# Vetted Consumer

> Straight 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. The site is non-commercial: no affiliate links, no sponsorship, no payment from any brand we cover.

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 the software that runs them. We also publish free interactive tools for sizing hardware to a model, and expose the same tools to AI agents.

## 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
- [Quant picker](https://vettedconsumer.com/quant-picker/): which GGUF quant to download for your machine, with file size and context budget
- [Cost calculator](https://vettedconsumer.com/cost-calculator/): buy hardware vs rent cloud GPUs vs use an API, with break-even math for your usage
- [Hardware finder](https://vettedconsumer.com/hardware-finder/): answer 4 questions, get 3 straight 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-server/): how to connect, the nine tools, real example responses. Endpoint: https://vettedconsumer.com/mcp (Streamable HTTP, no auth). 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

## About this site
- [Methodology](https://vettedconsumer.com/methodology/): how we research, weight sources, and score hardware
- [How we research](https://vettedconsumer.com/how-we-research/): sources, verification, and what we will not claim
- [Corrections](https://vettedconsumer.com/corrections/): a public, dated log of fixes
- [Disclosure](https://vettedconsumer.com/disclosure/): non-commercial: no affiliate, referral, sponsorship, or review units
- [Glossary](https://vettedconsumer.com/glossary/): local-LLM terms in plain English

## Guides: how local LLMs work
- [Why Is My Local LLM So Slow? The 6 Bottlenecks, in Order](https://vettedconsumer.com/why-is-my-local-llm-slow-the-6-bottlenecks-in-order/): A slow local LLM is almost always one of six things, and they have an order of impact: the model doesn't fit, low bandwidth, a too-dense model, slow prefill, the wrong runtime, or bad settings. How to diagnose each.
- [Local Embedding Models Explained: The Other Model Your RAG Setup Needs](https://vettedconsumer.com/local-embedding-models-explained-for-rag/): An embedding model turns text into vectors so your local RAG can find the right document chunks. What it does, whether it needs a GPU (barely), and which to use.
- [LLM Sampling Settings Explained: Temperature, Top-P, Top-K, and Min-P](https://vettedconsumer.com/llm-sampling-settings-explained-temperature-top-p-top-k-min-p/): What the temperature, top-p, top-k, and min-p sliders in LM Studio and Ollama actually do, and the settings to use for coding, chat, and creative writing.
- [How Much RAM Do You Need to Run a Local LLM in 2026?](https://vettedconsumer.com/how-much-ram-to-run-a-local-llm-2026/): For small models 16GB is fine, but the MoE models everyone runs in 2026 need 64GB to 256GB+ of system RAM, and RAM speed matters as much as capacity. How to size it.
- [The Attention Rebuild: How 2026's Open Models Made 1M Context Fit on Machines You Own](https://vettedconsumer.com/the-attention-rebuild-how-2026s-open-models-made-1m-context-fit-on-machines-you-own/): A dense 70B needs ~655GB of KV cache at 1M tokens, more than the model itself. The 2026 open models rebuilt attention (sliding windows, linear state, hybrids) and the context ceiling moved. The papers, the numbers, and the runtime gotcha.
- [Speculative Decoding, Explained: The Free Speed Toggle Your Local LLM Is Probably Not Using](https://vettedconsumer.com/speculative-decoding-explained-the-free-speed-toggle-your-local-llm-is-probably-not-using/): A draft model guesses, the big model verifies in one parallel pass, and the output is provably identical. How speculative decoding works, the 2026 MTP shift, and the measured case where it makes things slower.
- [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 real 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
- [Kolibri 1: Aleph Alpha's 78B Open Model Reads Only 3.5B Per Token. What It Takes to Run It](https://vettedconsumer.com/kolibri-1-aleph-alphas-78b-open-model-reads-only-3-5b-per-token-what-it-takes-to-run-it/): Kolibri 1 is a 78B mixture-of-experts model with 3.46B active parameters, Apache 2.0, built for German and English. Its windowed attention makes a 262K context cost about 5GB. What Aleph Alpha claims, what owners measured, and why no mainstream runtime loads it yet.
- [Hy4 Preview: Tencent's 770B Apache-Licensed Flagship Is a 512GB Model, and That Settles Who Can Run It](https://vettedconsumer.com/hy4-preview-tencents-770b-apache-licensed-flagship-is-a-512gb-model-and-that-settles-who-can-run-it/): 770B total, 49B active, 1M context, Apache 2.0, and a 435 GiB 4-bit file. What Tencent claims, what the sparse-attention papers do, what llama.cpp and MLX support today, and the one desk-sized machine that runs it.
