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Llama 4 is Meta’s most recent Llama release (April 2025): natively multimodal (text + image input) mixture-of-experts models with very long context. Both open-weight releases — Scout (109B total / 17B active) and Maverick (400B total / 17B active) — activate only 17B parameters per token, but all expert weights must stay resident in memory. That makes them big-unified-memory or multi-GPU models, not single consumer-GPU pulls: Scout’s Q4-class download is ~67 GB, Maverick’s is ~245 GB. Run the one that fits your hardware, then pair the Tokios connector to reach it as https://api.tokios.com from any OpenAI- or Anthropic-compatible client.
Meta’s model development has moved to the Muse line — the closed-weight Muse Spark and the open-weight Muse Glimmer 30B. Meta has not formally discontinued Llama and the weights stay downloadable, but no Llama 5 has been announced.
Want a Llama that fits a single 24 GB GPU? ollama run llama3.1:8b or ollama run llama3.3:70b — the 3.x line is still the practical single-GPU choice. Llama 4 is what you step up to on 96 GB+ unified memory or a multi-GPU server.

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Variants & tags

Every value below is from the Ollama library and Meta’s Hugging Face model cards. Tags and sizes change as new quantizations ship — confirm against ollama.com/library/llama4 before you standardize on one. Sources: ollama.com/library/llama4/tags, Meta’s Llama 4 announcement, meta-llama/Llama-4-Scout-17B-16E-Instruct.
A third model, Llama 4 Behemoth (~2T parameters), was previewed as the teacher model for the family but has not shipped publicly. Only Scout and Maverick are downloadable.

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Why “17B active” still needs 67 GB

MoE routing activates 17B parameters per token, but the router picks a different subset of experts for every token — so all expert weights must stay loaded. The memory math is driven by total parameters, not active ones:
  • Scout: 109B × ~0.5 bytes at Q4-class ≈ 55 GB of weights, ~67 GB as shipped with overhead
  • Maverick: 400B × ~0.5 bytes ≈ 200 GB of weights, ~245 GB as shipped
What the active-parameter count buys you is speed, not fit: per-token compute is that of a 17B model, so on hardware with enough memory, Llama 4 generates faster than a dense model of similar total size. See memory and offloading for how runtimes handle models near the memory ceiling.

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Hardware fit

Comfort at roughly Q4 with a modest context length. Treat every entry as an estimate — the ground truth is the actual download size plus KV cache for your context, and the long-context headline numbers (10M for Scout) multiply KV-cache memory far past what most machines hold. See what size model fits your GPU? for the sizing math.

Serve it from one endpoint once it runs

Register your Llama 4 deployment with Tokios and call it from any machine with an sk-tok-… key — no inbound ports on the box holding the GPUs.

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Run it — beginner to advanced

Leave Ollama listening on the default http://127.0.0.1:11434. Raise num_ctx past Ollama’s small default to use the long context — but size it against your free memory, not the 10M headline.

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Capabilities

  • Multimodal input: Scout and Maverick accept text and images natively; output is text-only.
  • Context: Scout advertises a 10M-token window and Maverick 1M. Serving anywhere near those lengths is a KV-cache memory problem of its own — see KV cache and context before you plan around them.
  • Languages: Meta’s model cards list 12 supported languages, including English, German, French, Spanish, Portuguese, Hindi, and Vietnamese.
  • Reasoning: General instruction-tuned models — no separate thinking mode. For step-by-step reasoning output, see DeepSeek or gpt-oss.

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Connect it with Tokios

1

Keep your local runtime running

Start Llama 4 in Ollama, vLLM, or another OpenAI-compatible runtime and leave it bound to loopback.
2

Pair the Tokios connector

Sign in at tokios.com/console, open the Setup tab, and click Start pairing to get a one-time claim code (TKS-XXXX-XXXX). Run the installer on the machine that runs the model:
Windows
macOS
Linux
Then approve the device on the Setup or Connectors tab.
3

Register a deployment

In the Models tab, register the upstream model id (for example llama4:scout) under a public deployment name like llama4-tunnel. Clients send the deployment name in the model field, never the local id.
4

Create an API key and call it

In the Keys tab, click Create key, then:

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Caveats

  • No consumer-GPU path. Unlike the 3.x line, neither Llama 4 model fits a 24–48 GB card in VRAM. llama.cpp-based runtimes can offload experts to system RAM, but throughput drops sharply once weights leave the GPU — budget for unified memory or a server before you standardize on Llama 4.
  • License: Llama 4 uses Meta’s Llama 4 Community License — not Apache or MIT. It carries an acceptable-use policy and a separate Meta license requirement for products above 700 million monthly active users. Check it before commercial use.
  • Long context is a memory budget, not a free feature. A 10M-token window at full occupancy needs KV cache far beyond the weight footprint. Size num_ctx / --max-model-len to what your memory actually holds.
  • Tokios passes image content and tool-call fields through to your upstream runtime — it doesn’t add capabilities the runtime or quantization doesn’t support.

Muse Glimmer

Meta’s current open-weight line — Apache 2.0, agentic, and fits one 24 GB card.

What fits your GPU?

Size the model to your VRAM or unified memory before downloading.

Quantization & hardware

Which weight formats run on your GPU, Mac, or unified-memory box.

Quickstart

Pair a connector and register your first model end to end.