Local Deployment of DeepSeek-V4-Flash on Apple Silicon
Technical enthusiasts and developers are reporting successful local deployments of the DeepSeek-V4-Flash model on Apple Silicon hardware, according to unverified reports from community discussions on Reddit. The tests indicate that the model can be run on high-memory Mac configurations, though performance metrics vary significantly depending on the quantization method and hardware architecture used.
Performance Benchmarks on M1 Max Hardware
In a recent report shared on Reddit, a user documented running the DeepSeek-V4-Flash-0731 model on an M1 Max MacBook equipped with 64GB of unified memory. Using the IQ3-XXS quantization (approximately 104GB) and specific patches to the llama.cpp framework, the user reported a decoding speed of approximately 8 tokens per second (tok/s) and a prefill rate of 30 tok/s.
The user noted that the context window was limited to 64k tokens to prevent excessive prefill times. While the user described the performance as “snappy” compared to initial expectations, other community members expressed skepticism regarding the utility of these speeds. Some commenters argued that a prefill rate of 30 tok/s is insufficient for professional workflows, suggesting that rates closer to 200 tok/s are required for practical application.
Comparative Hardware Configurations
The performance of DeepSeek-V4-Flash appears to scale significantly with specialized hardware. One developer reported achieving 25 tok/s for generation and over 500 tok/s for prefill using a multi-GPU setup consisting of three V620 enterprise GPUs. This setup utilized optimized ROCm IQ-quant kernels and tensor split mode within llama.cpp.
In a separate discussion regarding local AI infrastructure, a developer described a high-end workstation utilizing a CNC chassis and a dual RTX Pro 6000 Blackwell rig. This setup, which operates alongside a Mac cluster, is used to serve workloads for a team of 30 developers. The infrastructure is currently configured to host several large language models, including DeepSeek, MiniMax, and Qwen.
Technical Constraints and Scaling
The ability to run these models locally remains heavily dependent on available VRAM and quantization levels. The use of IQ3-XXS quantization was cited as a potential minimum requirement to maintain reliable responses and tool-calling capabilities on 64GB Mac hardware.
As developers continue to test these models across different architectures—including upcoming M5 Max hardware—the industry continues to observe the trade-offs between memory capacity, quantization precision, and the resulting inference speeds required for professional-grade AI deployment.