Sunk Cost sunkcost.ai Data checked 2026-09-03

What can you run with 36 GB of memory?

A model does not get the 36 GB. It gets 27 GB on both machines here sold with 36 GB, because the system keeps the rest. That holds 27 of the 39 current models at 32k of context, and the strongest of them is Qwen3.8 27B.

What a model gets27 GB of the 36 GB. Each machine's page says where the rest goes.
Models that fit at 32k27 of the 39 current ones, at the quantisation this site lists each at.
The strongest of themQwen3.8 27B, sonnet-class.
Cheapest machine at this sizeMac Studio M5 Max, 36GB at $2,499.

Run the numbers on Qwen3.8 27B at 36 GB

Jump to: The machines sold with 36 GB · Models that fit in 36 GB · What 48 GB adds over 36 GB · How much context 36 GB leaves room for · Does a 36 GB machine pay for itself?

The machines sold with 36 GB

There are two machines here with 36 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.

MachineWhat a model getsPriceModels it holds at 32kStrongest of them
Mac Studio M4 Max, 36GBprevious 27 GB $1,999 27 Qwen3.8 27B Run the numbers
Mac Studio M5 Max, 36GB 27 GB $2,499 27 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 36 GB

Every current model a 36 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 Max, 36GB, the machine this list is cut from; speed follows memory bandwidth rather than memory size, so another machine at 36 GB runs the same model at its own rate, and its page gives every one of them.

ModelParametersNeeds at 32kMachines at 36 GBSpeed on the Mac Studio M5 Max, 36GB
Qwen3.8 27BQ4_K_M 27.8B 18.6 GB 2 of 2 19 tok/s estimated Run the numbers
Qwen3.6 27BQ4_K_M 27.8B 19 GB 2 of 2 18 tok/s estimated Run the numbers
Qwen3.6 35B-A3BQ4_K_M 36B 22.8 GB 2 of 2 55 tok/s estimated Run the numbers
Muse Glimmer 30BQ4_K_M 29.8B 17.8 GB 2 of 2 19 tok/s estimated Run the numbers
Gemma 4 26B-A4BQ4_K_M 25.2B 17.8 GB 2 of 2 40 tok/s estimated Run the numbers
Granite 4.2 30BQ4_K_M 29.3B 26.3 GB 2 of 2 13 tok/s estimated Run the numbers
Gemma 4 31B itQ4_K_M 31.3B 25.8 GB 2 of 2 13 tok/s estimated Run the numbers
GLM-4.7-FlashQ4_K_M 31.2B 20.1 GB 2 of 2 39 tok/s estimated Run the numbers
Nemotron 3.5 Lightning 30B-A3BQ4_K_M 31.6B 25.7 GB 2 of 2 53 tok/s estimated Run the numbers
Gemma 4 12BQ4_K_M 12B 8 GB 2 of 2 43 tok/s estimated Run the numbers
Qwen3.5 9BQ4_K_M 9.7B 6.8 GB 2 of 2 51 tok/s estimated Run the numbers
Qwen3.5 4BQ4_K_M 4.7B 3.8 GB 2 of 2 90 tok/s estimated Run the numbers
MiniCPM5 2BQ4_K_M 2.5B 3 GB 2 of 2 116 tok/s estimated Run the numbers
Granite 4.2 8BQ4_K_M 8B 10.7 GB 2 of 2 32 tok/s estimated Run the numbers
Ling 3.0 tinyQ4_K_M 7.9B 6.4 GB 2 of 2 57 tok/s estimated Run the numbers
Qwen3-Coder 30B-A3BQ4_K_M 30.5B 21.8 GB 2 of 2 26 tok/s estimated Run the numbers
gpt-oss-20bMXFP4 20.9B 12.9 GB 2 of 2 48 tok/s estimated Run the numbers
Gemma 4 E4BQAT Q4_0 8B 5.7 GB 2 of 2 40 tok/s estimated Run the numbers
Devstral Small 2 24BQ4_K_M 24B 19.7 GB 2 of 2 18 tok/s estimated Run the numbers
LFM2.5 2.6BQ4_K_M 2.7B 2.2 GB 2 of 2 156 tok/s estimated Run the numbers
Ministral 3 14BQ4_K_M 14B 13.6 GB 2 of 2 25 tok/s estimated Run the numbers
Ministral 3 8BQ4_K_M 8.9B 9.8 GB 2 of 2 35 tok/s estimated Run the numbers
Ornith 1.5 35B-A3BQ4_K_M 36B 22.4 GB 2 of 2 56 tok/s estimated Run the numbers
KAT-Coder V2.5 Dev 35B-A3BQ4_K_M 34.7B 22.1 GB 2 of 2 55 tok/s estimated Run the numbers
Laguna XS 2.1Q4_K_M 33.4B 21.7 GB 2 of 2 43 tok/s estimated Run the numbers
Ornith 1.5 9BQ4_K_M 9.7B 6.9 GB 2 of 2 50 tok/s estimated Run the numbers
Spark-X2.5 4BQ4_K_M 4.1B 3.9 GB 2 of 2 89 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. 4 of the 27 above reach it, and the slowest is 13 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 48 GB adds over 36 GB

The machine here with 48 GB that hands a model the most of it is the Mac mini M5 Pro, 48GB, at 36 GB, and it holds 27 of the 39. At 36 GB the most is 27 GB, on the Mac Studio M5 Max, 36GB, holding 27. Nothing new fits. What the step buys is a longer window and room to work, not a model that was out of reach. The strongest either way is Qwen3.8 27B.

The cheapest machine at 48 GB is the Mac mini M5 Pro, 48GB at $2,299. The cheapest at 36 GB is the Mac Studio M5 Max, 36GB at $2,499, 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 36 GB and what each of them holds.

How much context 36 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 27 GB, so one column covers all of them. A model is counted only where its own context ceiling reaches that far.

Context27 GB to a modelMac Studio M5 Max, 36GB
4k 27
8k 27
16k 27
32k default 27
64k 25
128k 22

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 36 GB machine pay for itself?

On the Mac Studio M5 Max, 36GB at $2,499, the cheapest machine at 36 GB with a published price, the model that pays it back soonest at 500k tokens a day is Qwen3.8 27B, in 32 years. At 20M tokens a day, agents running most of the day, it is Qwen3.8 27B in 9.5 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.

Put your own usage in

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.