Can I Run It?

• Which Quant Should You Download? Q4_K_M vs Q8_0 vs F16, Decided by Your Hardware

  The download page forces a choice — Q4_K_M, Q8_0, F16, or an IQ variant — and most guides answer it with vibes. This is a constraint selector: your VRAM/unified memory sets the ceiling, your quality tolerance sets the floor, and for almost everyone the answer is Q4_K_M.

• GGUF vs GPTQ vs AWQ: Which Model Format for Your Setup?

  Format confusion is runtime confusion. GGUF belongs to llama.cpp's world (CPU offload, Macs, single-GPU flexibility), GPTQ/AWQ to vLLM (batching, multi-GPU, datacenter efficiency), and EXL3 to the enthusiast edge. This glossary maps format to runtime to hardware so you download the right artifact for your stack.

• Can I Run Qwen3.5 Locally? Picking Your Size from 3B to 235B

  Qwen3.5 spans 3B to 235B parameters. This guide pairs your hardware to the right model size using requirement math, not marketing claims. A single table from 8GB cards to 256GB Macs, with Unsloth Dynamic 2.0 quantization recommendations.

• Can I Run MiniMax M3 Locally? Requirements for the Multimodal 1M-Context Challenger

  MiniMax M3 claims frontier coding and 1M context with native image/video input — but multimodal + long context multiplies memory pressure beyond what the headline parameter count suggests. A transparent look at local VRAM requirements, KV-cache math at 1M tokens, and when cloud becomes the realistic path.

• Can I Run Kimi K2.6 Locally? Hardware for the Top Open Coding Model

  Kimi K2.6 headlines the local-coding tier lists, but honest hardware requirements are shaped by its MoE architecture: enormous parameter count means unified-memory machines and multi-GPU rigs are the only viable local paths. Per-quantization VRAM table, realistic throughput expectations, and the daily-driver coding verdict.

• Can I Run GLM-5.2 Locally? Hardware Requirements for the 1M-Context Flagship

  GLM-5.2 (released June 2026) is open-weight but not runnable on most home hardware. The 1M-context model demands a 256GB-class machine and hits single-digit tokens/sec even then. Honest constraints, the math, and where to go instead.

• Can I Run DeepSeek V4 Flash Locally? VRAM Requirements by Quant

  DeepSeek V4 Flash is a 14B speed-tier model built for local inference and coding. This guide shows the exact VRAM needed per quantization level, which hardware qualifies, and when cloud rental is the smarter choice than buying.

• Hardware to Run a 32B Model Locally: The Sweet Spot Tier

  32B models hit the 2026 sweet spot: near-70B quality at half the hardware cost and power draw. This guide covers the VRAM math, which cards qualify (used 3090, A6000, 4090, some Macs), and when 32B at Q5 outperforms 70B at Q2 in practical work.

• Hardware to Run a 70B Model Locally: VRAM, the 48GB Wall, and Your Real Options

  What it actually takes to run a 70B model at home: the VRAM math, why 48GB is the practical floor at Q4, and the five hardware paths (dual 3090, used A6000, Apple Silicon, DGX Spark, or cloud).

• Hardware to Run a 7B/8B Model Locally: RTX 3090, Apple M3 Max, and Budget Options

  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

  A constraint-first framework for choosing hardware to run AI models locally. Covers VRAM, memory bandwidth, quantization, Apple Silicon, DGX Spark, and budget paths — so you buy once and regret nothing.

• Quantization: What It Means for Local AI and Why It Matters

  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.