DevPulse → Can I Run It? → Llama 4 Scout 17B

Can I Run

Llama 4 Scout 17B

Meta's first MoE model. 109B total parameters but only 17B active — gives you big-model quality at medium-model memory cost.

ChatCodeReasoningVisionMultilingualTool UseMoE
109B
Parameters
17B
Active (MoE)
512K
Context
58 GB
RAM (Q4_K_M)

RAM by quantization

Lower quantization = less RAM but lower quality. Q4_K_M is the recommended sweet spot for most users.

FormatBitsRAMQualityVerdict
Q3_K_M346 GBModerateTight fit
Q4_K_MREC458 GBGoodNeeds high RAM
Q5_K_M570 GBGoodNeeds high RAM
Q8_08110 GBExcellentMax-spec only

Which Mac can run Llama 4 Scout 17B?

Based on the recommended Q4_K_M quantization. You need RAM for both the model and your running apps — DevPulse calculates this for you. No CUDA installation. No driver hell. Just Apple Silicon doing what Jensen charges $30K for.

8 GB
Can’t run
16 GB
Can’t run
24 GB
Can’t run
32 GB
Can’t run
36 GB
Can’t run
48 GB
Can’t run
64 GB
Close apps first
~6 GB for apps
96 GB
Runs great
~38 GB for apps
128 GB
Runs great
~70 GB for apps
192 GB
Runs great
~134 GB for apps

Tips for running Llama 4 Scout 17B

1 MoE architecture means only 17B params are active per token — fast inference despite 109B total

2 Q3_K_M at 46 GB is the minimum viable option on 64 GB Macs — close everything

3 512K context window is enormous — but longer contexts use more RAM at runtime

4 On 96+ GB Macs, use Q4_K_M for the best quality/memory tradeoff

Skip the cloud GPU bill

Why pay $3/hr for an A100 when your Mac has unified memory sitting idle? DevPulse tells you if Llama 4 Scout 17B fits right now — factoring in Chrome, Docker, and everything else hogging your RAM.

Download Free

Model details

Provider
Meta
Architecture
moe
License
Llama 4 Community
Commercial use
Yes
Min RAM
48 GB
Recommended
64 GB
Released
2025-04
Ollama
llama4-scout
VRAM estimates from CanIRun.ai. Data sourced from llama.cpp, Ollama, and LM Studio.

Tokens per second on Apple Silicon

How fast will Llama 4 Scout 17B run on each chip?

Apple Silicon inference is bandwidth-bound — every generated token streams the model's active weights through unified memory once. Estimates are for single-batch generation at Q4_K_M (58 GB) at ~70% of peak bandwidth (typical llama.cpp / Ollama efficiency). Speculative decoding can lift these another 30-60%.

ChipBandwidthSmallest RAM that fitstok/s (est.)
M168 GB/s—won't fit
M2100 GB/s—won't fit
M3100 GB/s—won't fit
M4120 GB/s—won't fit
M2 Pro200 GB/s—won't fit
M3 Pro150 GB/s—won't fit
M4 Pro273 GB/s—won't fit
M2 Max400 GB/s96 GB~5 tok/s
M3 Max400 GB/s96 GB~5 tok/s
M4 Max546 GB/s96 GB~7 tok/s
M2 Ultra800 GB/s128 GB~10 tok/s
M3 Ultra819 GB/s96 GB~10 tok/s

“Smallest RAM that fits” assumes ~40% headroom for context, OS, and your dev stack. Reclaim VRAM before loading →

Running it offline

Local-AI guides for Llama 4 Scout 17B.

Knowing the model fits is half the problem. The other half is keeping your Mac's unified memory free enough to actually load it, and keeping the load alive across a long session.

CLI for local AI agents →

Pre-flight checks, JSON output, exit codes for scripts. Built for Ollama, llama.cpp, LM Studio, and MLX.

Free VRAM before loading →

The 6-step protocol for clearing unified-memory headroom on Apple Silicon, from cheapest to deepest.

Run a 70B-class model on a MacBook →

Full setup guide. Hardware sizing, real tokens-per-second, the agent-loop pattern, how to keep it from OOM-ing.

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Run Llama 4 Scout 17B locally. No GPU required.

While cloud GPU prices keep climbing, your Mac can run Llama 4 Scout 17B for free. DevPulse tells you if it fits alongside your dev tools — before you download 58 GB of model weights.

Download for macOS

macOS 14+ · Apple Silicon & Intel · Free during launch