- [Qwen3.8-Flash-Next: The Qwen4 Preview You Can Run on a 48GB Mac](https://vettedconsumer.com/qwen3-8-flash-next-qwen4-preview-what-runs-it/): Qwen3.8-Flash-Next previews the Qwen4 architecture: 125B with 6B active, and an offloadable 51GB table that lets a 48GB Mac run it. What runs it.
- [GLM-5.3-Flash: A 320B MoE That Wants a 256GB Mac (Despite the Name)](https://vettedconsumer.com/glm-5-3-flash-open-model-spotlight-what-runs-it/): GLM-5.3-Flash is a 320B/18B-active MoE under MIT license with near-frontier scores. 'Flash' is misleading: the floor is 93GB. What actually runs it.
- [Qwen3.8-27B: The Local Model Most People Should Run in 2026](https://vettedconsumer.com/qwen3-8-27b-open-model-spotlight-what-runs-it/): Qwen3.8-27B is the 24GB-tier default: dense 27B, Apache 2.0, near-lossless at 4-bit. The catch is its long thinking. What it is, and what runs it.
- [The Best Open LLM You Can Actually Run Right Now, by VRAM Tier (August 2026)](https://vettedconsumer.com/best-open-llm-to-run-by-vram-tier-2026/): A tiered, current guide to the best open model for your hardware: gpt-oss-20b on 12GB, Gemma 4 31B on 24GB, gpt-oss-120b or DeepSeek V4 Flash on 128GB, and what to rent instead. With the numbers and the reasons.
- [What "Open Weights" Lets You Do: The 2026 Model License Map, Read From the Actual Texts](https://vettedconsumer.com/what-open-weights-lets-you-do-the-2026-model-license-map-read-from-the-actual-texts/): 13 of the latest open models ship under Apache, 7 under MIT, and 7 under five different custom licenses. What each one permits for a local runner, a fine-tuner, and a business, verified against the primary license texts.
- [DeepSeek V4 Flash Tested: Frontier-Class Coding for 79 Cents a Day, and It Runs on a 128GB Box](https://vettedconsumer.com/deepseek-v4-flash-tested-frontier-class-coding-for-79-cents-a-day-and-it-runs-on-a-128gb-box/): DeepSeek's V4-Flash-0731 is out of preview: 284B total, 13B active, MIT, one Index point behind GLM-5.2 at a third the size. What Bijan Bowen's day of testing found, and the hardware that holds it.
- [Every Frontier Open Model Is a MoE Now. Here Is What That Does to Your Hardware Math](https://vettedconsumer.com/every-frontier-open-model-is-a-moe-now-what-that-does-to-your-hardware-math/): Kimi K3, DeepSeek V4, Inkling, GLM-5.2, gpt-oss: every 2026 open release is a sparse MoE, with totals exploding and active parameters pinned at 3-50B. The research behind the convergence, and the hardware-buying rule it leaves behind.
- [What Hardware Runs Inkling? A 975B Model That Fits on One Box (Unlike Kimi K3)](https://vettedconsumer.com/what-hardware-runs-inkling-975b-fits-on-one-box-unlike-kimi-k3/): Inkling's weights are out, so the quant sizes are measured, not projected. The ranked answer for what runs it, and why a single 512GB Mac Studio is enough.
- [Inkling: Mira Murati's First Open Model Is a 975B MoE You Can Actually Run](https://vettedconsumer.com/inkling-mira-muratis-first-open-model-is-a-975b-moe-you-can-actually-run/): Thinking Machines Lab's first open model is a 975B/41B MoE under Apache 2.0, with weights out now. Where it lands on the benchmarks, and why it fits on one box.
- [What Hardware Runs Kimi K3? The 2.8T Options, Ranked (and When to Just Rent)](https://vettedconsumer.com/what-hardware-runs-kimi-k3-the-2-8t-options-ranked-and-when-to-just-rent/): Kimi K3 is 2.8 trillion parameters, so a single 512GB Mac Studio cannot hold it. Here is the hardware that can, ranked from an 8x H200 node to a Mac cluster, and why renting or the API usually wins.
