text model · Seed-OSS · macOS
Can I run Seed-OSS 36B Instruct on Apple M1 (8GB)?
No. Seed-OSS 36B Instruct needs ~22.5 GB even at Q4_K_M, but Apple M1 (8GB) only has ~5.5 GB usable.
Needs ~22.5 GB even at Q4_K_M, but only ~5.5 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 17 GB: Seed-OSS 36B Instruct needs roughly 22.5 GB at Q4_K_M and Apple M1 (8GB) leaves only about 5.5 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
- ~5.5 GB
- Device memory
- 8 GB
Which quant fits
How to run it
On macOS use LM Studio (Polished GUI, ships MLX on Apple Silicon, one-click model downloads.).
- Parameters
- 36B
- Q4_K_M size
- 20.27 GB
- Q8_0 size
- 35.78 GB
- Context
- 512k
- Memory
- 8 GB unified
- Usable for weights
- ~5.5 GB
- Power draw
- ~39 W
- Best runtime
- Ollama (llama.cpp Metal backend)
What you can run instead
Run Seed-OSS 36B Instruct on other hardware
FAQ
Can Apple M1 (8GB) run Seed-OSS 36B Instruct?
No. Seed-OSS 36B Instruct needs ~22.5 GB even at Q4_K_M, but Apple M1 (8GB) only has ~5.5 GB usable.
How much memory does Seed-OSS 36B Instruct need?
Apple M1 (8GB) 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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A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/seed-oss-36b/apple-m1-8gb) 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.