---
title: MemTensor/MemT-1.0-Flash
url: https://recentmodel.com/models/MemTensor/MemT-1.0-Flash/
status: archived
published: 2026-10-17T15:45:17Z
updated: 2026-10-22T23:27:46Z
source: https://huggingface.co/MemTensor/MemT-1.0-Flash
---

# MemTensor/MemT-1.0-Flash

MemT-1.0 is a new generation of memory foundation models that makes persistent, dynamically evolving memory an intrinsic part of model computation. Mixture-of-experts, 288B total (256 experts, 6 per token). Context: 1M tokens (config). License: mit. Vendor-reported: "Across 11 memory benchmarks, MemT-1.0-Flash and MemT-1.0-Nano rank first and second among the evaluated parametric-memory methods, respectively."

- Vendor-reported: Across 11 memory benchmarks, MemT-1.0-Flash and MemT-1.0-Nano rank first and second among the evaluated parametric-memory methods, respectively.

- **Org:** MemTensor
- **Released:** 2026-10-17
- **Status:** archived
- **License:** mit (permissive)
- **Parameters:** 287.9B
- **Architecture:** MoE · 256 experts, 6/token · MemTDeepseekV4ForCausalLM
- **Context:** 1,048,576 tokens
- **Task:** text-generation · text
- **Signal:** 70 HF likes · 682 downloads
- **Archived:** 2026-10-22 (no longer listed by the collector; stats as last seen)

## Runs on

estimate from params; sizing calculation, not tested.

- Q4 ~174.5 GB (estimated): 2× DGX Spark (256GB), Mac 256GB, CPU only, 256GB RAM (slow)
- Q8 ~307.6 GB (estimated): Mac 512GB
- FP16 ~577.6 GB (estimated): no listed device

## Links

- [Hugging Face](https://huggingface.co/MemTensor/MemT-1.0-Flash)