DevPulse → Can I Run It? → Qwen 3 8B

Can I Run

Qwen 3 8B

Mid-tier dense model with thinking-mode support. Strong general-purpose pick for 16 GB Macs.

ChatCodeReasoningMultilingualTool Use
8B
Parameters
128K
Context
5.5 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
Q4_K_MREC45.5 GBGoodRuns great
Q5_K_M56.5 GBGoodRuns great
Q8_089.5 GBExcellentRuns great
F161617.0 GBLosslessRuns OK

Which Mac can run Qwen 3 8B?

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
Barely fits
~3 GB for apps
16 GB
Runs well
~11 GB for apps
24 GB
Runs well
~19 GB for apps
32 GB
Runs great
~27 GB for apps
36 GB
Runs great
~31 GB for apps
48 GB
Runs great
~43 GB for apps
64 GB
Runs great
~59 GB for apps
96 GB
Runs great
~91 GB for apps
128 GB
Runs great
~123 GB for apps
192 GB
Runs great
~187 GB for apps

Tips for running Qwen 3 8B

1 Sweet-spot Qwen 3 size — Q4_K_M fits alongside Chrome and an IDE on 16 GB

2 Toggle thinking mode for harder reasoning tasks

3 Apache 2.0 license — production-ready

Skip the cloud GPU bill

Why pay $3/hr for an A100 when your Mac has unified memory sitting idle? DevPulse tells you if Qwen 3 8B fits right now — factoring in Chrome, Docker, and everything else hogging your RAM.

Download Free

Model details

Provider
Alibaba
Architecture
dense
License
Apache 2.0
Commercial use
Yes
Min RAM
16 GB
Recommended
24 GB
Released
2025-04
Ollama
qwen3:8b
VRAM estimates from CanIRun.ai. Data sourced from llama.cpp, Ollama, and LM Studio.

Tokens per second on Apple Silicon

How fast will Qwen 3 8B 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 (5.5 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/s8 GB~9 tok/s
M2100 GB/s8 GB~13 tok/s
M3100 GB/s8 GB~13 tok/s
M4120 GB/s16 GB~15 tok/s
M2 Pro200 GB/s16 GB~25 tok/s
M3 Pro150 GB/s18 GB~19 tok/s
M4 Pro273 GB/s24 GB~35 tok/s
M2 Max400 GB/s32 GB~51 tok/s
M3 Max400 GB/s36 GB~51 tok/s
M4 Max546 GB/s36 GB~69 tok/s
M2 Ultra800 GB/s64 GB~102 tok/s
M3 Ultra819 GB/s96 GB~104 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 Qwen 3 8B.

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.

Fix Ollama OOM on Mac →

When Ollama says “out of memory” on a Mac with plenty of RAM. Diagnose and fix in one command.

Similar Models

Related Pages

Llama 3.1 8B
Meta — 8B params
Qwen 3.5 9B
Alibaba — 9B params
Gemma 3 4B
Google — 4B params
Qwen 3 4B
Alibaba — 4B params
Phi-4 Mini 3.8B
Microsoft — 3.8B params
Llama 3.2 3B
Meta — 3B params

Run Qwen 3 8B locally. No GPU required.

While cloud GPU prices keep climbing, your Mac can run Qwen 3 8B for free. DevPulse tells you if it fits alongside your dev tools — before you download 5.5 GB of model weights.

Download for macOS

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