text model · Flint · macOS
Can I run Flint 40B-A2.5B on Apple M2 (16GB)?
No. Flint 40B-A2.5B needs ~25.9 GB even at Q4_K_M, but Apple M2 (16GB) only has ~10.5 GB usable.
Needs ~25.9 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 15.4 GB: Flint 40B-A2.5B needs roughly 25.9 GB at Q4_K_M and Apple M2 (16GB) leaves only about 10.5 GB usable for a model. The lightest tracked hardware that runs Flint 40B-A2.5B is the Nvidia GeForce RTX 5090 (32GB) at 32 GB. See Flint 40B-A2.5B on Nvidia GeForce RTX 5090 (32GB).
- Q4_K_M needed
- ~25.9 GB
- Usable on device
- ~10.5 GB
- Device memory
- 16 GB
Which quant fits
- Parameters
- 39.8B (MoE, 2.5B active)
- Q4_K_M size
- 23.61 GB
- Q8_0 size
- 39.44 GB
- Context
- 256k
- Ollama tag
- flint:40b
- Memory
- 16 GB unified
- Usable for weights
- ~10.5 GB
- Power draw
- ~50 W
- Best runtime
- Ollama (llama.cpp Metal backend) / MLX
What you can run instead
Run Flint 40B-A2.5B on other hardware
FAQ
Can Apple M2 (16GB) run Flint 40B-A2.5B?
No. Flint 40B-A2.5B needs ~25.9 GB even at Q4_K_M, but Apple M2 (16GB) only has ~10.5 GB usable.
How much memory does Flint 40B-A2.5B need?
Apple M2 (16GB) does not have enough memory. At Q4_K_M the weights are ~23.61 GB; with KV cache and runtime overhead, budget ~25.9 GB at a 4k context. It is a Mixture-of-Experts model (39.8B total / 2.5B active), so all experts must stay in memory; memory tracks total params, not active params.
What is the best tool to run Flint 40B-A2.5B 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/flint-40b-a2.5b/apple-m2-16gb) Sources
-
apple.com · 2 sources
-
developer.apple.com · 1 source
-
github.com · 2 sources
-
huggingface.co · 3 sources
-
lmstudio.ai · 1 source
-
ollama.com · 1 source
-
stencel.io · 1 source
-
support.apple.com · 2 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.