive: The Memory Crisis, Explained (via Dave2D)](https://vettedconsumer.com/why-everything-got-more-expensive-the-memory-crisis-explained-via-dave2d/): The Steam Deck jumped $300 overnight, and so did laptops, mini PCs, and Macs. Dave2D explains the AI-driven DRAM crisis behind it, plus what it means for when you should buy.

## More coverage
- [Intel Arc B580: The Best Budget GPU, If You Tick One Box](https://vettedconsumer.com/intel-arc-b580-the-best-budget-gpu-if-you-tick-one-box/): 12GB of VRAM for ~$249 makes the Arc B580 the budget value king. But real r/IntelArc owners warn about the Resizable BAR / CPU-overhead catch that can tank its performance.
- [Framework 12 vs the Cheap MacBook: When Repairability Loses to Value](https://vettedconsumer.com/framework-12-vs-the-cheap-macbook-when-repairability-loses-to-value/): Jeff Geerling pits the Framework 12 against Apple's entry MacBook and calls it 'a bad value.' We add real r/framework owner sentiment, and who should still buy it.
- [ASUS ROG Ally X20: The OLED Handheld Fans Wanted, With Two Catches](https://vettedconsumer.com/asus-rog-ally-x20-the-oled-handheld-fans-wanted-with-two-catches/): The ROG Ally X20 finally brings a 120Hz OLED to the Ally line. But it's the same Z2 Extreme chip, and Reddit is alarmed at a reported $2,000+ AR-glasses bundle. The honest take.
- [AMD's RX 9070 GRE: Reviewers and Reddit Agree, Skip It](https://vettedconsumer.com/amds-rx-9070-gre-reviewers-and-reddit-agree-skip-it/): Hardware Unboxed calls the Radeon RX 9070 GRE a 'garbage deal,' and r/radeon agrees. The cut-down specs, the $550 problem, and what to buy instead.
- [RTX 5070 Ti: The Same Chip as the 5080 for Less, Here's the Catch](https://vettedconsumer.com/rtx-5070-ti-the-same-chip-as-the-5080-for-less-heres-the-catch/): The RTX 5070 Ti is the same GB203 die and same 16GB as the 5080, for ~25% less. Real r/nvidia owner math on when it's the smarter buy, and when it isn't.
- [Snapdragon X2 Elite: Qualcomm's Second Try Is the One That Counts](https://vettedconsumer.com/snapdragon-x2-elite-qualcomms-second-try-is-the-one-that-counts/): 18 cores, 5.0 GHz, an 80-TOPS NPU, and a community that's finally warming to Arm-on-Windows. What the Snapdragon X2 Elite changes, with real owner sentiment.
- [RTX 5080 Buyer's Guide: Who Should Buy It (and Who Needs the 5090)](https://vettedconsumer.com/rtx-5080-buyers-guide-who-should-buy-it-and-who-needs-the-5090/): The RTX 5080 is NVIDIA's sensible $1,000 flagship. Here's who it's perfect for, why its 16GB matters for AI work, and when to stretch to the 5090.
- [Surface Laptop 7 Buyer's Guide: The Snapdragon X Elite Laptop, Honestly](https://vettedconsumer.com/surface-laptop-7-buyers-guide-the-snapdragon-x-elite-laptop-honestly/): Microsoft's Snapdragon X Elite ultraportable has MacBook-beating battery and Copilot+ AI from $999, but Windows-on-Arm has one catch. Who should buy it.
- [Intel Built a Chip Just for Handhelds, First Look at the MSI Claw 8 EX AI+](https://vettedconsumer.com/intel-built-a-chip-just-for-handhelds-first-look-at-the-msi-claw-8-ex-ai/): Intel's new Arc G3 handheld chip claims to beat AMD's best by ~42%. A hands-on first look at the MSI Claw 8 EX AI+, and what to buy while you wait.