Unverified50% confidenceSolutionExact time
On Apple Silicon, the natively recommended approach is MLX 4-bit affine quantization (group size 64), with a 4-bit MLX build occupying approximately 15 GB of unified memory.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
9/11/2026
First Seen
Valid until: 12/10/2026
Sources
Related Entities
Related Claims
VerifiedDense models at the 30B scale are considered the sweet spot for local deployment, capable of running on Apple Silicon devices such as M2 Ultra (192GB) and M4 Pro (48GB), or on an RTX 4090 (24GB VRAM) with the aid of quantization techniques.77% similarUnverifiedLocal inference is constrained by the user's hardware: a 7B-parameter quantized model (such as the Q4 version of Llama 3 8B) has a file size of approximately 4–5 GB, can reach 20–40 tokens per second on a Mac with an Apple M-series chip, and may drop to 3–5 tokens per second on a laptop with only an integrated GPU and 8 GB of RAM.75% similarUnverifiedAfter 4-bit quantization, Llama 3.3 70B has a model size of approximately 40 GB and can run on a Mac Studio with 64 GB of memory or a single professional GPU with 48 GB of VRAM.70% similarUnverifiedUnder hardware conditions of 16 GB of memory, only models within 5 GB can be accommodated, so the developer used the Qwen 2.5 7B 4-bit quantized model, which is approximately 4.8 GB in size, with an 8K context window.70% similar
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