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project-maya

Project Maya - GLM-5.3-Flash (321B MoE) on your own NVIDIA GPU(s): engine, server, dashboard, quant tools. Built on Strata.

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This is 1 of 144 launches in llm inference and deployment tools — see how it stacks up on momentum and crowding →

36 other launches read as similar to this one →

Details

External ID
1407997858
Source
GITHUB
Company
—
Product
project-maya
Website domain
github.com
Launched
Oct. 6, 2026
Cohort
—
Upvotes
281
Upvotes percentile
0.95
Tags
—

Enrichment

Niche
llm inference and deployment tools
Vertical
Horizontal
Function
Model & infra
Audience
Developer
AI stance
AI-native
Project type
Hobby / open-source project
Normalized one-liner
local inference engine and dashboard for glm-5.3-flash on nvidia gpus
Manually corrected
False

Could you build this?

No Serving a massive 321B parameter MoE model on consumer/local NVIDIA hardware requires custom CUDA kernels, advanced quantization schemes, distributed inference memory management, and deep systems engineering.

What it would actually take: A real implementation requires writing low-level C++/CUDA inference primitives, tensor/pipeline parallelism, custom matrix multiplication kernels (e.g., AWQ, GPTQ, or FP8/FP4 formats), and high-throughput memory paging (like vLLM or llama.cpp). It requires high-performance computing (HPC) engineers and specialized machine learning systems expertise.

Competitors

Other products that read as similar to this one — 36 launches clear the similarity bar, closest 8 shown.

Attention rank: #5 of 37 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 322 days after the earliest competitor.

Other launches for this product