What can you run with 128 GB of memory?
A model does not get the 128 GB. On the Framework Desktop, 128GB it gets 96 GB and on the DGX Spark, 128GB it gets 119.5 GB, because the system keeps a share of it, and how much differs by machine. That is 33 of the 39 current models at 32k of context either way, and the strongest of them is Qwen3.8 27B.
Run the numbers on Qwen3.8 27B at 128 GB
Jump to: The machines sold with 128 GB · Models that fit in 128 GB · What 192 GB adds over 128 GB · How much context 128 GB leaves room for · Does a 128 GB machine pay for itself?
The machines sold with 128 GB
There are 11 machines here with 128 GB, and what they hand a model is not the same figure. 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 | |
|---|---|---|---|---|---|
| DGX Spark, 128GB | 119.5 GB | $4,699 | 33 | Qwen3.8 27B | Run the numbers |
| Framework Desktop, 128GB | 96 GB | $3,449 | 33 | Qwen3.8 27B | Run the numbers |
| Mac Studio M4 Max, 128GBprevious | 96 GB | $3,499 | 33 | Qwen3.8 27B | Run the numbers |
| GMKtec EVO-X2, 128GB | 96 GB | $3,500 | 33 | Qwen3.8 27B | Run the numbers |
| GMKtec EVO-X3, 128GB | 96 GB | $3,600 | 33 | Qwen3.8 27B | Run the numbers |
| Minisforum MS-S1 Max, 128GB | 96 GB | $3,799 | 33 | Qwen3.8 27B | Run the numbers |
| Beelink GTR9 Pro, 128GB | 96 GB | $4,349 | 33 | Qwen3.8 27B | Run the numbers |
| Corsair AI Workstation 300, 128GB | 96 GB | $4,700 | 33 | Qwen3.8 27B | Run the numbers |
| Mac Studio M5 Max, 128GB | 96 GB | $5,099 | 33 | Qwen3.8 27B | Run the numbers |
| HP Z2 Mini G1a, 128GB | 96 GB | $5,544 | 33 | Qwen3.8 27B | Run the numbers |
| MacBook Pro M5 Max (16-inch), 128GB | 96 GB | $6,999 | 33 | Qwen3.8 27B | 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 128 GB
Every current model the roomiest 128 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 DGX Spark, 128GB, the machine this list is cut from; speed follows memory bandwidth rather than memory size, so another machine at 128 GB runs the same model at its own rate, and its page gives every one of them.
| Model | Parameters | Needs at 32k | Machines at 128 GB | Speed on the DGX Spark, 128GB | |
|---|---|---|---|---|---|
| Qwen3.8 27BQ4_K_M | 27.8B | 18.6 GB | 11 of 11 | 11 tok/s estimated | Run the numbers |
| Ling 3.0 flashQ4_K_M | 124B | 81.6 GB | 11 of 11 | 26 tok/s estimated | Run the numbers |
| Qwen3.6 27BQ4_K_M | 27.8B | 19 GB | 11 of 11 | 11 tok/s estimated | Run the numbers |
| Qwen3.6 35B-A3BQ4_K_M | 36B | 22.8 GB | 11 of 11 | 70 tok/s estimated | Run the numbers |
| Muse Glimmer 30BQ4_K_M | 29.8B | 17.8 GB | 11 of 11 | 11 tok/s estimated | Run the numbers |
| Gemma 4 26B-A4BQ4_K_M | 25.2B | 17.8 GB | 11 of 11 | 52 tok/s estimated | Run the numbers |
| Qwen3.5 122B-A10BUD-Q4_K_M | 125.1B | 79.1 GB | 11 of 11 | 25 tok/s estimated | Run the numbers |
| Granite 4.2 30BQ4_K_M | 29.3B | 26.3 GB | 11 of 11 | 7.8 tok/s estimated | Run the numbers |
| Gemma 4 31B itQ4_K_M | 31.3B | 25.8 GB | 11 of 11 | 7.9 tok/s estimated | Run the numbers |
| GLM-4.7-FlashQ4_K_M | 31.2B | 20.1 GB | 11 of 11 | 50 tok/s estimated | Run the numbers |
| Nemotron 3.5 Lightning 30B-A3BQ4_K_M | 31.6B | 25.7 GB | 11 of 11 | 68 tok/s estimated | Run the numbers |
| Gemma 4 12BQ4_K_M | 12B | 8 GB | 11 of 11 | 26 tok/s estimated | Run the numbers |
| Qwen3.5 9BQ4_K_M | 9.7B | 6.8 GB | 11 of 11 | 30 tok/s estimated | Run the numbers |
