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Model Memory Fit · LLM

Qwen2.5 7B Instruct: memory fit on every Jetson module.

Method v1.0 · dataset 2026-09-07 · verified 2026-09-07 · last updated September 2026

7.61 B parameters · 28 layers · 32,768-token max context. Hugging Face: Qwen/Qwen2.5-7B-Instruct.

Model facts

FactValueNote
Parameters7.61 B
Layers28
Hidden size3584
Attention heads28
KV heads4
Head dimension128
Max context32,768 tokens
dtypebfloat16
Vocabulary152,064

Qwen (Alibaba) · hf:Qwen/Qwen2.5-7B-Instruct · config.json · verified 2026-09-07

“"hidden_size": 3584, "num_attention_heads": 28, "num_key_value_heads": 4”

Parameters source

Published quantised artefacts

A file the publisher or a community mirror actually ships, used as the class-A weight figure when the requested quant matches. Any quant without a row here falls back to parameters × bytes-per-parameter (class D for block quants).

QuantFormatFileSizeSource
GGUF Q4_K_M / INT4 (AWQ, MLC q4f16)GGUF Q4_K_MQwen2.5-7B-Instruct-Q4_K_M.gguf4.683 GBbartowski (community GGUF) · hf:bartowski/Qwen2.5-7B-Instruct-GGUF · verified 2026-09-07

Jetson tokens/s measurements

Published or archived throughput numbers, not modelled. Class C (external measured benchmark).

ModuleRuntimeQuantMeasuredSource
Jetson Orin Nano Superunspecified (archived benchmark)21.75 tok/sNVIDIA Jetson AI Lab (archive) · jetson-ai-lab:benchmarks.html · verified 2026-09-07

Every module × every quantisation

Verdict and total memory at 4096-token context, 1 sequence, llama.cpp, headless. Q4 cells link to the static breakdown page; every other cell links to the live engine at that quantisation.

Notes

head_dim derived as hidden_size/num_attention_heads=3584/28=128.

Method and data: Model Memory Fit methodology. Full registry: model-memory.json.

Change the context, concurrency or runtime.

The live engine covers any context length, sequence count, KV precision and runtime, with a permanent link.

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