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Project Maya

GLM-5.3-Flash (321 B), DeepSeek-V4.1-Flash-1017 (748 B) and MiniMax-M3.1-Flash (428 B) on your own GPU
private and fast · one NVIDIA GPU or up to 16 · AMD and Windows · chat, pictures, OpenAI- and Anthropic-compatible API · several requests at once

Created and led by Peasant Smith (@mw00), co-maintained with @needmorevram

_{Project Maya is free. If it runs well on your PC, sponsoring it pays for the test hardware and the work on it.}

Models this size usually need a datacenter. Maya runs three of them on the hardware you already have: GLM-5.3-Flash, DeepSeek-V4.1-Flash-1017 and MiniMax-M3.1-Flash. Each model has an engine built for it. It keeps the experts your conversation uses most on the GPU, the next ones in RAM and the rest on your NVMe SSD, and moves them as you chat. It measures your GPUs, CPU, RAM and SSD and tunes itself to them. Nothing leaves your machine.

• Fast: up to 149 tokens/s on two RTX 5090s and an RTX 4090, 124 on two H100s, 118 on four RTX 4090s, 81 on one RTX PRO 6000, 30+ on a single RTX 5090. Real numbers from real users.
• Close to the original: Maya's own quants of GLM-5.3-Flash keep 97.7-99.2% of the FP8 model's zero-shot accuracy; DeepSeek V4.1 · Maya-S picks the same next token as the original 92.6% of the time, MiniMax M3.1 · Maya-S 87.6% (the average over its categories).
• Plugs into your tools: OpenAI- and Anthropic-compatible API with tool calls, ready for coding agents.
• Several requests at once: up to 8 answered together with GLM-5.3-Flash, DeepSeek-V4.1-Flash and MiniMax-M3.1-Flash - for agents that run several sessions, or several people on one PC.
• Everything in the box: a chat and a live monitor in the browser, pictures on every model, thinking levels, a Models page that downloads, loads and deletes models, a Turbo button, a one-command setup.

Quick start

git clone https://github.com/mw00/project-maya.git && cd project-maya
./setup.sh

The setup asks which model you want, downloads it, builds everything once and starts the model. Then open http://127.0.0.1:8080. Windows: START-MAYA.bat instead of ./setup.sh. More in Install.

All documentation: docs/README.md

Pick your model

Every Maya quant is named after its model, then its tier - XS, S, M or L, by how many bits its experts keep. The setup recommends one for your PC; to pick one yourself, give its setup id: ./setup.sh --model glm-maya-s24.

Model	Maya quant · setup id	Size	Made for
GLM-5.3-Flash zai-org · MIT · 321 B, 18 B active · up to 1M context	Maya-S (default) · glm-maya-s	96.5 GB	PCs with a smaller memory pool (RAM and VRAM together)
	Maya-S24 · glm-maya-s24	94.7 GB	cards of 24 GB or less: ~1.5 GB more of the card for experts, decode +14%
	Maya-XS · glm-maya-xs	73.4 GB	PCs with 64 GB of RAM and a 24 GB card, at 128K context
	Maya-M · glm-maya-m	116 GB	a bigger memory pool; calibrated toward tool calls and front-end code
	Maya-M-Derisked · glm-maya-m-derisked	116 GB	Maya-M with a weight edit by Blackfrost_AI that reduces blanket refusals; experimental
	Maya-L · glm-maya-l	156.3 GB	the biggest memory pools; the closest to the original
GLM-5.3 zai-org · its own license · 744 B, 40 B active · up to 1M context · text only	UD-IQ2_M (unsloth) · GLM-5.3-UD-IQ2_M	238.6 GB	the biggest memory pools; NVIDIA from the V100 on and AMD (details)
DeepSeek-V4.1-Flash-1017 DeepSeek · MIT · 748 B	Maya-S (default) · deepseek-maya-s	340 GB	any amount of memory: what VRAM and RAM don't hold is read from the SSD
	Maya-XS (v2) · deepseek-maya-xs	277 GB	the whole model in a 128 GB PC's memory (the Engram tables stay on the SSD); v2 is about 12% closer to the original and about 20% faster at writing than v1 - running the setup again offers the update
MiniMax-M3.1-Flash MiniMax · MiniMax Community License · 428 B, 23 B active · up to 1M context	Maya-XS · minimax-maya-xs	93 GB	the whole model in a 128 GB PC's memory
	Maya-S · minimax-maya-s	122 GB	closer to the original

Which model fits your PC? See docs/MEMORY.md for each model's recommended memory (VRAM + RAM together).

All of them run on NVIDIA from the V100 / RTX 20 on, and on AMD and Windows (see What you need). DeepSeek's sizes include its Engram tables. The setup also offers unsloth's GGUF and LibertAIDAI's EXL3 quants of DeepSeek, orcarouter's abliterated DeepSeek, Mia-AiLab's EXL3 quant of MiniMax, turboderp's EXL3 quants of GLM, Qwen-3.8-Flash-Next (CYBER-FROST) as its Q5_K_M GGUF and Qwen-3.8-Flash-Next itself as unsloth's UD-IQ2_XS GGUF, both with pictures (its settings). Each model's files, drafters, pictures and options: docs/MODELS.md.

Quality

Maya's quants are made from each model's released weights and measured against the original:

• GLM-5.3 · Maya quants keep 97.7-99.2% of the FP8 model's zero-shot accuracy and pick its next token 83-90% of the time (Maya-L: 99.2%, 90%). How they're made and measured: MAYA-S, MAYA-S24, MAYA-M, MAYA-L.
• DeepSeek V4.1 · Maya-S picks the same next token as the original 92.6% of the time.
• MiniMax M3.1 · Maya-S picks it 87.6% of the time (the average over its categories).

More on each model's card on Hugging Face.

How fast is it?

Real machines measured by our users, most with ./maya.sh --bench (decode: three greedy 256-token answers;