llamaperf

What people run, by hardware

Pick the kind of machine you have. You'll see the model sizes people report running on it, most reported first, with the speed they typically get. It tells you what works on hardware like yours. It doesn't tell you which model gives the best answers.

To check a specific model on your exact card, use the calculator. For coding, see the coding models by VRAM.

Maker
Mac

A Mac counts when its report names the product, or when only that product was sold with its chip and memory.

Apple Silicon
Model size

Apple Silicon 48-96 GB· MacBook Pro only

70B-class models fit at 4 bits. Memory speed 273 to 614 GB/s across the 10 devices with reports, so a typical figure here spans all of them. 26 single-device reports.

#ModelReports
1Qwen3.827B
Alibaba · mostly 4-bit
No generation data · with speculation 60 t/s (7)
8
2Qwen3.8 Flash-Next125B · 6B active
Alibaba · mostly Q4_0
No generation data
4
3Qwen3.627B
Alibaba · mostly 4-bit
Gen 20 t/s (2 runs) · PP 121 t/s (1 run) · with speculation 63 t/s (1)
3
4DeepSeek V4 Flash284B · 13B active
DeepSeek · mostly Q2-Q4 mixed imatrix
Gen 13 t/s (1 run)
2
5Kimi K32800B · 104B active
Moonshot AI · mostly VQ3R
No generation data
2
6Qwen3.635B · 3B active
Alibaba · mostly 4-bit group-64
No generation data · with speculation 108 t/s (2)
2
7Qwen3.8 Uncensored27B
Alibaba · mostly 4-bit
No generation data · with speculation 59 t/s (2)
2
8Gemma 426B · 4B active
Google DeepMind
Gen 97 t/s (1 run)
1
9DeepSeek V4 Flash 9B distill9B
DeepSeek · mostly Q4_K_M
Gen 44 t/s (1 run)
1
10Qwen3.527B
Alibaba · mostly affine 4bit
No generation data · PP 461 t/s (1 run)
1

How this list works

Each row is one model size, because a 27B and a 397B from the same family are different choices. Rows are ordered by how many people report running that size on this hardware.

Generation speed is the median of runs on one device, serving one request, without speculative decoding. PP is the median prompt-processing speed from plain runs that reported it, so its run count can be smaller. Speculative decoding (MTP, a draft model, DFlash, n-gram lookup) can make the same model several times faster, so those runs get their own column. A report that doesn't make clear which kind of run it was counts in neither.

Hardware is grouped by how much it holds and how fast its memory is, since memory speed sets how fast a model writes. Click a model for every report behind its row.