How to Run Llama 4 Locally in 2026 (The Honest Hardware Guide)

Let me save you the first disappointment so we can get to the interesting part: Llama 4 did not land as the local-AI king everyone expected. On the coding and reasoning benchmarks that the community actually cares about, the released Scout model underwhelmed against Qwen3 and DeepSeek, and Meta took real heat when it turned out the version topping the LMArena leaderboard was a chat-tuned variant that wasn't the same as the weights you could download. So no, I'm not going to tell you Llama 4 is the best model you can run at home — it usually isn't. What I will tell you is why it's still genuinely worth understanding: its mixture-of-experts design and enormous context window make it one of the most interesting models to run on the unified-memory hardware that's reshaping local AI. That's the real story, and it's the one nobody sells you.

What Llama 4 actually is

Llama 4 is Meta's first natively multimodal, mixture-of-experts (MoE) family. Instead of one dense network where every parameter fires on every token, an MoE model has many "expert" sub-networks and a router that activates only a few per token. Scout has 16 experts and activates 17B of its 109B parameters per token; Maverick has 128 experts and activates 17B of its 400B. The payoff is that inference is as cheap as a 17B model even though the model "knows" far more — you get big-model breadth at small-model compute cost, per token.

For running at home, Scout is the one that matters — Maverick's 400B total puts it firmly in data-center or serious multi-GPU territory. So the rest of this cluster focuses on Scout, with Maverick as the reference point for "when you'd step up."

What you actually need to run Scout

Here's the honest memory ladder for Llama 4 Scout, from "serious workstation" down to "I just want to poke at it." Full-precision is a non-starter at home (~218GB); quantization is the whole game.

Two honest notes on that chart. First, MoE loads everything, so even the quality-usable Q4_K_M quant is ~62GB — which means no single consumer GPU holds a good Scout quant; you need a 64GB+ card, a big unified-memory machine, or you drop to an aggressive low-bit quant. That's why unified memory keeps coming up. Second, remember to leave headroom for the KV cache, which grows with context — Llama 4's long context is the whole point, but at 128k tokens the cache alone can want ~24GB on top of the weights.

Read as a buying guide, the same numbers give this ladder:

The map: pick your path

• Which hardware to buy for Scout — the honest GPU and memory picks, from a single big card to unified memory: best GPU for Llama 4 Scout.
• Scout or Maverick — which one you should actually try, and when Maverick is worth the pain: Llama 4 Scout vs Maverick, locally.
• The quantization math — GGUF sizes, quality trade-offs, and how low you can safely go: Llama 4 quantized: GGUF & memory.
• Is it even the right model? — an honest head-to-head: Llama 4 vs Qwen3 for local use.
• The best fit of all — why unified-memory machines are Llama 4's natural home: running Scout on Strix Halo & Mac Studio.

Llama 4 is one model in a fast-moving field. For the broader hardware picture — which GPU actually matters for any local model — start with my colleague's VRAM-first best-GPU guide.