- [Kimi K3: The Largest Open Model Ever (2.8T Params), and Why Almost No One Can Run It Locally](https://vettedconsumer.com/kimi-k3-the-largest-open-model-ever-2-8t-params-and-why-almost-no-one-can-run-it-locally/): Moonshot's Kimi K3 is the first 2.8-trillion-parameter open model, and the weights are out: 1.56TB native, 104B active per token. Even a 512GB Mac Studio cannot hold the smallest 594GB quant.
- [Kimi K2.7 Code: The Open Trillion-Parameter Coder, and the 594GB Reality of Running It Locally](https://vettedconsumer.com/kimi-k2-7-code-trillion-parameter-open-coder-local-reality/): Moonshot's Kimi K2.7 Code is an open 1T-parameter coder you can download, then discover the weights are ~594GB, more than any single machine holds. What it claims, what the community finds, and what it would really take to run, for the 99% who should just rent or API it.
- [GPT-5.6 is here, and you can't run it. Here's what you can run instead.](https://vettedconsumer.com/gpt-5-6-is-here-and-you-cant-run-it-heres-what-you-can-run-instead/): OpenAI's GPT-5.6 (Sol, Terra, Luna) launched as a closed, API-only preview gated to a few approved companies. Open weights now trail the frontier by about four months. Here's the best one you can run yourself today, the hardware it needs, and what self-hosting costs versus the API.
- [Qwen-AgentWorld-35B-A3B: a local 'world model' you can run at home](https://vettedconsumer.com/qwen-agentworld-35b-a3b-a-local-world-model-you-can-run-at-home/): Alibaba shipped an agentic 'world model' as open weights. What it is, what the early community makes of it, and the real hardware it takes to run, with no first-hand testing claims.
- [MiniMax M3: The First Open-Weight Multimodal Frontier Model (and the License Catch)](https://vettedconsumer.com/minimax-m3-the-first-open-weight-multimodal-frontier-model-and-the-license-catch/): MiniMax M3 is the first open-weight frontier model that natively reads text, images and video. But it ships under a restricted license, and running 428B locally is a server-or-big-Mac job.
- [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 real hardware reality of running it at home.

## Unified-memory machines (Macs, Strix Halo, DGX Spark)
- [MLX vs llama.cpp on a Mac: Which Is Faster, and When the Answer Flips](https://vettedconsumer.com/mlx-vs-llama-cpp-on-a-mac-which-is-faster-and-when-the-answer-flips/): MLX generates 20–40% faster than llama.cpp on short prompts, and the measurements show why. At 146K tokens of context the lead reverses and MLX runs at half speed. The numbers, the M5 change, Ollama's switch, and which engine to use for what.
- [Mac mini M4 (32GB) first-party benchmark: Qwen3 4B and 8B on llama.cpp, measured on our own machine](https://vettedconsumer.com/mac-mini-m4-32gb-first-party-benchmark-qwen3-llama-cpp/): Our own Mac mini M4 with 32GB, llama.cpp b10929, Qwen3 4B and 8B at Q4_K_M: prompt speed, generation speed, and what an 8K context does to both. Method and raw numbers included.
- [M6 Mac mini vs M5 Pro Mac mini for Local AI: Is the $899 Mini a Dead End?](https://vettedconsumer.com/m6-mac-mini-vs-m5-pro-mac-mini-for-local-ai/): The $899 M6 Mac mini caps at 32GB and 170 GB/s; the M5 Pro doubles bandwidth to 307 GB/s at 64GB. For local LLMs, which mini is worth it.
- [What to Pre-Download for a 256GB or 512GB Mac Studio](https://vettedconsumer.com/what-to-predownload-256gb-512gb-mac-studio/): The frontier open models worth a big M5 Ultra's memory, by tier: GLM-5.3-Flash, DeepSeek V4 Flash, Qwen Flash-Next, Hy4, with real M3 Ultra speeds.
- [M5 Max vs M5 Ultra for Local LLMs: The 96GB-vs-128GB Trap](https://vettedconsumer.com/mac-studio-m5-max-vs-m5-ultra-local-llm-96gb-128gb/): At the same storage, a 128GB M5 Max costs $100 less than a 96GB M5 Ultra. For big MoE models that 32GB matters more than bandwidth. Which to buy.
- [Which Mac Should You Buy to Run Local LLMs in 2026? A Memory-First Buyer's Guide](https://vettedconsumer.com/which-mac-for-local-llms-2026-buyers-guide/): A memory-first guide to choosing a Mac for local LLMs: how much unified memory and bandwidth each model tier needs, real measured tok/s by chip, the prompt-processing caveat, and whether to wait for the new M5 Mac Studio.
