Every Way to Get 512GB of VRAM for Local AI (at Full Context)

512GB of VRAM for local AI doesn't mean 512GB of context. I price every way to get 512GB (Mac Studio M5 Ultra, four NVIDIA DGX Sparks, four RTX PRO 6000s, an EPYC RAM build, 16 AMD MI50s, Strix Halo mini PCs) and work out which models actually run at full context. The number on the box is almost never the number that matters. The model decides. Every token a local LLM reads leaves notes in memory (the KV cache), and that cost per token varies wildly: • DeepSeek V4.1 Flash: 890 bytes per token, so a full 1M-token context is under 1 GB • Muse Spark Open: about 64 KB per token, 69 GB for 1M tokens • GLM-5.3: about 88 KB per token at 16-bit, 94 GB for 1M tokens That's a 100x gap for the same million tokens. DeepSeek also keeps its 189 GiB Engram lookup table on the SSD, so the model everyone buys 512GB for needs closer to 300 GiB of fast memory. GLM-5.3 (523 GiB at 4-bit with full context) and Muse Spark Open (535 GiB) don't fit in 512 at all. What the video covers: • 512GB Mac Studio M5 Ultra: 1.2 TB/s, 80-core GPU only, "coming late October" with no price. macOS gives the GPU about 75% by default (384 GiB), and the override resets on reboot. Two 256GB M5 Ultras over Thunderbolt 5 are the orderable 512 today. • Four DGX Sparks ($6,950 each, $27,800): DeepSeek V4.1 Flash at the full 1M context with an 8M-token cache pool. 87.7 tok/s for one user, about 5,000 tok/s prefill, 342.7 tok/s across 16 users. Keep memory allocation under 92%. • Four RTX PRO 6000 Blackwell Max-Q cards: only 384GB, but faster. 249.1 tok/s and 17,982 tok/s prefill, so a 1M prompt reads in about a minute. $16,000 each, or $9.96/hr rented on RunPod. • The $6,000 dual-EPYC DeepSeek R1 build from January 2025: its 24 x 32GB DDR5 RDIMMs now cost $31,200 on their own (used listing). • Cheap 512s that charge you in time: 16x AMD MI50 32GB (GLM-5.2 at 12.2 tok/s, about 200 tok/s prefill) and two Strix Halo MS-S1 MAX mini PCs (about 430 tok/s prefill). • Prefill vs decode, prompt caching, and why compressing the KV cache cost GLM-5.2 ten points on Terminal-Bench (70.8% vs 81%). • Local vs API: DeepSeek V4.1 Flash costs $0.15 in / $0.60 out per million tokens (off-peak). At one user, four Sparks take about 17 years to pay back. At 16 busy sessions it's about 4.3 years. Speeds are individual owners' measurements, not averages. Prices were checked on October 17, 2026; check them again the day you buy. CHAPTERS 00:00 Why everyone is shopping for 512GB 01:40 Why 512 looks like the number 03:18 The KV cache: 890 bytes vs 88 KB per token 05:26 The 189 GB that stays on SSD (Engram) 06:56 512GB Mac Studio M5 Ultra 09:32 Four DGX Sparks 11:29 Four RTX PRO 6000s 13:45 The $6,000 RAM build, now $31,200 15:19 16x AMD MI50 and Strix Halo 16:58 Where 512GB stops being enough 18:54 Local vs API: who should buy the box 20:39 Pick the model, then the memory SOURCES • DeepSeek V4.1 Flash model card: https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash • DwarfStar V4.1 GGUFs (antirez): https://huggingface.co/antirez/deepseek-v4.1-flash-gguf • MiaAI Lab, DeepSeek V4.1 Flash on 4x DGX Spark: https://github.com/MiaAI-Lab/DeepSeek-v4.1-Flash-DGX-Sparks • local-inference-lab, 4x RTX PRO 6000: https://github.com/local-inference-lab/rtx6kpro/blob/master/models/deepseek-v4.1-flash.md • Meta, Muse Spark Open on two Mac Studios: https://research.meta.ai/blog/muse-spark-open • Muse Spark Open config (16 of 62 layers full attention): https://huggingface.co/unsloth/Muse-Spark-Open-816B-A42B • GLM-5.3 config: https://huggingface.co/zai-org/GLM-5.3 • Mac Studio specs: https://www.apple.com/mac-studio/specs/ • macOS GPU memory cap: https://github.com/ivanopcode/devnote-override-macos-metal-vram-cap • DGX Spark price: https://www.theregister.com/a/5300622 • r/LocalLLaMA, 4x DGX Spark numbers vs Wccftech: https://www.reddit.com/r/LocalLLaMA/comments/1x79ijg/ • r/LocalLLaMA, 4-bit GLM-5.2 on 4x DGX Spark: https://www.reddit.com/r/LocalLLaMA/comments/1ur41ou/ • r/LocalLLaMA, DeepSeek V4.1 Flash on a 512GB M3 Ultra: https://www.reddit.com/r/LocalLLaMA/comments/1wgy6tm/ • r/LocalLLaMA, RTX PRO 6000 at $16,000: https://www.reddit.com/r/LocalLLaMA/comments/1vm5e14/ • r/LocalLLaMA, 16x MI50 512GB: https://www.reddit.com/r/LocalLLaMA/comments/1v3myq9/ • Alex Ziskind, M5 Ultra vs 2 DGX Sparks: https://www.youtube.com/watch?v=_yrw6c5gw3E • Alex Ziskind, 8x RTX PRO 6000: https://www.youtube.com/watch?v=C-31AX2TZ9Y • Donato Capitella, DeepSeek V4.1 Flash on Strix Halo: https://www.youtube.com/watch?v=1DaMkTuiCEQ • Matthew Carrigan's $6,000 DeepSeek R1 build: https://x.com/carrigmat/status/1884244369907278106 • RunPod pricing: https://www.runpod.io/pricing • DDR5 RDIMM prices: https://datacenterdisk.com/drives/kingston-32gb-ddr5-5600-sbv1 • Minisforum MS-S1 MAX: https://store.minisforum.com/products/minisforum-ms-s1-max-mini-pc • DeepSeek API pricing: https://api-docs.deepseek.com/quick_start/pricing #LocalAI #MacStudio #DGXSpark