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janas

Janas-LLM: large mixture-of-experts language models on ordinary computers, in C. Experts streamed from NVMe, with or without a GPU.

Details

External ID
1378187980
Source
GITHUB
Company
—
Product
janas
Website domain
github.com
Launched
Sept. 20, 2026
Cohort
—
Upvotes
21
Upvotes percentile
0.6211247758134768
Tags
avx2, c, cpu-inference, gguf, inference-engine, linux, llm, local-llm, mixture-of-experts, moe, nvme, quantization, qwen3, simd, speculative-decoding, vulkan
Fetched at
Sept. 24, 2026, 5:02 p.m.
Updated at
Sept. 24, 2026, 5:02 p.m.

Enrichment

Theme
voice AI agents and infrastructure
Vertical
Horizontal
Function
Model & infra
Audience
Developer
AI stance
AI-native
Project type
Hobby / open-source project
Normalized one-liner
c-based mixture-of-experts inference engine streaming from nvme
Manually corrected
False

Could you build this?

No Streaming MoE model weights dynamically from NVMe drives in pure C requires advanced systems programming, custom memory-mapped I/O (io_uring/direct I/O), and low-level matrix math kernels.

What it would actually take: The architecture requires a custom inference engine in C utilizing asynchronous Linux I/O (`io_uring`) or DirectStorage to page gigabytes of expert weights off NVMe in real time matching token generation latencies. The hard engineering problems are cache replacement algorithms, latency hiding via prefetching predicted expert activation paths, and hand-optimized SIMD/GEMM routines. This demands high-performance computing (HPC) and deep systems programming expertise.

Competitors

Other products that read as similar to this one — 1097 launches clear the similarity bar, closest 8 shown.

Attention rank: #427 of 1098 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 325 days after the earliest competitor.

Other launches for this product

Same idea, different domain

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