DeepSeek Coder 33B Instruct on Apple M4 Max (64 GB)

Can the Apple M4 Max (64 GB) run DeepSeek Coder 33B Instruct?

66 pts

Yes, it runs well

Runs well. At Q4_K_M it needs about 26.9 GB of 48.0 GB usable, with an estimated speed of 17.3 tok/s.

Best quant: Q4_K_M
Generation speed: 17.3 tok/s
Prompt processing: 342 tok/s
First token (1K prompt): 3.0 s
Largest safe context: 16K

Download DeepSeek Coder 33B Instruct

Recommended for the Apple M4 Max (64 GB): Q4_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:33b-instruct-q4_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 Q4_K_M file (opens in a new tab)

huggingface.co/TheBloke/deepseek-coder-33B-instruct-GGUF (opens in a new tab)

ollama run hf.co/TheBloke/deepseek-coder-33B-instruct-GGUF:Q4_K_M

Original weights · Hugging Face

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

huggingface.co/deepseek-ai/deepseek-coder-33b-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 (opens in a new tab)

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

Links checked 6 days ago against Hugging Face, Ollama and GitHub.

How the B 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.	18.8 / 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.	9.9 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.	12.9 / 15
Total	66.6 / 100	66 ptsGood (B) · 66 of 100 points: Model quality 18.8/45, Memory fit 25.0/25, Speed 9.9/15, Memory use 12.9/15

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

Every quantization on the Apple M4 Max (64 GB)

Quant	Memory needed	Available	Speed	Fit	Grade · points	Download
`Q2_K`	19.9 GB	48.0 GB	28.4 tok/s	Runs well	61 ptsGood (B) · 61 of 100 points: Model quality 13.9/45, Memory fit 25.0/25, Speed 12.6/15, Memory use 9.5/15	GGUF (opens in a new tab)
`Q3_K_M`	22.9 GB	48.0 GB	22.4 tok/s	Runs well	63 ptsGood (B) · 63 of 100 points: Model quality 16.6/45, Memory fit 25.0/25, Speed 11.2/15, Memory use 11.0/15	GGUF (opens in a new tab)
`Q4_K_M`	26.9 GB	48.0 GB	17.3 tok/s	Runs well	66 ptsGood (B) · 66 of 100 points: Model quality 18.8/45, Memory fit 25.0/25, Speed 9.9/15, Memory use 12.9/15	GGUF (opens in a new tab)
`Q5_K_M`	30.2 GB	48.0 GB	14.3 tok/s	Runs well	67 ptsGood (B) · 67 of 100 points: Model quality 19.2/45, Memory fit 25.0/25, Speed 9.0/15, Memory use 14.5/15	GGUF (opens in a new tab)
`Q6_K`	33.9 GB	48.0 GB	12.0 tok/s	Runs well	67 ptsGood (B) · 67 of 100 points: Model quality 19.6/45, Memory fit 25.0/25, Speed 8.2/15, Memory use 15.0/15	GGUF (opens in a new tab)
`Q8_0`	41.9 GB	48.0 GB	8.8 tok/s	Tight fit	65 ptsGood (B) · 65 of 100 points: Model quality 19.8/45, Memory fit 24.0/25, Speed 7.0/15, Memory use 15.0/15	GGUF (opens in a new tab)
`F16`	73.2 GB	48.0 GB	—	Won't run	5 ptsWon't run (F) · 5 of 100 points: Model quality 5.9/45, 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

73 ptsGreat (A) · 73 of 100 points: Model quality 18.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 5090 Q4_K_M · 66.0 tok/s
71 ptsGood (B) · 71 of 100 points: Model quality 16.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 3090 Q3_K_M · 44.6 tok/s
71 ptsGood (B) · 71 of 100 points: Model quality 16.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 4090 Q3_K_M · 48.0 tok/s
71 ptsGood (B) · 71 of 100 points: Model quality 16.6/45, Memory fit 25.0/25, Speed 14.9/15, Memory use 15.0/15 AMD Radeon RX 7900 XTX Q3_K_M · 39.3 tok/s
67 ptsGood (B) · 67 of 100 points: Model quality 19.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 8.4/15 NVIDIA RTX PRO 6000 Blackwell Q6_K · 45.8 tok/s

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.