Llama 4 Scout 17B 16E Instruct — Hardware Requirements & GPU Compatibility
Vision
Llama 4 Scout 17B 16E Instruct is a 108.6B-parameter open language model from Meta in the Llama family. At Q2_K it needs about 50.79 GB of VRAM — see which GPUs and Macs can run it below.
460.7K downloads
1.3K likesSpecifications
- Publisher
- Meta
- Family
- Llama
- Parameters
- 108.6B
- License
- Other
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HuggingFace
How Much VRAM Does Llama 4 Scout 17B 16E Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 50.8 GB | — | 46.17 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 52.3 GB | — | 47.53 GB | 3-bit small quantization |
| Q8_0 | 8.00 | 119.5 GB | — | 108.64 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Llama 4 Scout 17B 16E Instruct?
Q8_0 · 119.5 GBLlama 4 Scout 17B 16E Instruct (Q8_0) requires 119.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 156+ GB is recommended. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Llama 4 Scout 17B 16E Instruct?
Q8_0 · 119.5 GB5 devices with unified memory can run Llama 4 Scout 17B 16E Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (128 GB).
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightBenchmarks
View all 5 →Related Models
Derivatives (1)
Frequently Asked Questions
- How much VRAM does Llama 4 Scout 17B 16E Instruct need?
Llama 4 Scout 17B 16E Instruct requires 50.8 GB of VRAM at Q2_K, or 119.5 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 108.6B × 3.4 bits ÷ 8 = 46.2 GB
KV Cache + Overhead ≈ 4.6 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q2_K50.8 GB- Can NVIDIA GeForce RTX 5090 run Llama 4 Scout 17B 16E Instruct?
No — Llama 4 Scout 17B 16E Instruct requires at least 50.8 GB at Q2_K, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- What's the best quantization for Llama 4 Scout 17B 16E Instruct?
For Llama 4 Scout 17B 16E Instruct, Q3_K_S (52.3 GB) offers the best balance of quality and VRAM usage. Q8_0 (119.5 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 50.8 GB.
VRAM requirement by quantization
Q2_K50.8 GBQ3_K_S ★52.3 GBQ8_0119.5 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Llama 4 Scout 17B 16E Instruct on a Mac?
Llama 4 Scout 17B 16E Instruct requires at least 50.8 GB at Q2_K, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Llama 4 Scout 17B 16E Instruct locally?
Yes — Llama 4 Scout 17B 16E Instruct can run locally on consumer hardware. At Q2_K quantization it needs 50.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Llama 4 Scout 17B 16E Instruct?
At Q2_K, Llama 4 Scout 17B 16E Instruct can reach ~57 tok/s on AMD Instinct MI300X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 50.8 × 0.55 = ~57 tok/s
Estimated speed at Q2_K (50.8 GB)
~57 tok/s~43 tok/s~36 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Llama 4 Scout 17B 16E Instruct?
At Q2_K, the download is about 46.17 GB. The full-precision Q8_0 version is 108.64 GB.
- Which GPUs can run Llama 4 Scout 17B 16E Instruct?
No single consumer GPU has enough VRAM to run Llama 4 Scout 17B 16E Instruct at Q2_K (50.8 GB). Multi-GPU or professional hardware is required.
- Which devices can run Llama 4 Scout 17B 16E Instruct?
8 devices with unified memory can run Llama 4 Scout 17B 16E Instruct at Q2_K (50.8 GB), including Mac Pro M2 Ultra (192 GB), Mac Studio M2 Ultra (192 GB), Mac Studio M4 Max (128 GB), Mac Studio M4 Max (64 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.