Can Mac Studio M5 Ultra 256 GB run DeepSeek-V4-Flash (284B-A13B)?

256 GB unified · 1228 GB/s memory bandwidth · DeepSeek-V4-Flash (284B-A13B) has 284B parameters (13B active per token).

Short answer

Yes, excellent for chat and excellent or better for every workload we test.

Best quantization for chat: 4-bit (150 GB of weights) on LM Studio. Generation: 50–80 tok/s, leaving about 95 GB free for your other apps.

How it rates for each workload

Same model and machine, different jobs. Agents and long documents send far bigger prompts than chat, so they need much more speed and memory.

Workload	Verdict	Quant	Generation	First reply	Context
Chatwith Open WebUI	Excellent	4-bit	50–80 tok/s	under 1 s	8K	Details
Coding questionswith Continue	Excellent	4-bit	45–80 tok/s	≈2 s	16K	Details
Coding in a repositorywith Aider	Excellent	4-bit	45–75 tok/s	≈6 s	32K	Details
Agentic codingwith OpenCode	Excellent	4-bit	45–75 tok/s	≈8 s	32K	Details
Long documents (64K)with Open WebUI	Excellent	4-bit	40–70 tok/s	≈30 s	64K	Details

Speeds are bandwidth-based estimates; no direct benchmark exists for this pair. “First reply” is the time to read the workload’s opening prompt. How we calculate

Get it running

Copy-paste commands for the 4-bit build with a 64K context. Ollama and llama.cpp download the file straight from Hugging Face.

ollama run hf.co/unsloth/DeepSeek-V4-Flash-GGUF:UD-Q4_K_XL

This build is split into 5 files, which Ollama can't import from Hugging Face. Use llama.cpp or LM Studio instead.

Other models for Mac Studio M5 Ultra 256 GB

The best-rated models for chat on this machine.

Everything Mac Studio M5 Ultra 256 GB can run

Cheapest machines for DeepSeek-V4-Flash (284B-A13B)

Hardware rated comfortable or better for chat, lowest price first.

Same chip, other memory: 128 GB, 512 GB

Different tool, context size or workload?

These pages use typical settings. The full check models your exact setup.

Open sourceCheck my exact setup