| Qwen3.5 4BQ4_K_M | 4.7B | 3.8 GB | 11 of 11 | 54 tok/s estimated | Run the numbers |
| MiniCPM5 2BQ4_K_M | 2.5B | 3 GB | 11 of 11 | 69 tok/s estimated | Run the numbers |
| gpt-oss-120bMXFP4 | 116.8B | 64.6 GB | 11 of 11 | 41 tok/s measured | Run the numbers |
| Granite 4.2 8BQ4_K_M | 8B | 10.7 GB | 11 of 11 | 19 tok/s estimated | Run the numbers |
| Ling 3.0 tinyQ4_K_M | 7.9B | 6.4 GB | 11 of 11 | 74 tok/s estimated | Run the numbers |
| Mistral Small 4 (119B-2603)Q4_K_M | 119.4B | 74.5 GB | 11 of 11 | 40 tok/s estimated | Run the numbers |
| Qwen3-Coder NextQ4_K_M | 79.7B | 49.2 GB | 11 of 11 | 68 tok/s estimated | Run the numbers |
| Qwen3-Coder 30B-A3BQ4_K_M | 30.5B | 21.8 GB | 11 of 11 | 34 tok/s measured | Run the numbers |
| Devstral 2 123BQ4_K_M | 125B | 86.7 GB | 11 of 11 | 2.4 tok/s estimated | Run the numbers |
| gpt-oss-20bMXFP4 | 20.9B | 12.9 GB | 11 of 11 | 60 tok/s measured | Run the numbers |
| Gemma 4 E4BQAT Q4_0 | 8B | 5.7 GB | 11 of 11 | 51 tok/s estimated | Run the numbers |
| Devstral Small 2 24BQ4_K_M | 24B | 19.7 GB | 11 of 11 | 10 tok/s estimated | Run the numbers |
| LFM2.5 2.6BQ4_K_M | 2.7B | 2.2 GB | 11 of 11 | 93 tok/s estimated | Run the numbers |
| Ministral 3 14BQ4_K_M | 14B | 13.6 GB | 11 of 11 | 15 tok/s estimated | Run the numbers |
| Ministral 3 8BQ4_K_M | 8.9B | 9.8 GB | 11 of 11 | 21 tok/s estimated | Run the numbers |
| Ornith 1.5 35B-A3BQ4_K_M | 36B | 22.4 GB | 11 of 11 | 71 tok/s estimated | Run the numbers |
| KAT-Coder V2.5 Dev 35B-A3BQ4_K_M | 34.7B | 22.1 GB | 11 of 11 | 70 tok/s estimated | Run the numbers |
| Laguna XS 2.1Q4_K_M | 33.4B | 21.7 GB | 11 of 11 | 55 tok/s estimated | Run the numbers |
| Ornith 1.5 9BQ4_K_M | 9.7B | 6.9 GB | 11 of 11 | 30 tok/s estimated | Run the numbers |
| Spark-X2.5 4BQ4_K_M | 4.1B | 3.9 GB | 11 of 11 | 53 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. 1 of the 33 above reaches it, and the slowest is 2.4 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 192 GB adds over 128 GB
The machine here with 192 GB that hands a model the most of it is the Framework Desktop, 192GB, at 160 GB, and it holds 36 of the 39. At 128 GB the most is 119.5 GB, on the DGX Spark, 128GB, holding 33. The step adds Qwen3.8 Flash Next, DeepSeek V4-Flash and MiniMax M2.7. The strongest goes from Qwen3.8 27B to Qwen3.8 Flash Next.
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 128 GB and what each of them holds.
How much context 128 GB leaves room for
The cache grows with the window you ask for, so the same machine holds fewer models the longer the context. One column here for each amount a 128 GB machine hands over. A model is counted only where its own context ceiling reaches that far.
| Context | 96 GB to a modelFramework Desktop, 128GB | 119.5 GB to a modelDGX Spark, 128GB |
|---|---|---|
| 4k | 33 | 33 |
| 8k | 33 | 33 |
| 16k | 33 | 33 |
| 32k default | 33 | 33 |
| 64k | 32 | 33 |
| 128k | 32 | 32 |
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 128 GB machine pay for itself?
On the Strix Halo Framework Desktop, 128GB at $3,449, the cheapest machine at 128 GB with a published price, the model that pays it back soonest at 500k tokens a day is Qwen3.8 27B, in 45 years. At 20M tokens a day, agents running most of the day, it is Qwen3.5 122B-A10B in 16 months.
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.