- [The M5 Mac and Local LLMs: Apple's New Matmul Hardware Finally Attacks Prompt Processing](https://vettedconsumer.com/m5-mac-local-llm-neural-accelerator-prompt-processing/): The M5's per-core matmul units make prompt processing 3.3-4.1x faster than M4, Apple's own data shows. Generation gains are modest and bandwidth-bound. What the new M5 Max and M5 Ultra Mac Studio mean for local LLMs.
- [Strix Halo vs the Mac for Local AI: The 128GB Matchup, in Other People's Measured Numbers](https://vettedconsumer.com/strix-halo-vs-the-mac-for-local-ai-the-128gb-matchup-in-other-peoples-measured-numbers/): Tom's Hardware measured all three 128GB platforms; the Strix community published per-backend numbers; owners posted their power bills. The Mac is 1.25-2.26x faster at decode and 4x the price. The full matchup, charted.
- [Beelink GTR9 Pro: A 128GB Local-AI Powerhouse, With One Catch to Check First](https://vettedconsumer.com/beelink-gtr9-pro-a-128gb-local-ai-powerhouse-with-one-catch-to-check-first/): Ryzen AI Max+ 395, 128GB unified memory, dual 10GbE: the GTR9 Pro is a local-AI dream. But real owners warn about an early ethernet defect; here's how to buy the fixed version.
- [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 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
- [Does a Second GPU Make Your Local LLM Faster? Layer Split vs Tensor Parallel, Explained](https://vettedconsumer.com/does-a-second-gpu-make-your-local-llm-faster-layer-split-vs-tensor-parallel-explained/): llama.cpp's default layer split adds memory, not speed: two 3090s generate at one card's pace. Tensor parallel reads both cards at once but pays two synchronisations per layer. The mechanism, the measurements, and when PCIe lanes and NVLink matter.
- [AZisk's Quad RTX Pro 6000 Build: 384GB of VRAM, and 71% of Every Agent Turn Never Touches It](https://vettedconsumer.com/azisks-quad-rtx-pro-6000-build-384gb-of-vram-and-71-of-every-agent-turn-never-touches-it/): Alex Ziskind built a four-GPU, 384GB Threadripper machine, then measured that 71% of each coding-agent turn happens off the GPU. One agent: an 8-core chip ties a 32-core. Sixteen agents: four times faster. The numbers, checked against other quad-card builds.
- [You Can Run DeepSeek V4 Flash on a Single RTX 3090. The Catch Is RAM, Not the GPU](https://vettedconsumer.com/run-deepseek-v4-flash-single-rtx-3090-the-catch-is-ram/): DigitalSpaceport ran the full 284B DeepSeek V4 Flash on one used RTX 3090 plus system RAM at ~10-11 tok/s. Usable for chat, and the real bottleneck turns out to be how much (and how fast) RAM you have.
- [Intel Arc Pro B60: 192GB of VRAM the Cheap Way, and What It Really Costs](https://vettedconsumer.com/intel-arc-pro-b60-192gb-of-vram-the-cheap-way-and-what-it-really-costs/): Four dual-GPU Intel cards promise 192GB of VRAM for a fraction of NVIDIA's price. We checked what that card actually costs in the US, and why two single B60s are the better buy.
- [The Cheapest Way to Run a 70B Model Locally in 2026 (What Owners Actually Use)](https://vettedconsumer.com/the-cheapest-way-to-run-a-70b-model-locally-in-2026-what-owners-actually-use/): You don't need a $10K rig to run 70B locally. Real r/LocalLLaMA owners on the cheapest paths (MI50 stacks at ~$120, used 3090s, or a quiet $1,500 Strix Halo box) and the tradeoffs of each.
- [Two Used RTX 3090s vs One RTX 5090 for Local LLMs: 48GB and a 70B, or 32GB and Raw Speed?](https://vettedconsumer.com/two-used-rtx-3090-vs-one-rtx-5090-for-local-llms/): Two used 3090s (48GB, ~$1,800) cost about half a 5090 and run a 70B the 5090 can't fit. But the 5090 is ~1.5 to 1.7x faster on anything up to 32B, simpler, and lower power. Owner numbers on both, and which one your model needs.
