This article compares Apple M5 Ultra (256GB unified memory) and NVIDIA RTX 5090 (32GB GDDR7) for local LLM inference across 27B–100B+ models, measuring capacity limits, token throughput, power draw, and CUDA vs MLX ecosystems, concluding M5 Ultra excels beyond 32GB while RTX 5090 leads on smaller models.
Federico Viticci's four-day deep test of the M5 Ultra Mac Studio (256GB unified memory) reveals 1.2TB/s bandwidth, 2.5x faster prefill, up to 93.5% faster long-context generation, and superior concurrency for 24/7 local AI agent workflows at near-zero cost, outperforming RTX 5090 in usability.
Hands-on benchmarks of the M5 Ultra 256GB Mac Studio show 45.7% faster single-stream LLM decoding, 3.6x higher multi-user prefill throughput, 43.6% CPU multi-core gains, 67.8% GPU improvement, and 82.8% higher Cyberpunk 2077 4K ray-tracing frame rates versus M3 Ultra, with purchasing guidance.
The article analyzes Apple M5 Ultra 256GB unified memory capacity for local LLM inference, showing Q4-quantized models from 27B to 200B+ fit, but KV cache overhead for long contexts reduces headroom; it compares with multi-GPU setups and advises on purchase decisions.
Apple's M5 Ultra 256GB Mac Studio delivers 35-40% higher multi-core performance than M2 Ultra, 1024 GB/s memory bandwidth, and uniquely fits 671B parameter models like DeepSeek-R1 locally at 10-15 tokens/sec, though it's overkill for general creative work.
The M5 Ultra combines a 36‑core full‑performance CPU, an 80‑core GPU with built‑in neural accelerators, 256 GB of unified memory and ~1100 GB/s bandwidth in a silent, compact box, delivering unmatched desktop AI inference performance compared with RTX 5090 and DGX Spark.
