Can you run Llama 4 Scout 17B 16E Instruct locally?

Llama 4 family · 2025 · 108.6B parameters (17B activated per token) · Hugging Face model card

How much VRAM does Llama 4 Scout 17B 16E Instruct need at each quantization?

Llama 4 Scout 17B 16E Instruct needs 71.7 GB of VRAM at Q4_K_M. The table below lists weights-only size and total VRAM including overhead for each common quantization level with 4k and 32k token contexts.

config.json 401 gated; layer/head counts not published on vendor card

Method: weights = parameters × bits-per-weight (Q4_K_M ≈ 4.8, Q5_K_M ≈ 5.7, Q6_K ≈ 6.6, Q8_0 ≈ 8.5, FP16 = 16), plus 10% loading overhead, plus KV cache from the verified architecture config (not applicable to this architecture). Source: HF-verified 2026-09-23 (rechecked 2026-09-27) via api safetensors.total=108641793536; config.json HTTP 401 gated so layers/heads/hidden/vocab are NOT recorded. Context 10485760 and active params 17.0B are vendor model-card claims (manufacturer-spec provenance), experts=16 from vendor card. downloadable_safetensors_sum=217283738720 differs from the api dump (multi-format tree). VRAM figures are documented calculations (Q4 ~0.6GB/B on active params), not measurements. No tok/s invented. repo=meta-llama/Llama-4-Scout-17B-16E-Instruct@92f3b1597a195b523d8d9e5700e57e4fbb8f20d3 license=other

Which GPUs can run Llama 4 Scout 17B 16E Instruct locally?

At Q4_K_M with a 4k context, Llama 4 Scout 17B 16E Instruct needs 71.7 GB of VRAM. The lists below are computed live from our GPU database and grouped by how much headroom the card has. Disclosure: CompareAIHardware.com participates in the Amazon Associates program and earns from qualifying purchases through links on this page. Affiliate relationships do not influence our recommendations.

Runs Llama 4 Scout 17B 16E Instruct comfortably (25%+ VRAM headroom)

Minimum GPUs that fit Llama 4 Scout 17B 16E Instruct at Q4_K_M

These GPUs hold the model but leave little headroom — keep contexts short.

Needs 2+ GPUs to run Llama 4 Scout 17B 16E Instruct

One of these cards is too small on its own, but a pair (tensor or pipeline parallel, ~90% efficiency) covers the 71.7 GB requirement. See our multi-GPU guide for setup.

Can a Mac run Llama 4 Scout 17B 16E Instruct?

Yes — these Apple Silicon machines fit Llama 4 Scout 17B 16E Instruct at Q4_K_M, since macOS lets the GPU use about 75% of unified memory. Generation speed is bound by memory bandwidth, so the GB/s column matters as much as capacity.

Can Llama 4 Scout 17B 16E Instruct run on mini PCs, Jetson, or NPU devices?

These edge and NPU devices from our database have enough memory for Llama 4 Scout 17B 16E Instruct at Q4_K_M. Their memory bandwidth is far below discrete GPUs, so expect a fraction of desktop generation speed.

Which pre-built systems can run Llama 4 Scout 17B 16E Instruct?

These mini PCs and workstations from our database fit Llama 4 Scout 17B 16E Instruct at Q4_K_M. Usable-memory figures are conservative: Windows shares about half of system RAM with the GPU by default (Linux can expose more), macOS lets Apple Silicon GPUs use about 75% of unified memory, and Linux unified-memory systems such as GB10 expose roughly 90%. Generation speed is bound by memory bandwidth, so compare the GB/s column before buying.

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