text model · Seed-OSS · macOS
Can I run Seed-OSS 36B Instruct on Apple M4 Max (64GB)?
Yes. Seed-OSS 36B Instruct runs on Apple M4 Max (64GB) at Q4_K_M (~22.5 GB of ~48 GB usable).
Runs at Q4_K_M using ~22.5 GB of ~48 GB usable. You have room for Q8_0 for higher quality.
That figure is at a 4k context and moves about ±15% as context length changes. Apple M4 Max (64GB) leaves ~25.5 GB of headroom, room to step up to Q8_0 for higher quality.
- Q4_K_M needed
- ~22.5 GB
- Usable on device
- ~48 GB
- Device memory
- 64 GB
- Best quant
- Q4_K_M
Which quant fits
Running cost · estimate
- Power draw
- ~145 W
- Electricity / 1M tokens
- ~$0.27
- Pays for itself after
- ~15,213M tok
At ~$0.15/kWh and the estimated ~22 tok/s, a million generated tokens costs about $0.27 in electricity. Versus a hosted API at ~$0.5 per million tokens, the ~$3,499 Apple M4 Max (64GB) pays for itself after roughly 15,213 million tokens, so local hardware is mostly a fixed cost, not a per-token one. TDP is the peak draw, so this is an upper bound. Assumptions.
Run it
Pick your tool. All 2 load the same Q4_K_M weights.
llama-cli -hf bartowski/ByteDance-Seed_Seed-OSS-36B-Instruct-GGUF:Q4_K_M lms get bartowski/ByteDance-Seed_Seed-OSS-36B-Instruct-GGUF 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
- 64 GB unified
- Usable for weights
- ~48 GB
- Power draw
- ~145 W
- Best runtime
- MLX direct / Ollama (MLX backend)
You could also run
Run Seed-OSS 36B Instruct on other hardware
FAQ
Can Apple M4 Max (64GB) run Seed-OSS 36B Instruct?
Yes. Seed-OSS 36B Instruct runs on Apple M4 Max (64GB) at Q4_K_M (~22.5 GB of ~48 GB usable).
How much memory does Seed-OSS 36B Instruct need?
Apple M4 Max (64GB) has room to spare. 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.
Embed this
A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/seed-oss-36b/apple-m4-max-64gb) 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.