What can you run with 512 GB of memory?
A model does not get the 512 GB. It gets 384 GB on both machines here sold with 512 GB, because the system keeps the rest. That holds every one of the 39 current models at 32k of context, and the strongest of them is GLM-5.3-Flash.
Run the numbers on GLM-5.3-Flash at 512 GB
Jump to: The machines sold with 512 GB · Models that fit in 512 GB · What 512 GB adds over 256 GB · How much context 512 GB leaves room for · Does a 512 GB machine pay for itself?
The machines sold with 512 GB
There are two machines here with 512 GB, and each hands a model the same amount. The fourth column is what each one holds at 32k of context, and it is the number that machine's own page prints.
| Machine | What a model gets | Price | Models it holds at 32k | Strongest of them | |
|---|---|---|---|---|---|
| Mac Studio M3 Ultra, 512GBprevious | 384 GB | $9,499 | 39 | GLM-5.3-Flash | Run the numbers |
| Mac Studio M5 Ultra, 512GB | 384 GB | not published | 39 | GLM-5.3-Flash | Run the numbers |
List prices, and the memory each maker publishes. A machine marked previous is one that is no longer sold, priced at what it launched at.
Models that fit in 512 GB
Every current model a 512 GB machine here holds at 32k of context, strongest first. The third column is the whole job at once: the weights plus the cache for 32k of context. The fourth says how many of the machines at this size have room for it, which is the part a size on its own cannot tell you. Speeds are on the Mac Studio M5 Ultra, 512GB, the machine this list is cut from; speed follows memory bandwidth rather than memory size, so another machine at 512 GB runs the same model at its own rate, and its page gives every one of them.
| Model | Parameters | Needs at 32k | Machines at 512 GB | Speed on the Mac Studio M5 Ultra, 512GB | |
|---|---|---|---|---|---|
| GLM-5.3-FlashUD-Q4_K_M | 321.3B | 189.5 GB | 2 of 2 | 32 tok/s estimated | Run the numbers |
| Qwen3.8 Flash NextQ4_K_M | 180B | 120.5 GB | 2 of 2 | 74 tok/s estimated | Run the numbers |
| DeepSeek V4-FlashUD-Q4_K_M | 284B | 155.3 GB | 2 of 2 | 49 tok/s estimated | Run the numbers |
| Qwen3.8 27BQ4_K_M | 27.8B | 18.6 GB | 2 of 2 | 48 tok/s estimated | Run the numbers |
| Tencent Hy3Q4_K_M | 295B | 192.9 GB | 2 of 2 | 15 tok/s estimated | Run the numbers |
| Inkling SmallUD-Q4_K_M | 266B | 163.6 GB | 2 of 2 | 43 tok/s estimated | Run the numbers |
| Ling 3.0 flashQ4_K_M | 124B | 81.6 GB | 2 of 2 | 52 tok/s estimated | Run the numbers |
| MiniMax M2.7Q4_K_M | 228.7B | 148.5 GB | 2 of 2 | 25 tok/s estimated | Run the numbers |
| Qwen3.6 27BQ4_K_M | 27.8B | 19 GB | 2 of 2 | 47 tok/s estimated | Run the numbers |
| Qwen3.6 35B-A3BQ4_K_M | 36B | 22.8 GB | 2 of 2 | 143 tok/s estimated | Run the numbers |
| Muse Glimmer 30BQ4_K_M | 29.8B | 17.8 GB | 2 of 2 | 50 tok/s estimated | Run the numbers |
| Gemma 4 26B-A4BQ4_K_M | 25.2B | 17.8 GB | 2 of 2 | 105 tok/s estimated | Run the numbers |
| Qwen3.5 122B-A10BUD-Q4_K_M | 125.1B | 79.1 GB | 2 of 2 | 51 tok/s estimated | Run the numbers |
| Granite 4.2 30BQ4_K_M | 29.3B | 26.3 GB | 2 of 2 | 34 tok/s estimated | Run the numbers |
| Gemma 4 31B itQ4_K_M | 31.3B | 25.8 GB | 2 of 2 | 35 tok/s estimated | Run the numbers |
| GLM-4.7-FlashQ4_K_M | 31.2B | 20.1 GB | 2 of 2 | 102 tok/s estimated | Run the numbers |
| Nemotron 3.5 Lightning 30B-A3BQ4_K_M | 31.6B | 25.7 GB | 2 of 2 | 137 tok/s estimated | Run the numbers |
| Gemma 4 12BQ4_K_M | 12B | 8 GB | 2 of 2 | 113 tok/s estimated | Run the numbers |
| Qwen3.5 9BQ4_K_M | 9.7B | 6.8 GB | 2 of 2 | 133 tok/s estimated | Run the numbers |
| Qwen3.5 4BQ4_K_M | 4.7B | 3.8 GB | 2 of 2 | 236 tok/s estimated | Run the numbers |
| MiniCPM5 2BQ4_K_M | 2.5B | 3 GB | 2 of 2 | 303 tok/s estimated | Run the numbers |
| gpt-oss-120bMXFP4 | 116.8B | 64.6 GB | 2 of 2 | 90 tok/s estimated | Run the numbers |
| Granite 4.2 8BQ4_K_M | 8B | 10.7 GB | 2 of 2 | 84 tok/s estimated | Run the numbers |
| Ling 3.0 tinyQ4_K_M | 7.9B | 6.4 GB | 2 of 2 | 150 tok/s estimated | Run the numbers |
| Mistral Small 4 (119B-2603)Q4_K_M | 119.4B | 74.5 GB | 2 of 2 | 81 tok/s estimated | Run the numbers |
| Qwen3-Coder NextQ4_K_M | 79.7B | 49.2 GB | 2 of 2 | 137 tok/s estimated | Run the numbers |
| Qwen3-Coder 30B-A3BQ4_K_M | 30.5B | 21.8 GB | 2 of 2 | 69 tok/s estimated | Run the numbers |
