System · desktop

RTX 5090 workstation (96 GB DDR5)

Runs models up to ~47B parameters at 4-bit entirely on the GPU. Single 32 GB GPU desktop.

GPU memory: 32 GB
System RAM: 96 GB
Runs: 16 of 19model variants, 8K
Estimated cost: ~$4,000

What it runs

Largest models that fit without spilling into system memory, at an 8K contextHow much text the model can consider at once, counted in tokens — roughly ¾ of a word each..

Everything →

Variant
Mixtral 8x7B Instruct v0.1
46.7B · Q4_K_M via llama.cpp
Tight fit
~138 tok/s est.
Variant
Qwen2.5 32B Instruct
32.8B · Q5_K_M via llama.cpp
Runs well
~49 tok/s est.
Variant
Qwen2.5-Coder 32B Instruct
32.8B · Q4_K_M via llama.cpp
Runs well
~57 tok/s est.
Variant
DeepSeek-R1-Distill-Qwen-32B
32.8B · Q4_K_M via llama.cpp
Runs well
~57 tok/s est.
Variant
Qwen3 30B-A3B
30.5B · Q4_K_M via llama.cpp
Runs well
~458 tok/s est.
Variant
Gemma 3 27B IT
27.4B · Q4_K_M via llama.cpp
Runs well
~67 tok/s est.
Variant
Gemma 2 27B IT
27.2B · Q4_K_M via llama.cpp
Runs well
~68 tok/s est.
Variant
Mistral Small 24B Instruct 2501
23.6B · Q6_K via llama.cpp
Runs well
~59 tok/s est.
Variant
Qwen2.5 14B Instruct
14.8B · Q6_K via llama.cpp
Runs well
~92 tok/s est.
Variant
Phi-4
14.7B · Q8_0 via llama.cpp
Runs well
~72 tok/s est.

Components

1× Hardware NVIDIA GeForce RTX 5090 gpu · 32 GB
1× Hardware AMD Ryzen 9 7950X cpu
System RAM bandwidth 96 GB/s

Measured performance

Model · quantization	Runtime	Context	Measurements	Source
Qwen2.5 32B Instruct Q4_K_M	llama.cppcuda · b4600	4K	2,900 tok/s · Prompt processing (512) 61 tok/s · Generation (128)	Mutinai illustrative fixtures

Community results

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