meta-llama
Meta: Llama 4 Scout
meta-llama/llama-4-scout
Llama 4 Scout 17B Instruct (16E) is a mixture-of-experts (MoE) language model developed by Meta, activating 17 billion parameters out of a total of 109B. It supports native multimodal input...
- Type
- text
- Provider
- meta-llama
- Model ID
- meta-llama/llama-4-scout
- Capabilities
- vision, tools
- Context window
- 1.3M tokens
- Self-hostable
- Yes — runs on your own GPU
- Input price
- $0.11 / 1M tokens
- Output price
- $0.33 / 1M tokens
Cloud price is billed from prepaid credits when a request fails over to the cloud. Open-weight models run free on GPUs you own — the gateway routes to your nodes first.
What it costs to run
cloud · 1M in + 1M out
$0.44
$0.11 for a million input tokens plus $0.33 for a million output tokens, billed from credits only when a request fails over to the cloud.
your hardware
$0 / token
Served from a workstation or on-prem server you own, there is no per-token fee — the marginal cost is power.
Run it on your own hardware
Estimated GPU memory for Meta: Llama 4 Scout (109B parameters), including the KV cache for a 8,192-token context.
- Q4_K_M
- ~64.0 GBfits a 80 GB card
- Q8_0
- ~109.5 GBmulti-GPU or large unified memory
- FP16 / BF16
- ~204.6 GBmulti-GPU or large unified memory
Install the agent on the machine and it dials out to the gateway over a single WebSocket — no port forwarding or inbound firewall rules. Requests for this model go to your node first and fail over to the cloud only when it can't serve.
/// initialize
Call Meta: Llama 4 Scout through one OpenAI-compatible endpoint.
Serve it from hardware you own, with cloud failover when your nodes are busy or offline.
no credit card · 2 nodes free · openai-compatible
Frequently asked questions
- What is the context window of Meta: Llama 4 Scout?
- Meta: Llama 4 Scout accepts up to 1,310,720 tokens (1.3M) of context per request.
- How much does Meta: Llama 4 Scout cost per million tokens?
- Through Wide Area Intelligence, Meta: Llama 4 Scout costs $0.11 per 1M input tokens and $0.33 per 1M output tokens when served from the cloud, billed from prepaid credits. A workload of 1M input plus 1M output tokens costs $0.44.
- Can I run Meta: Llama 4 Scout on my own hardware?
- Yes — Meta: Llama 4 Scout is an open-weight model you can run on a workstation or on-prem server you own. At Q4_K_M it needs roughly 64.0 GB of GPU memory with an 8,192-token context — it fits a single 80 GB card. Wide Area Intelligence routes requests to your own nodes first and fails over to the cloud only when they can't serve.
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