| Devstral 2 123BQ4_K_M | 125B | 86.7 GB | 2 of 2 | 10 tok/s estimated | Run the numbers |
| gpt-oss-20bMXFP4 | 20.9B | 12.9 GB | 2 of 2 | 124 tok/s estimated | Run the numbers |
| Gemma 4 E4BQAT Q4_0 | 8B | 5.7 GB | 2 of 2 | 104 tok/s estimated | Run the numbers |
| Devstral Small 2 24BQ4_K_M | 24B | 19.7 GB | 2 of 2 | 46 tok/s estimated | Run the numbers |
| LFM2.5 2.6BQ4_K_M | 2.7B | 2.2 GB | 2 of 2 | 408 tok/s estimated | Run the numbers |
| Ministral 3 14BQ4_K_M | 14B | 13.6 GB | 2 of 2 | 66 tok/s estimated | Run the numbers |
| Ministral 3 8BQ4_K_M | 8.9B | 9.8 GB | 2 of 2 | 92 tok/s estimated | Run the numbers |
| Ornith 1.5 35B-A3BQ4_K_M | 36B | 22.4 GB | 2 of 2 | 145 tok/s estimated | Run the numbers |
| KAT-Coder V2.5 Dev 35B-A3BQ4_K_M | 34.7B | 22.1 GB | 2 of 2 | 143 tok/s estimated | Run the numbers |
| Laguna XS 2.1Q4_K_M | 33.4B | 21.7 GB | 2 of 2 | 112 tok/s estimated | Run the numbers |
| Ornith 1.5 9BQ4_K_M | 9.7B | 6.9 GB | 2 of 2 | 131 tok/s estimated | Run the numbers |
| Spark-X2.5 4BQ4_K_M | 4.1B | 3.9 GB | 2 of 2 | 233 tok/s estimated | Run the numbers |
For scale, the calculator starts from 80 tok/s for a hosted API and times a local machine against it. 22 of the 39 above reach it, and the slowest is 10 tok/s.
Superseded models are left out of the count, because what a machine is worth buying for is what you would run on it today; the leaderboard ranks every model this site lists, older ones included. Where the cache figure comes from is one section of the memory guide.
What 512 GB adds over 256 GB
The machine here with 512 GB that hands a model the most of it is the Mac Studio M5 Ultra, 512GB, at 384 GB, and it holds 39 of the 39. At 256 GB the most is 192 GB, on the Mac Studio M5 Ultra, 256GB, holding 38. The step adds Tencent Hy3. The strongest either way is GLM-5.3-Flash.
The cheapest machine at 512 GB is the Mac Studio M3 Ultra, 512GB at $9,499. The cheapest at 256 GB is the Mac Studio M5 Ultra, 256GB at $10,799, so the larger size is the cheaper of the two here.
Both sides are read off the machine at each size that hands a model the most, so the comparison is the best case against the best case. The table above has every machine at 512 GB and what each of them holds.
How much context 512 GB leaves room for
The cache grows with the window you ask for, so the same machine holds fewer models the longer the context. Every machine at this size hands a model 384 GB, so one column covers all of them. A model is counted only where its own context ceiling reaches that far.
| Context | 384 GB to a modelMac Studio M5 Ultra, 512GB |
|---|---|
| 4k | 39 |
| 8k | 39 |
| 16k | 39 |
| 32k default | 39 |
| 64k | 39 |
| 128k | 39 |
Counted over the 39 current models, at the quantisation each is listed at and with the cache at 16 bits. The calculator has a switch for a smaller cache, which changes both what fits and how fast a long window runs.
Does a 512 GB machine pay for itself?
On the Mac Studio M3 Ultra, 512GB at $9,499, the cheapest machine at 512 GB with a published price, the model that pays it back soonest at 500k tokens a day is Inkling Small, in 111 years. At 20M tokens a day, agents running most of the day, it is Inkling Small in 2.8 years. The Mac Studio M3 Ultra, 512GB is the previous generation, so that price is what it launched at.
The sum is the same everywhere on this site: what the same work costs to rent, less what the electricity costs to generate it, against the price of the machine. The best buys rank the quickest pay-back at every level of use, and what it costs a month puts the machine and the API bill in the same shape.
Every figure is at 32k of context unless the row says otherwise, with the cache at 16 bits and each model at the quantisation this site lists it at. Weights are the published file sizes on each model's page, and the cache is worked out from the architecture recorded there. What a model gets is the memory the GPU can address, which each machine's page explains. Speeds say whether anybody measured them; where they were not, they are worked out from memory bandwidth. To change the context, the quantisation or the price you would pay, open the calculator. For the sizes either side of this one, the memory guide has the ladder in full.