Llama 4

Llama 4 local deployment is practical if you match the model to the hardware: run Scout quantized for workstation experiments, use vLLM or SGLang on H100/H200 servers for API serving, and treat Maverick as a multi-GPU or heavily quantized model. Quick answer: what hardware can actually run Llama 4 locally? Llama 4 local deployment is the process of running Meta’s Llama 4 Scout or Llama 4 Maverick weights on hardware you control, from a 24 GB VRAM workstation to an 8xH100 server. Scout is the easier target because it has 17B active parameters, 16 experts, and 109B total parameters; Maverick also activates 17B parameters but has 128 experts and about 400B total parameters. In practice, a quantized Scout build can be useful on one high-end consumer GPU, while production Scout and most Maverick deployments belong on H100, H200, or dual 48 GB workstation hardware. The main mistake is assuming active parameters define memory use. Mixture-of-experts lowers compute per token, but disk, VRAM, and sharding still care about the full model. The takeaway: choose Scout for local iteration and Maverick only when your hardware budget is explicit. ...

Gemma 4 31B scores 89.2% on AIME 2026 — a 330% improvement over Gemma 3 27B’s 20.8% — while Qwen3-235B-A22B leads on GPQA Diamond at 77.2% and Llama 4 Scout holds the record with a 10 million token context window. Three competitive open-source model families launched in 2026, each with distinct architectural advantages that make the choice non-obvious. Gemma 4 leads on reasoning-per-parameter efficiency. Llama 4’s Scout model offers an unmatched context window for processing entire codebases. Qwen 3 provides the strongest raw coding performance at full size. This guide covers the technical and practical differences for developers choosing which family to run locally or deploy in production. ...

Llama 4 Scout and Maverick are Meta’s open-weight multimodal models — available today via multiple API providers with OpenAI-compatible endpoints. Scout offers a 10M-token context window at $0.08–$0.15 per 1M input tokens; Maverick beats GPT-4o on MMLU, HumanEval, and SWE-bench. Here’s how to integrate both. What Is Llama 4? Scout, Maverick, and Behemoth Explained Llama 4 is Meta’s fourth-generation open-weight large language model family, released in April 2026 as a multimodal, Mixture-of-Experts architecture covering three tiers: Scout, Maverick, and the research-preview Behemoth. Scout has 17B active parameters out of ~109B total across 16 experts, with a groundbreaking 10-million-token context window — the largest available in any production API as of May 2026. Maverick scales to ~400B total parameters (still 17B active per forward pass) across 128 experts and delivers benchmark scores of 91.8% MMLU, 91.5% HumanEval, and 74.2% SWE-bench, outperforming GPT-4o and Gemini 2.0 Flash. Behemoth sits at ~2 trillion total parameters with 288B active — still in training and research preview, not yet available via public API. All three models support multimodal inputs (text + images), structured output, function calling, and streaming. The key architectural insight is that active parameter count — not total — determines inference cost, which is why both Scout and Maverick run at the speed of a ~17B dense model while achieving quality far above their class. Meta released these models under a custom Llama 4 Community License that permits commercial use with attribution for most use cases. ...