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text model · Seed-OSS · macOS

Can I run Seed-OSS 36B Instruct on Apple M4 (16GB)?

Compatibility verdict VRAM threshold engine
No, not enough memory would not load

No. Seed-OSS 36B Instruct needs ~22.5 GB even at Q4_K_M, but Apple M4 (16GB) only has ~10.5 GB usable.

Needs ~22.5 GB Device usable ~10.5 GB

Needs ~22.5 GB even at Q4_K_M, but only ~10.5 GB is usable.

That figure is at a 4k context and moves about ±15% as context length changes.

The gap is about 12 GB: Seed-OSS 36B Instruct needs roughly 22.5 GB at Q4_K_M and Apple M4 (16GB) leaves only about 10.5 GB usable for a model. The lightest tracked hardware that runs Seed-OSS 36B Instruct is the Nvidia GeForce RTX 4090 (24GB) at 24 GB. See Seed-OSS 36B Instruct on Nvidia GeForce RTX 4090 (24GB).

Q4_K_M needed
~22.5 GB
Usable on device
~10.5 GB
Device memory
16 GB
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Which quant fits

Quant ladder vs ~10.5 GB usable
Q2_K
~17.3 GB
Q3_K_M
~19.8 GB
Q4_K_M
~22.5 GB
Q5_K_M
~27.9 GB
Q6_K
~31.7 GB
Q8_0
~38 GB
FP16
~74.5 GB
The line marks Apple M4 (16GB)'s ~10.5 GB budget; rungs past it are too large.

How to run it

On macOS use LM Studio (Polished GUI, ships MLX on Apple Silicon, one-click model downloads.).

Model Seed-OSS
Parameters
36B
Q4_K_M size
20.27 GB
Q8_0 size
35.78 GB
Context
512k
Full Seed-OSS 36B Instruct requirements →
Device macOS
Memory
16 GB unified
Usable for weights
~10.5 GB
Power draw
~65 W
Best runtime
Ollama (MLX backend, preview) / MLX direct
Best models for Apple M4 (16GB) →

What you can run instead

Run Seed-OSS 36B Instruct on other hardware

FAQ

Can Apple M4 (16GB) run Seed-OSS 36B Instruct?

No. Seed-OSS 36B Instruct needs ~22.5 GB even at Q4_K_M, but Apple M4 (16GB) only has ~10.5 GB usable.

How much memory does Seed-OSS 36B Instruct need?

Apple M4 (16GB) does not have enough memory. At Q4_K_M the weights are ~20.27 GB; with KV cache and runtime overhead, budget ~22.5 GB at a 4k context.

What is the best tool to run Seed-OSS 36B Instruct on macOS?

LM Studio for a simple setup; mlx-lm for the most speed. vLLM is NOT a Mac tool, it is a CUDA/Linux serving engine. Unified memory is not a fixed VRAM slice; ~70% is usable for weights.

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Sources

Weights are measured from GGUF files; KV cache and overhead are computed, so totals can vary ~15% with context and runtime. Any tok/s is a bandwidth estimate. See methodology.