VRAM > Unified Memory
stop getting baited into mac minis or dgx sparks for your local weights' write functions
I see everyone trying to virtue signal that they are techies and ‘with the times’ by just going out and buying unified memory devices to then post an ollama screenshot showing tok/s…? As if that were the end game and they ‘figured it all out’. Case closed?
That shows real builders you are actually the exact opposite. Someone has to say it. I remember even just random twitter encounters humbled me and I was thankful for it because I then learned (always learning).
Gonna name some common names: new Steam Machine, Apple Mac Mini, NVIDIA DGX Spark, and AMD’s new spark competitor box that also comes in at like $3k). These are poopy devices to buy for the price and you dont look cool because you got openclaw leaking your data everywhere on it. DON’t BUY lol.
Instead…do something like this:
A lot of you guys got rekt into buying noticeably worse performing hardware for agents by opting out of GPUs and into bad marketing (mac minis, DGX Spark, aka any device utilizing unified memory instead of VRAM). OpenClaw was worth testing an old mac mini or something but not to roll out across your enterprise without any security functions like good loorrrd.
So, let me explain why using a GPU to serve your local weights on its VRAM is always going to be better, for cost and performance, at least for the next 5 years.
Tired of seeing open mouth youtube accounts gain a tick off confusing real people trying to enter.
Why?
Well…
The median builder workload punishes the exact thing unified memory is bad at. It also rewards the exact thing VRAM is good at. Few reasons ill get into.
Prompt processing is their daily pain, not really token generation. They are feeding large context constantly. Think agent loops that re ingest history every turn. That reading phase is compute bound and unified memory machines have weak GPUs. Horror story always the same here, something like: “m series holds the model fine but takes 90+ seconds before the first token on a long prompt.” On a real GPU with VRAM, like i am saying, it takes seconds. That gap is the difference between usable and unusable.
Bandwidth, tok/s directly. Token generation is bandwidth bound so not the same as compute. GDDR7//HBM (~1.8 TB/s on a RTX 6000, ~936 GB/s even on an old 3090) vs LPDDR5x (~256-819 GB/s) is a 2-7x gap that shows up as your tok/s. Builders post the tok/s….so, they buy the thing that makes the number go up. Simple.
VRAM does more than inference. Serving multiple users and fine tuning both need FLOPS, not just a big memory pool. Unified memory can hold a 70B but crawls through a training backward pass or chokes under concurrent load. Builders want one box that runs AND trains AND serves. That is GPU sillicon, NOT poopy unified memory.
Tinkering cultures rules. The hobby/career is modular. You start with a used 3090 or 2080, add a second, swap, overclock, resell. Unified memory is literally SOLDERED AND FIXED at purchase, at literal comparable prices to USED GPUs that actually RUN WHAT YOU THINK YOU ARE BUYING. Bought the wrong RAM tier on unified memory? You’re stuck. A used 3090 is around $1,000 right now and is still the cheapest on ramp there is to local ai.
CUDA. People scoff here, but they forget… Almost every tool a builder touches in this realm (vLLM, exLlama, llama.cpp’s best kernels, FlashAttention, the newest quant formats [NVFP4, AWQ, GPTQ], speculative decoding, DFlash, MTP) ships on CUDA FIRST, often CUDA only, and is debugged there first. On a Mac or AMD, you are perpetually a few months behind, hitting “not supported on this backend” walls, or running the slower path. Builders optimize for the “new trick works the day it drops” for content and to stay at the tip of the spear, so that is NVIDIA specific GPU’s, not an argument for the unified mem DGX spark. The net result is that unified memory makes you a second class citizen in the software that matters.
NVIDIA GPUs hold value. You can recoup a lot of your spend on the next upgrade. A soldered box can’t be parted out; when you are done, you sell one thing for one hugely depreciated price.
The GPU is multi purpose. With a GPU, you can do gaming, rendering, video, all things a unified memory box literally cannot do, so a GPU earns its keep even when its not running your intelligence. To me, that is just case in point.
Performance per dollar is about work completed, not sticker price. Even when two machines cost the same, the GPU (remember, VRAM lives on the GPU) finishes the job FASTER. Faster prompt ingest, more tok/s, real concurrency. So, the effective cost per task is lower. You are not paying for hardware, you are paying for THROUGHPUT; VRAM gives you more throughput per dollar spent, and its not a debate.
You can’t turn unified memory into an API endpoint. Remember the bottleneck: to generate one token, the GPU has to stream all the model’s weights through the chip once. That read is the slow, expensive step. So, if you ever decide to serve or batch from your VRAM you can. The GPU can give you more concurrent users because every active request needs its own KV cache to be actually HELD in VRAM. More GPUs = More people that can chat and have access with your local intelligence (an impossibility with unified mem alone). Also, vLLM opened the door to continuous batching, so the requests arrive and finish at different times. vLLM slots new ones into the batch the instant a seat frees up, keeping the bus full and always optimized. This needs both spare compute (unified memory fails) AND spare VRAM to juggle caches!
Serving one user wastes 95% of the GPU. Serving thirty fills the idle compute for almost free. Same weight read, same watts, thirty times the output. That’s the whole reason a GPU pays for itself and a unified memory box can’t: filling the bus only works if the bus has empty seats, and only real GPUs have them! This is why GPU farms existed in the first place.
The used market is a cheat code for VRAM, not unified mem. A flood of ex mining and old gaming 3090s sell for like $900-$1100 (if nice or refurbished shape) for 24 GB of 936 GB/s memory. Nothing in unified memory land comes within range of that bandwidth per dollar. There is no “used mac studio m3 ultra at a third of retail” market haha; soldered machines depreciate as one sealed unit. Go get a used NVIDIA 3090 if you are trying to jump in the arena.
Stop getting rekt.
What do you think?
God-Willing, see you at the next letter
GRACE & PEACE