Nex-N2.5 Max — VRAM, Speed & Local Setup

Written by Jakub Rusinowski · Last updated September 8, 2026

A 1.6-trillion-parameter text-only MoE on a DeepSeek-V4-Pro base — 967 GB at Q4_K_M, which Nex serves on 16x H200 across two nodes. The best BrowseComp score in Nex's table at 92.6, plus 86.1 on Terminal-Bench 2.1 and 65.7 on SWE-Bench Pro. Listed here as a reference point for what frontier open weights now cost to hold, not as a local recommendation. DeepSeek lineage means MLA compressed KV cache, so its context is far cheaper than the parameter count suggests.

Nex-N2.5 Max needs about 967 GB of VRAM at Q4_K_M — quantized weights plus framework overhead, before any KV cache. On Apple Silicon that figure comes out of unified memory.

Specifications

Parameters	1.6 Trillion (49B active)
Context window	262,144
Architecture	Mixture-of-Experts (DeepSeek-V4-Pro base, MLA attention, text-only)
Provider	Nex-AGI
Licence	Open-weight (terms unpublished)
Specified at	Q4_K_M
System RAM	1024 GB
Record updated	2026-09-08

Licence

Custom Open-Weight — commercial use permitted. Weights are downloadable and commercial use is permitted, subject to the licence’s acceptable-use terms.

VRAM and Speed by Quantization

Modelled on a reference NVIDIA RTX 4090 (24 GB), with no KV cache (this record has no published architecture). Speed figures are ESTIMATES from the memory-bandwidth roofline described on the methodology page, not benchmarks we ran — rows marked measured come from published or reader-submitted runs. VRAM here includes the KV cache, so it reads higher than the headline figure above, which does not.

Quant	Weights	VRAM needed	Est. speed	Fit on 24 GB
Q2_K	526.0 GB	526.8 GB	—	Won't fit
Q3_K_M	682.0 GB	682.8 GB	—	Won't fit
Q4_K_M	966.0 GB	966.8 GB	—	Won't fit
Q5_K_M	1134.0 GB	1134.8 GB	—	Won't fit
Q6_K	1312.0 GB	1312.8 GB	—	Won't fit
Q8_0	1700.0 GB	1700.8 GB	—	Won't fit
F16	3200.0 GB	3200.8 GB	—	Won't fit

Want the memory numbers alone, at every quantization level and your own context length? Use the Nex-N2.5 Max VRAM calculator.

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How to Run Nex-N2.5 Max

Install Ollama, then run:

ollama run nex-n2-5

Weights on Hugging Face: nex-agi/Nex-N2.5-Max.

Best for: frontier tasks, agentic tasks, research, enterprise.

Can I Run Nex-N2.5 Max on My GPU?

Other Nex-N2.5 Sizes

Nex-N2.5 mini — about 22 GB VRAM at Q4_K_M
Nex-N2.5 Pro — about 240 GB VRAM at Q4_K_M

Nex-N2.5 Max — Frequently Asked Questions

How much VRAM does Nex-N2.5 Max need?

About 967 GB at Q4_K_M — quantized weights plus framework overhead, before any KV cache. The cache grows with context length and is added on top; the table above folds it in. Apple Silicon counts unified memory toward the same figure.

Does Nex-N2.5 Max run on an RTX 4090 (24 GB)?

No. Nex-N2.5 Max needs about 967 GB at Q4_K_M, more than a single RTX 4090's 24 GB. It needs a larger card, several GPUs, or Apple Silicon with enough unified memory — or it runs with part of the weights offloaded to system RAM, which is much slower.

How do I run Nex-N2.5 Max locally?

Install Ollama and run `ollama run nex-n2-5`. That pulls the weights and starts a local OpenAI-compatible endpoint; after the download nothing leaves the machine.

What other sizes does Nex-N2.5 come in?

Nex-N2.5 mini (22 GB), Nex-N2.5 Pro (240 GB), Nex-N2.5 Max (967 GB). Every size shares the family's training and licence; the larger ones score higher and need proportionally more memory.

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