Can Mac Pro M2 Ultra 192 GB run DeepSeek-V4-Flash (284B-A13B)?

192 GB unified · 800 GB/s memory bandwidth · DeepSeek-V4-Flash (284B-A13B) has 284B parameters (13B active per token).

Short answer

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

Best quantization for chat: 4-bit (150 GB of weights) on LM Studio. Generation: 35–45 tok/s, leaving about 31 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	Comfortable	4-bit	35–45 tok/s	≈1 s	8K	Details
Coding questionswith Continue	Comfortable	4-bit	35–45 tok/s	≈8 s	16K	Details
Coding in a repositorywith Aider	Acceptable	4-bit	30–45 tok/s	≈25 s	32K	Details
Agentic codingwith OpenCode	Acceptable	4-bit	30–45 tok/s	≈35 s	32K	Details
Long documents (64K)with Open WebUI	Acceptable	4-bit	30–40 tok/s	≈2 min	64K	Details

Speeds are estimates calibrated against measured benchmarks on similar hardware. “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 Pro M2 Ultra 192 GB

The best-rated models for chat on this machine.

Everything Mac Pro M2 Ultra 192 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: 64 GB, 128 GB

Different tool, context size or workload?

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

Open sourceCheck my exact setup