#hardware

Is AMD a real option for local LLMs in 2026, or still the 'it works but...' alternative? The honest answer, from running RDNA3 and RDNA4 cards in the lab: usable, genuinely good value at the 24GB tier, with one persistent caveat you need to know before you buy.

A Mac is the only machine where 'unified memory' means a 70B model fits without a discrete GPU. Is MLX on Apple Silicon a real local-AI path in 2026, or a niche? The honest answer for Mac owners — and the one thing that decides whether it's worth it.

A mini PC that runs a 70B model in a box the size of a hardback book — marketing claim or reality? We run the same Ryzen AI Max+ 395 chip in our lab's EvoX2 machine. Here's what it actually does, what it doesn't, and whether the unified-memory mini PC is the real deal.

A complete, tested parts list for a real local-AI machine at the four-figure tier — what to spend on, what to cheap out on, and the one upgrade that's actually worth the money. Built around the 12GB sweet spot for 7B–13B daily and 30B MoE when you need it.

The one number that decides your entire local-AI experience. A practical VRAM-to-model map with real measured numbers from the lab — what fits at each tier (6GB → 48GB+), what 'fits' actually means once you account for context, and the fastest way to answer it for your exact card.

24GB of VRAM for ~$700 used, when the cheapest new 24GB card costs double that. Is the RTX 3090 still the best local-AI buy in 2026? The honest answer: yes for most people, with a clear checklist for what to verify before you buy.

A year ago Intel Arc was still a punchline for AI workloads. The Battlemage refresh (B580/B60) is the thing that changed that — and a year of driver, backend, and ecosystem work has made it genuinely good, with caveats. Here's where Arc actually stands for local AI in mid-2026.

A start-to-finish guide to running LLMs on your own hardware in 2026 — pick the right GPU, choose a backend, pick a quantization, and pick a runtime. Every recommendation is backed by measured benchmarks, not guesses. For beginners and builders.

Every GPU roundup ranks the same NVIDIA cards. Almost none run the same model file on Arc, AMD, and NVIDIA and report the actual tok/s gap. We did. The cross-vendor gap is real. So is the price gap — and that flips the recommendation for budget buyers.

We took Qwen3.6-27B at Q4_K_M — byte-identical weights, verified by Ollama digest — and ran it on all four lab machines: dual 12GB NVIDIA, single 20GB AMD, dual 32GB AMD, and a 24GB Intel Arc. The three CUDA/ROCm machines landed within 10% of each other. The honest answer to 'do I need two GPUs?' is: only once the model stops fitting in one.

Every 'home AI lab' guide describes a single workstation. None describe running models across the machines you already own. Here's the four-node cross-vendor fleet we run — Intel Arc, dual AMD, AMD APU, dual NVIDIA — the routing pattern that ties it together, and what a fleet gets you that a single big box doesn't.

Running Wan, Hunyuan Video, and CogVideoX locally — what's actually runnable on consumer GPUs, what isn't, and the optimizations that help. The honest version, because video is where local hardware hurts most.

Every 'how much power does local AI use' piece multiplies GPU TDP specs by hours and calls it a day. Almost nobody measures actual wall-power during inference. We measured a narrow slice — and the headline finding is that quantization barely moves power draw. The honest version of what we have, what we don't, and the rough cost math.

What it actually takes to run Flux and Stable Diffusion on your own GPU — VRAM floors by model, GPU picks by budget tier, and the five optimizations that make marginal cards viable. No 'best GPU' list — the math.

Forum threads answer 'is dual GPU worth it' with 'depends.' Our four-machine baseline (bm-013) gives the actual crossover: dual-12GB-NVIDIA tied single-32GB-AMD on a 27B model. Below ~18GB of model weight the second card idles; above, dual becomes mandatory or faster. The measured rule, with the trade-offs single-card buyers forget.

We ran the same two 8B models on both cards with byte-identical weights and identical settings. The RX 7900 XT is ~3.9x faster — consistently across both architectures. Here's the full 2x2, what it actually tells you, and where we got the framing wrong the first time.

We benchmarked OpenVINO Model Server 2026.2.1 against llama.cpp Vulkan on the same Intel Arc Pro B60. OpenVINO is ~1.76x faster on a 30B-A3B model — and it works cleanly, contradicting our own earlier note that Battlemage was Vulkan-only. Here's the data and the correction.

Yes, really. A Q4_K_M 30B coder model fits in 12GB of VRAM and runs at usable speed on a single Intel Arc B580. Here's the recipe — with the numbers to back it.