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giga-embeddings-10B-A1.8B_hybrid

GGUF port and imatrix-guided IQ4_XS/Q5_K quantization of Giga-Embeddings-instruct-10B-A1.8B, a 10B MoE embedding model for Russian, English and code, running on a single 16 GB GPU with stock llama.cpp. Pipeline, llama.cpp patches, and every measurement.

Details

External ID
1387395819
Source
GITHUB
Company
—
Product
giga-embeddings-10B-A1.8B_hybrid
Website domain
github.com
Launched
Sept. 25, 2026
Cohort
—
Upvotes
41
Upvotes percentile
0.7869587496797336
Tags
—
Fetched at
Sept. 29, 2026, 5:02 p.m.
Updated at
Sept. 29, 2026, 5:02 p.m.

Enrichment

Theme
scientific computing and deep tech tools
Vertical
Horizontal
Function
Model & infra
Audience
Developer
AI stance
AI-native
Project type
Hobby / open-source project
Normalized one-liner
quantized embedding model port for llama.cpp
Manually corrected
False

Could you build this?

No This project involves low-level C++/CUDA quantization engineering, custom GGML/llama.cpp kernel patching for Mixture-of-Experts architectures, and importance matrix (imatrix) calibration for a 10B parameter embedding model. This demands specialized knowledge of low-level machine learning systems, quantized tensor formats, and GGML internals.

What it would actually take: Requires deep C++/CUDA systems programming, fork/modification of llama.cpp/ggml tensor computation graphs, custom imatrix dataset generation in Russian and English, and benchmarking embedding cosine similarity regressions across quantization types (IQ4_XS vs Q5_K). Requires a GPU engineering specialist who understands low-level tensor quantization algorithms.

Competitors

Other products that read as similar to this one — 170 launches clear the similarity bar, closest 8 shown.

Attention rank: #34 of 171 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 325 days after the earliest competitor.

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