- [A $1,000 Local-AI Server With 32GB of VRAM: Inside a Triple-3060 OptiPlex Build](https://vettedconsumer.com/1000-dollar-32gb-vram-triple-3060-optiplex-local-ai-server/): A single 32GB RTX 5090 runs ~$2,900. This build reaches the same 32GB for about $1,000: three used RTX 3060-class cards in a repurposed Dell OptiPlex, idling at 55W. The benchmarks, the Mixture-of-Experts lesson (a 26B runs 4x faster than a 27B), and what it can and can't run.
- [RTX 5090 vs RTX 4090 for Local LLMs: What 32GB and 78% More Bandwidth Really Buy](https://vettedconsumer.com/rtx-5090-vs-rtx-4090-for-local-llms-what-32gb-and-78-more-bandwidth-really-buy/): The 4090 is a used-only card near $2,000; the 5090 costs more and is scarce. For running LLMs locally, here is what the extra 8GB and 78% more bandwidth really get you.
- [RTX 5090 vs Mac Studio M3 Ultra for Local LLMs: What Owners of Both Measured](https://vettedconsumer.com/rtx-5090-vs-mac-studio-m3-ultra-for-local-llms/): The $2,000 GPU vs the big-memory Mac. They almost never run the same model: on anything that fits in 32GB the RTX 5090 is faster and cheaper and crushes prompt processing; the moment a model doesn't fit, the Mac Studio is the only one that holds it. Owner numbers on both.
- [Bandwidth, Not TFLOPS: What Sets Your Local LLM Speed (and Why the Newest Card Isn't Always Fastest)](https://vettedconsumer.com/bandwidth-not-tflops-what-sets-your-local-llm-speed-and-why-the-newest-card-isnt-always-fastest/): We pulled owner benchmarks for 16 GPUs and Macs. Local LLM generation speed tracks memory bandwidth, not TFLOPS. But bandwidth is only a ceiling: a brand-new high-bandwidth card can run slower than an old one until the software catches up. The data, all cited, and how to buy around it.
- [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 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
- [MSI Pro Max Edge AI+: ETA PRIME's First Look at a $5,499 Strix Halo Mini PC](https://vettedconsumer.com/msi-pro-max-edge-ai-eta-primes-first-look-at-a-5-499-strix-halo-mini-pc/): ETA PRIME found a quiet, well-cooled 4-litre box with a built-in PSU and measured power modes of 65, 100 and 120W. It is also the same Ryzen AI Max+ 395 with 128GB that sells for under $2,200 elsewhere. What the extra $3,300 buys a local-LLM user.
- [ServeTheHome Tests a $100,000 DGX Station: 2.25 Billion Tokens a Day, and What It Means for One User](https://vettedconsumer.com/servethehome-tests-a-100-000-dgx-station-2-25-billion-tokens-a-day-and-what-it-means-for-one-user/): Gigabyte's GB300 DGX Station hit 26,000 tokens per second across 512 users in ServeTheHome's tests, and 100-120 per user on 300B-class models. Why the 748GB figure is really 252GB of fast memory, and how it compares with a four-card RTX Pro 6000 build.
- [AZisk Bolted an RTX Pro 6000 Onto a Strix Halo and Ran a 122B Model Across NVIDIA and AMD at Once](https://vettedconsumer.com/azisk-rtx-pro-6000-strix-halo-oculink-122b-model-across-nvidia-and-amd/): Alex Ziskind used the GMKtec X3's OcuLink port to run a 122B model across an RTX Pro 6000 and a Strix Halo APU at once. The measured numbers, and the eGPU lesson underneath them.
- [ServeTheHome's $1K-Cheaper 128GB AI Box Has a Twist: It's a Rebadge](https://vettedconsumer.com/servethehome-bosgame-m5-128gb-ai-box-rebadge-twist/): ServeTheHome found the Bosgame M5 128GB Strix Halo box undercuts AMD's Ryzen AI Halo by ~$1,000, then hit the twist: it's a rebadge. Why the 128GB tier is now a commodity, and what actually differs between boxes.
- [GMKtec EVO-X3 Tested by Wendell: The Fastest Strix Halo Yet, and Its Unholy OcuLink Trick](https://vettedconsumer.com/gmktec-evo-x3-tested-by-wendell-the-fastest-strix-halo-yet-and-its-unholy-oculink-trick/): Level1Techs measured 118 GB/s and ~60 tok/s on Qwen 3.6, then used the X3's OcuLink port to mix NVIDIA and AMD GPUs into one 128GB box. What the fastest Strix Halo config yet offers, and what its price history means.
