Glyd

What runs on a 32 GB GPU, exactly

NVIDIA RTX 5090. Open models in their original precision, no quantization, with and without Glyd.

Measured on an RTX 5090
Weights, GiBbf16Glyd
Qwen3.5-9B17.6613.89
The measurements

Runs only with Glyd

bf16 does not fit; Glyd does, bit for bit

None of the models we track.

Runs either way

Glyd leaves room for a longer context or more users
Needs, GBRoom for context, K tokens
Modelbf16Glydbf16Glyd
Qwen3.5-2Best.6.34.82,2812,402
SmolLM3-3B8.46.3358386
Llama-3.2-3B-Instruct9.06.9228246
NVIDIA-Nemotron-3-Nano-4B-BF16est.9.77.11,5031,663
Qwen3-4B-Instruct-250710.98.3166184
Qwen3.5-4Best.11.28.1710803
gemma-4-E2B-itest.11.98.63,1153,584
Mistral-7B-Instruct-v0.317.212.4138174
gemma-4-E4B-itest.17.812.51,1571,521
Llama-3.1-8B-Instruct18.713.5126166
Qwen3-8B19.214.0110145
MiMo-V2.6-Distill-Qwen-9Best.20.714.5420608
Qwen3.5-9B21.215.7405571
gemma-4-12B-itest.26.418.6132252
gemma-3-12b-it26.918.8125248
phi-432.623.01663
DeepSeek-R1-Distill-Qwen-14B32.823.31563

Too big for 32 GB

Even compressed

Questions about 32 GB GPUs

Which models run on a 32 GB GPU only with Glyd?

None of the models we track: every one that fits with Glyd fits in bf16 too, and Glyd leaves the difference for a longer context.

Is it slower than bf16?

Not measured on a 32 GB GPU yet. In v0.22.0's run, one sequence at a time took 8 to 25% less GPU time a step than bf16 on every GPU tested.

Has Glyd been measured on a 32 GB GPU with a 2026 model?

Yes: Qwen3.5-9B's weights took 13.89 GiB with Glyd on an RTX 5090 against bf16's 17.66 (vLLM 0.30.0, v0.29.0).

How is this different from a 4-bit GGUF?

A 4-bit model is smaller, 4 bits a weight and their scales, but its weights are rounded and its answers change. Glyd gives you the original model.

Updated Oct 5, 2026. A model fits when its weights, an 8K-token KV cache and 1.5 GB for the runtime fit in the memory nvidia-smi reports: 32,607 MiB on a 32 GB card.