WIDEAREAINTELLIGENCE
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meta/llama-4-scout-17b-16e-instruct

cloudflare/meta/llama-4-scout-17b-16e-instruct

↓ runs free on your own hardware⚙ tool calling

Meta's Llama 4 Scout is a 17 billion parameter model with 16 experts that is natively multimodal. These models leverage a mixture-of-experts architecture to offer industry-leading performance in text and image understanding.

specs & pricing
Type
text
Provider
cloudflare
Model ID
cloudflare/meta/llama-4-scout-17b-16e-instruct
Capabilities
tools
Context window
131K tokens
Self-hostable
Yes — runs on your own GPU
Input price
$0.30 / 1M tokens
Output price
$0.94 / 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

$1.23

$0.30 for a million input tokens plus $0.94 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.

AI cost calculator →GPU power cost →

Run it on your own hardware

Estimated GPU memory for meta/llama-4-scout-17b-16e-instruct (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.

VRAM calculator →Which GPU do I need? →

/// initialize

Call meta/llama-4-scout-17b-16e-instruct through one OpenAI-compatible endpoint.

Serve it from hardware you own, with cloud failover when your nodes are busy or offline.

Run it on your own hardwareTry it in chat

no credit card · 2 nodes free · openai-compatible

Frequently asked questions

What is the context window of meta/llama-4-scout-17b-16e-instruct?
meta/llama-4-scout-17b-16e-instruct accepts up to 131,000 tokens (131K) of context per request.
How much does meta/llama-4-scout-17b-16e-instruct cost per million tokens?
Through Wide Area Intelligence, meta/llama-4-scout-17b-16e-instruct costs $0.30 per 1M input tokens and $0.94 per 1M output tokens when served from the cloud, billed from prepaid credits. A workload of 1M input plus 1M output tokens costs $1.23.
Can I run meta/llama-4-scout-17b-16e-instruct on my own hardware?
Yes — meta/llama-4-scout-17b-16e-instruct 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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