Run Qwen 2.5 14B on Apple Silicon
Qwen 2.5 14B is the biggest model that comfortably fits a 16 GB Mac: ~9 GB of Q4 weights and a measured 11.7 tokens/sec on a base M4 (median of 3 runs). That is the ceiling of the entry config — noticeably smarter than 7–8B models, still usable for interactive work, and Apache 2.0 licensed.
How much memory does Qwen 14B need?
Weight size is parameter count × bits per weight; recommended memory adds 15% runtime headroom plus a KV-cache allowance for the 131,072-token context window.
| Quantization | Weights | Recommended RAM | Cheapest fit | Notes |
|---|---|---|---|---|
| Q4_K_M | ~9 GB | 16 GB | M4 (Mac mini) | Best quality/size trade-off — the default for most deployments |
| Q8_0 | ~15.7 GB | 24 GB | M4 (Mac mini) | Near-lossless; noticeably better on code and long reasoning |
| FP16 | ~29.6 GB | 40 GB | M4 Pro (Mac mini) | Full precision — research and eval baselines |
Which Mac should you pick?
Generation speed on Apple Silicon scales mostly with memory bandwidth, so the chip choice sets both whether the model fits and how fast it runs.
| Chip | Max RAM | Bandwidth | Fits Qwen 14B? | From |
|---|---|---|---|---|
| M4 · Mac mini | 32 GB | 120 GB/s | Q4 + Q8 | $99/mo |
| M4 Pro · Mac mini | 64 GB | 273 GB/s | Q4 + Q8 + FP16 | $199/mo |
| M4 Max · Mac Studio | 128 GB | 546 GB/s | Q4 + Q8 + FP16 | $286/mo |
| M3 Ultra · Mac Studio | 256 GB | 819 GB/s | Q4 + Q8 + FP16 | $572/mo |
Deploy Qwen 14B in about 5 minutes
- 1
Build a Mac in the constructor — pick an M4 with at least 16 GB of unified memory.
- 2
Pick any Ollama-based stack (or a clean machine) and pull Qwen 2.5 14B with one command — every deployment ships the OpenAI-compatible API.
- 3
Point your existing OpenAI SDK code at the new base_url and ship.
from openai import OpenAI
client = OpenAI(
api_key="mcy_live_...",
base_url="https://dep-xxxx.macyou.cloud/v1",
)
response = client.chat.completions.create(
model="qwen-2-5-14b",
messages=[{"role": "user", "content": "Hello!"}],
)License and what it's good at
Apache 2.0 — Apache 2.0 — unrestricted commercial use. Made by Alibaba Cloud.
- The most capable model per dollar on a 16 GB machine — measured, not estimated
- Meaningful quality jump over 7–8B on reasoning and code
- Apache 2.0
Typical workloads: best-quality single-model setup on the $99/mo config; code assistance where 7b falls short; non-interactive pipelines where 11.7 tok/s is plenty.
Frequently asked questions
Is 11.7 tokens/sec fast enough?
For chat — borderline but workable (about 9 words/sec). For batch pipelines, summarization queues, and agent steps — comfortably yes. If you need 14B-quality at chat speed, an M4 Pro roughly doubles it.
Qwen 14B or 32B?
14B fits 16 GB ($99/mo); 32B needs a 32 GB machine and returns ~2× slower generation on the same chip class. Take 14B for the budget, 32B when quality is the constraint — see the Qwen 32B guide.