REDDIT · REDDIT// 25d agoINFRASTRUCTURE

Llama 4 Scout’s 10M context hits deployment limits

A LocalLLaMA user asked whether anyone has actually run Llama 4 Scout at 5M-10M context on MI300X or H200, noting VRAM pressure and KV-cache constraints. The thread and broader ecosystem context point to a gap between the model’s advertised 10M-token capability and what current inference stacks typically expose in practice.

// ANALYSIS

The hot take is that 10M is currently more of an architecture ceiling than a routinely usable production setting for most deployments.

–Meta’s model card and Hugging Face release materials describe Scout as supporting up to 10M context, but this is not what most hosted runtimes expose today.
–Real-world limits vary sharply by provider and setup, with examples like ~300K (Together launch note), ~1.31M (Vertex MaaS quota docs), and 192K (Oracle OCI listing).
–The Reddit reply echoes the same bottleneck pattern: KV-cache memory dominates at long contexts, and aggressive quantization can preserve capacity while hurting coherence.
–For developers, framework choice matters less than end-to-end memory strategy (KV-cache placement, quantization tradeoffs, sharding, and latency tolerance).

// TAGS

llama-4-scoutllminferencegpuopen-weightsresearch

DISCOVERED

25d ago

2026-03-17

PUBLISHED

25d ago

2026-03-17

RELEVANCE

8/ 10

AUTHOR

wsebos