Run DeepSeek V3 on Apple Silicon
DeepSeek V3 is a 671-billion-parameter Mixture-of-Experts model with 37B active per token. MoE routing makes generation faster than a dense model of this size, but ALL expert weights must sit in memory: a Q4 build is roughly 400 GB. On Apple Silicon that means a Thunderbolt 5 cluster — two M3 Ultra 256 GB nodes pooling unified memory is the realistic entry configuration.
How much memory does DeepSeek V3 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 | ~406.8 GB | 480 GB | Thunderbolt 5 cluster | Best quality/size trade-off — the default for most deployments |
| Q8_0 | ~712.9 GB | 832 GB | Thunderbolt 5 cluster | Near-lossless; noticeably better on code and long reasoning |
| FP16 | ~1342 GB | 1560 GB | Thunderbolt 5 cluster | Full precision — research and eval baselines |
Realistic configs: 2 × M3 Ultra 256 GB over Thunderbolt 5 (Q4 with headroom). A single 256 GB machine cannot hold the expert weights — smaller DeepSeek distills (R1 8B/70B) exist for single-node use.
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 DeepSeek V3? | From |
|---|---|---|---|---|
| M4 · Mac mini | 32 GB | 120 GB/s | Cluster only | $99/mo |
| M4 Pro · Mac mini | 64 GB | 273 GB/s | Cluster only | $199/mo |
| M4 Max · Mac Studio | 128 GB | 546 GB/s | Cluster only | $286/mo |
| M3 Ultra · Mac Studio | 256 GB | 819 GB/s | Cluster only | $572/mo |
Deploy DeepSeek V3 in about 5 minutes
- 1
Build a Mac in the constructor — pick your cluster config with at least 480 GB of unified memory.
- 2
Select the DeepSeek V3 stack from the catalog — Ollama runtime, weights pre-loaded, OpenAI-compatible API out of the box.
- 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="deepseek-v3",
messages=[{"role": "user", "content": "Hello!"}],
)License and what it's good at
DeepSeek License — Model license permits commercial use with use-based restrictions; review the DeepSeek Model License for your case. Made by DeepSeek.
- Frontier-class reasoning, math, and code — near closed-API quality
- MoE: 37B active per token, so speed beats dense models of similar quality
- 128K context window
Typical workloads: maximum-quality private reasoning and code generation; distillation teacher for smaller models; frontier evals without sending data to third-party apis.
Frequently asked questions
Can a single Mac run DeepSeek V3?
No — Q4 weights are ~400 GB and the largest single machine (M3 Ultra) tops out at 256 GB. You need a Thunderbolt 5 cluster pooling memory across nodes, or a smaller DeepSeek distill (R1 8B / R1 70B) that fits one machine.
Why does an MoE model need all 671B parameters in RAM if only 37B are active?
The router picks different experts for every token, so any expert can be needed at any moment. Active parameters set the compute per token (speed); total parameters set the memory footprint. That is why V3 generates relatively fast once loaded, but loading it takes datacenter-class memory — or pooled unified memory on a Mac cluster.
What is the cheapest way to get DeepSeek-quality output on one Mac?
Run a DeepSeek R1 distill: R1 8B fits a 16 GB M4 (we measured 20 tokens/sec — see our benchmarks page), and R1 70B-class distills fit a 64 GB M4 Pro at Q4.