Yes — with 26.7 GB to spare

LLM-jp 4 33B Thinking at Q4_K_M fits your M4 Max · 64 GB entirely in unified memory at 8K context, at an estimated 12 tokens per second. There is room for its full 64K window.

Fully in unified memory 8K context Q4_K_M · 18.7 GB Apache 2.0 Released 14 Aug 2026 New this week Not in the Ollama library

Japan’s national-institute reasoning model, Japanese and English. A plain dense Llama-style 33B: Q4 is a tight 24 GB fit.

What hardware do I need for LLM-jp 4 33B Thinking? →

The VRAM budget

weights 18.7 GB

Weights 18.7 GB KV cache @ 8K 2.00 GB Runtime overhead 0.6 GB Free 26.7 GB of 48.0 GB

Quantisation ladder

Quant	Weights	Total @ 8K	Max context	Tok/s	Quality	Fit
Q8_0	32.9 GB	35.5 GB	58K	7.0	−0.1% ppl	Long context
Q6_K	25.4 GB	28.0 GB	64K	9.0	−0.4% ppl	Long context
Q5_K_M	21.9 GB	24.5 GB	64K	10	−0.8% ppl	Long context
Q4_K_M	18.7 GB	21.3 GB	64K	12	−1.9% ppl	Recommended
Q3_K_M	15.1 GB	17.7 GB	64K	15	−5.4% ppl	Long context
Q2_K	12.9 GB	15.5 GB	64K	18	−15% ppl	Long context

Quality is the published perplexity delta against f16 weights. Max context assumes an f16 KV cache; q8_0 roughly doubles it.

How to run it

llama.cpp LM Studio MLX

terminal

$ pip install mlx-lm
$ mlx_lm.generate --model mlx-community/llm-jp-4-33b-thinking-4bit \
    --max-tokens 512 --prompt "Hello"

Apple's own array framework. The fastest path on Apple Silicon. More on MLX.

01Download is 18.7 GB. Keep it on an SSD — a first load off a spinning disk takes minutes.

02macOS caps what the GPU may wire down at about 48.0 GB of 64 GB. Raising it with iogpu.wired_limit_mb is possible, and risky.

03There is room to go to the model's full 64K context on this card.

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