- [Minisforum MS-A2: The 16-Core Mini Workstation Homelabbers Keep Buying](https://vettedconsumer.com/minisforum-ms-a2-the-16-core-mini-workstation-homelabbers-keep-buying/): A 16-core Ryzen 9, dual 10GbE, and three NVMe slots in a tiny box. Real r/homelab owner takes on the MS-A2: why it's a Proxmox favorite, and the fan-noise and storage caveats.
- [Minisforum MS-01 Buyer's Guide: The Homelab Mini PC With a GPU Slot](https://vettedconsumer.com/minisforum-ms-01-buyers-guide-the-homelab-mini-pc-with-a-gpu-slot/): Dual 10GbE, three NVMe slots, and a real PCIe slot make the Minisforum MS-01 the ultimate homelab mini PC. Real r/homelab owner takes on its strengths, heat, and QC.
- [Unified Memory, Explained: Why Mini PCs Can Run 70B Models a Big GPU Can't (and Where They Slow Down)](https://vettedconsumer.com/unified-memory-explained-why-mini-pcs-can-run-70b-models-a-big-gpu-cant-and-where-they-slow-down/): One idea explains the whole mini PC category: unified memory lets a $2,000 box hold a 70B model no consumer GPU can fit, then decode it slowly. The roofline math, the prompt-processing catch, the NPU red herring, and the owner-measured speeds.
- [Three Mini PCs, One 70B Model: What Clustering Intel's New NUCs Can (and Can't) Do](https://vettedconsumer.com/three-mini-pcs-one-70b-model-what-clustering-intels-new-nucs-can-and-cant-do/): AZisk chained three of Intel's new ASUS NUC 16 Pro mini PCs to run a 70B none of them could hold. It works, but it is slow, and it rewrites how to think about clustering: split a model for size, copy it for speed. The numbers, and when a cluster is worth the money.
- [Mac mini M4 Pro Buyer's Guide: Do You Actually Need the Pro?](https://vettedconsumer.com/mac-mini-m4-pro-buyers-guide-do-you-actually-need-the-pro/): The M4 Pro adds cores, ~2x memory bandwidth, and Thunderbolt 5, but most buyers don't need it. Real r/macmini owner takes on when the Pro beats the base M4.
- [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
- [NPUs and Local LLMs: What Your NPU Is Actually For (and Why It Won't Speed Up Your Tokens)](https://vettedconsumer.com/npu-local-llm-what-its-for-and-why-it-wont-speed-up-tokens/): The NPU in your new laptop is great for always-on AI tasks, but it won't make your local LLM generate tokens faster, because generation is bandwidth-bound. What NPUs are really for.
- [Rockchip RK3588 for Local LLMs: The $150 NPU Board (Orange Pi 5, Radxa Rock 5B)](https://vettedconsumer.com/rockchip-rk3588-for-local-llms-orange-pi-5-radxa-rock-5b/): A $75 to $250 board with a 6-TOPS NPU can run a local LLM: small ones fast, a 7B slowly. The real numbers on Orange Pi 5 and Radxa Rock 5B, why the narrow memory bus (not the NPU) sets the ceiling, and which board to buy.
- [Which Edge Chips Can Run an LLM (and Which Can't)?](https://vettedconsumer.com/which-edge-chips-can-run-an-llm/): TOPS barely predicts it: a $250 Jetson runs an 8B model, a 26-TOPS Hailo can't run one at all. The whole edge field (Jetson, Rockchip, Pi, Coral, Hailo) sorted by what genuinely runs an LLM, and the three specs that decide it.
- [Your Coral and Hailo TPU Can't Run an LLM. Here's Why (and What Hailo-10 Changed)](https://vettedconsumer.com/your-coral-and-hailo-tpu-cant-run-an-llm-heres-why/): Buy a Coral or a Raspberry Pi AI Kit expecting a local chatbot and it won't work. Why vision TPUs physically can't run an LLM (it's memory, not TOPS), and the one exception that just shipped: the Hailo-10.
- [Raspberry Pi 5 (16GB) Buyer's Guide: A $120 Local-AI and Self-Hosting Machine](https://vettedconsumer.com/raspberry-pi-5-16gb-buyers-guide-a-120-local-ai-and-self-hosting-machine/): The 16GB Raspberry Pi 5 can run 30B MoE models locally and host your whole homelab. Real r/LocalLLaMA and r/raspberry_pi owner takes on what it does well, plus its slow-AI limits.
- [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.