Local AI Silicon Sizer
Calculate exact Unified Memory requirements, KV cache consumption, quantization thresholds, and generation speeds across Apple Silicon and desktop GPUs.
Total Memory Req.
42.8 GB
8.4% of 512 GB
Est. Generation
35.4 t/s
~28 ms / token
Weights Footprint
39.7 GB
Q4_K_M (4.5 bpp)
KV Cache Footprint
2.1 GB
FP8 (1 slot @ 32k)
Memory Allocation Breakdown
42.8 GB / 512 GB (Unified Pool)
Weights (39.7 GB)
KV Cache (2.1 GB)
Metal/CUDA Overhead (1.0 GB)
System Reserve (16.0 GB)
Available Headroom (453.2 GB)
Hardware Ecosystem Capability Comparison
Calculated for current model parameters| Silicon / System | Pool VRAM | Memory Bandwidth | Status | Est. Token Rate | Max Full Context |
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Why Unified Memory Reshaped Local AI
As noted by Marques Brownlee reviewing the M5 Ultra Studio, top-tier silicon improvements have shifted from video rendering pipelines to local machine learning inference.
- No PCIe Bottleneck: Traditional GPU rigs move model weights across PCIe buses (typically 32–64 GB/s). Apple's unified memory fabric grants both CPU and GPU direct zero-copy access up to 1,600 GB/s.
- Monolithic VRAM: While consumer GPUs cap out at 24 GB or 32 GB, an M5 Ultra with 512 GB unified memory can load a full-precision 70B model or a heavily quantized 671B MoE (DeepSeek V3/R1) entirely on a single quiet desktop box.
Token Generation Speed Physics
Autoregressive token generation is fundamentally memory-bandwidth bound, not compute bound:
- During the generation phase (batch size = 1), every newly emitted token requires reading all active model weights from memory into the tensor cores once.
- Formula: Theoretical Tok/sec = Bandwidth (GB/s) ÷ Active Model Footprint (GB)
- Real-world runtime efficiency on Metal (MLX/llama.cpp) typically achieves 70% to 85% of this theoretical ceiling, represented in our calibrated estimates.