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

What can you run with 48 GB of memory?

A model does not get the 48 GB. It gets 36 GB on all six machines here sold with 48 GB, because the system keeps the rest. That holds 35 of the 51 current models at 32k of context, and the strongest of them is Qwen3.8 27B.

What a model gets36 GB of the 48 GB. Each machine's page says where the rest goes.
Models that fit at 32k35 of the 51 current ones, at the quantisation this site lists each at.
The strongest of themQwen3.8 27B, sonnet-class.
Cheapest machine at this sizeMac mini M5 Pro, 48GB at $2,299.

Run the numbers on Qwen3.8 27B at 48 GB

Jump to: The machines sold with 48 GB · Models that fit in 48 GB · What 64 GB adds over 48 GB · How much context 48 GB leaves room for · Does a 48 GB machine pay for itself?

The machines sold with 48 GB

There are six machines here with 48 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 mini M4 Pro, 48GBprevious 36 GB $1,799 35 Qwen3.8 27B Run the numbers
Mac mini M5 Pro, 48GB 36 GB $2,299 35 Qwen3.8 27B Run the numbers
Mac Studio M4 Max, 48GBprevious 36 GB $2,499 35 Qwen3.8 27B Run the numbers
Mac Studio M5 Max, 48GB 36 GB $3,099 35 Qwen3.8 27B Run the numbers
MacBook Pro M5 Pro (16-inch), 48GB 36 GB $3,599 35 Qwen3.8 27B Run the numbers
MacBook Pro M5 Max (16-inch), 48GB 36 GB $4,999 35 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 48 GB

Every current model a 48 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 mini M5 Pro, 48GB, the machine this list is cut from; speed follows memory bandwidth rather than memory size, so another machine at 48 GB runs the same model at its own rate, and its page gives every one of them.

ModelParametersNeeds at 32kMachines at 48 GBSpeed on the Mac mini M5 Pro, 48GB
Qwen3.8 27BQ4_K_M 27.8B 18.6 GB 6 of 6 12 tok/s estimated Run the numbers
Qwen3.6 27BQ4_K_M 27.8B 19 GB 6 of 6 12 tok/s estimated Run the numbers
Nemotron 3.5 Bolt 74B-A9BIQ2 74.9B 22.3 GB 6 of 6 32 tok/s estimated Run the numbers
Qwen3.6 35B-A3BQ4_K_M 36B 22.8 GB 6 of 6 37 tok/s estimated Run the numbers
Muse Glimmer 30BQ4_K_M 29.8B 17.8 GB 6 of 6 13 tok/s estimated Run the numbers
Gemma 4 26B-A4BQ4_K_M 25.2B 17.8 GB 6 of 6 27 tok/s estimated Run the numbers
Granite 4.2 30BQ4_K_M 29.3B 26.3 GB 6 of 6 8.8 tok/s estimated Run the numbers
Gemma 4 31B itQ4_K_M 31.3B 23.1 GB 6 of 6 10 tok/s estimated Run the numbers
GLM-4.7-FlashQ4_K_M 31.2B 20.1 GB 6 of 6 26 tok/s estimated Run the numbers
Nemotron 3.5 Lightning 30B-A3BQ4_K_M 31.6B 25.7 GB 6 of 6 35 tok/s estimated Run the numbers
Gemma 4 12BQ4_K_M 12B 8 GB 6 of 6 29 tok/s estimated Run the numbers
Qwen3.5 9BQ4_K_M 9.7B 6.8 GB 6 of 6 34 tok/s estimated Run the numbers
Qwen3.5 4BQ4_K_M 4.7B 3.8 GB 6 of 6 60 tok/s estimated Run the numbers
MiniCPM5 2BQ4_K_M 2.5B 3 GB 6 of 6 78 tok/s estimated Run the numbers
Granite 4.2 8BQ4_K_M 8B 10.7 GB 6 of 6 21 tok/s estimated Run the numbers
Ling 3.0 tinyQ4_K_M 7.9B 5 GB 6 of 6 90 tok/s estimated Run the numbers
Qwen3-Coder 30B-A3BQ4_K_M 30.5B 21.8 GB 6 of 6 18 tok/s estimated Run the numbers
gpt-oss-20bMXFP4 20.9B 12.9 GB 6 of 6 32 tok/s estimated Run the numbers
Gemma 4 E4BQAT Q4_0 8B 5.7 GB 6 of 6 27 tok/s estimated Run the numbers
Devstral Small 2 24BQ4_K_M 24B 19.7 GB 6 of 6 12 tok/s estimated Run the numbers
LFM2.5 2.6BQ4_K_M 2.7B 2.2 GB 6 of 6 104 tok/s estimated Run the numbers
Ministral 3 14BQ4_K_M 14B 13.6 GB 6 of 6 17 tok/s estimated Run the numbers
Ministral 3 8BQ4_K_M 8.9B 9.8 GB 6 of 6 24 tok/s estimated Run the numbers
Nanbeige4.3 33BQ4_K_M 33.1B 27.8 GB 6 of 6 8.3 tok/s estimated Run the numbers
Flint 40B-A2.5BUD-Q4_K_XL 39.8B 26 GB 6 of 6 36 tok/s estimated Run the numbers
Ornith 1.5 35B-A3BQ4_K_M 36B 22.4 GB 6 of 6 37 tok/s estimated Run the numbers
KAT-Coder V2.5 Dev 35B-A3BQ4_K_M 34.7B 22.1 GB 6 of 6 37 tok/s estimated Run the numbers
Laguna XS 2.1Q4_K_M 33.4B 21.7 GB 6 of 6 29 tok/s estimated Run the numbers
Hy-Memory 30B-A3BQ4_K_M 30.1B 21.5 GB 6 of 6 18 tok/s estimated Run the numbers
LFM2.5 24B-A2BQ4_K_M 24B 15.2 GB 6 of 6 43 tok/s estimated Run the numbers
Ornith 1.5 9BQ4_K_M 9.7B 6.9 GB 6 of 6 34 tok/s estimated Run the numbers
Spark-X2.5 4BQ4_K_M 4.1B 3.9 GB 6 of 6 60 tok/s estimated Run the numbers
Spark-X2.6 4BQ4_K_M 4.1B 3.9 GB 6 of 6 60 tok/s estimated Run the numbers
Spark-X2.6 1.7BQ4_K_M 1.7B 1.6 GB 6 of 6 146 tok/s estimated Run the numbers
LFM3 1.7BQ4_K_M 1.7B 1.5 GB 6 of 6 153 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 35 above reach it, and the slowest is 8.3 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.

