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// 71d 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
71d ago
2026-03-17
PUBLISHED
71d ago
2026-03-17
RELEVANCE
8/ 10
AUTHOR
wsebos