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Can the Apple M4 Pro (24 GB) run DeepSeek Coder V2 Lite (16B)?

77 ptsGreat (A) · 77 of 100 points: Model quality 22.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15

Yes, it runs well

Runs well. At Q5_K_M it needs about 13.3 GB of 16.1 GB usable, with an estimated speed of 49.7 tok/s.

Best quant
Q5_K_M
Generation speed
49.7 tok/s
Prompt processing
2,377 tok/s
First token (1K prompt)
0.4 s
Largest safe context
95K

Download DeepSeek Coder V2 Lite (16B)

Recommended for the Apple M4 Pro (24 GB): Q5_K_M. Every other quantization has its own download link in the table below.

Quickest · Ollama

One command downloads a ready-to-run version and starts a chat.

ollama run deepseek-coder-v2:16b-lite-instruct-q5_K_M

Get Ollama at ollama.com/download (opens in a new tab) · model page (opens in a new tab)

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

Download the Q5_K_M file (opens in a new tab)

huggingface.co/lmstudio-community/DeepSeek-Coder-V2-Lite-Instruct-GGUF (opens in a new tab)

ollama run hf.co/lmstudio-community/DeepSeek-Coder-V2-Lite-Instruct-GGUF:Q5_K_M

Original weights · Hugging Face

Full-precision files from DeepSeek, for fine-tuning, vLLM, Transformers.

huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from DeepSeek. The weights themselves download from Hugging Face.

github.com/deepseek-ai/DeepSeek-Coder-V2 (opens in a new tab)

git clone https://github.com/deepseek-ai/DeepSeek-Coder-V2

Links checked 1 week ago against Hugging Face, Ollama and GitHub.

How the A grade is scored

Every result gets up to 100 points from four parts. The points decide the letter grade.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.22.7 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.25.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.15.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.15.0 / 15
Total77.7 / 10077 ptsGreat (A) · 77 of 100 points: Model quality 22.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15

Grade scale: S 85+ · A 72+ · B 58+ · C 42+ · D 25+ · F under 25 points.

Every quantization on the Apple M4 Pro (24 GB)

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 8.4 GB 16.1 GB 98.7 tok/s Runs well 68 ptsGood (B) · 68 of 100 points: Model quality 16.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 12.1/15
Q3_K_M 9.8 GB 16.1 GB 77.7 tok/s Runs well 73 ptsGreat (A) · 73 of 100 points: Model quality 19.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 14.1/15 GGUF (Q3_K_L) (opens in a new tab)
Q4_K_M 11.7 GB 16.1 GB 60.1 tok/s Runs well 77 ptsGreat (A) · 77 of 100 points: Model quality 22.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q5_K_M 13.3 GB 16.1 GB 49.7 tok/s Runs well 77 ptsGreat (A) · 77 of 100 points: Model quality 22.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q6_K 15.0 GB 16.1 GB 41.7 tok/s Tight fit 77 ptsGreat (A) · 77 of 100 points: Model quality 23.2/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q8_0 18.8 GB 16.1 GB 26.0 tok/s Heavy offload 54 ptsUsable (C) · 54 of 100 points: Model quality 23.4/45, Memory fit 4.0/25, Speed 12.1/15, Memory use 15.0/15 GGUF (opens in a new tab)
F16 33.6 GB 16.1 GB — Won't run 7 ptsWon't run (F) · 7 of 100 points: Model quality 7.0/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 Original weights (opens in a new tab)

Memory figures assume 8K of context. Longer conversations or documents grow the KV cache.

Other hardware that runs it

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.