Asklemmy

96 GB+ of RAM is relatively easy, but for LLM inference you want VRAM. You can achieve that on a consumer PC by using multiple GPUs, although performance will not be as good as having a single GPU with 96GB of VRAM. Swapping out to RAM during inference slows it down a lot.

On archs with unified memory (like Apple's latest machines), the CPU and GPU share memory, so you could actually find a system with very high memory directly accessible to the GPU. Mac Pros can be configured with up to 192GB of memory, although I doubt it'd be worth it as the GPU probably isn't powerful enough.

Also, the 83GB number I gave was with a hypothetical 1 bit quantization of Deepseek R1, which (if it's even possible) would probably be really shitty, maybe even shittier than Llama 7B.

but how can one enter TB zone?

Data centers use NVLink to connect multiple Nvidia GPUs. Idk what the limits are, but you use it to combine multiple GPUs to pool resources much more efficiently and at a much larger scale than would be possible on consumer hardware. A single Nvidia H200 GPU has 141 GB of VRAM, so you could link them up to build some monster data centers.

Nivida also sells prebuilt machines like the HGX B200 which can have 1.4TB of memory in a single system. That's less than the 2.6TB for unquantized deepseek, but for inference only applications, you could definitely quantize it enough to fit within that limit with little to no quality loss... so if you're really interested and really rich, you could probably buy one of those for your home lab.