Qwen3 32B is one of two models this site lists at two precisions, and 48 GB holds only the lighter build. At Q4_K_M the weights plus the cache for 32k come to 28.3 GB, inside the 36 GB every machine here with 48 GB hands a model. At Q8_0 the same model needs 43.4 GB, so nothing here with 48 GB runs it. The smallest size here that does is 64 GB.

What 64 GB adds over 48 GB

The machine here with 64 GB that hands a model the most of it is the Corsair AI Workstation 300, 64GB, at 48 GB, and it holds 35 of the 51. At 48 GB the most is 36 GB, on the Mac mini M5 Pro, 48GB, holding 35. 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 64 GB is the Strix Halo Corsair AI Workstation 300, 64GB at $1,700. The cheapest at 48 GB is the Mac mini M5 Pro, 48GB at $2,299, so the larger size is the cheaper of the two here.

More memory is not more speed. The Corsair AI Workstation 300, 64GB reads memory at 256 GB/s and the Mac mini M5 Pro, 48GB at 307 GB/s, so Qwen3.8 27B runs at 10 tok/s on the 64 GB machine and 12 tok/s on the 48 GB one, both worked out from memory bandwidth.

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 for room, which is not the same as the best case for speed. The table above has every machine at 48 GB and what each of them holds.

How much context 48 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 36 GB, so one column covers all of them. A model is counted only where its own context ceiling reaches that far.

Context36 GB to a modelMac mini M5 Pro, 48GB
4k 35
8k 35
16k 35
32k default 35
64k 35
128k 33

Counted over the 51 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 48 GB machine pay for itself?

On the Mac mini M5 Pro, 48GB at $2,299, the cheapest machine at 48 GB with a published price, the model that pays it back soonest at 500k tokens a day is LFM2.5 2.6B, in 35 years. At 20M tokens a day, agents running most of the day, it is LFM2.5 2.6B in 11 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. The sizes either side of this one are 36 GB and 64 GB. The memory guide has the ladder in full.