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The opportunity tracker for new startups.

mirai-s-ada

Mirai S (Qwen3.8-27B-S trellis GGUF) on a 12 GB RTX 4070: 262k q8_0 tiered cache, MTP drafting at every depth, harness-proofing, the layer; receipts.

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This is 1 of 76 launches in local AI inference and ComfyUI tooling — see how it stacks up on momentum and crowding →

41 other launches read as similar to this one →

Details

External ID
1407302011
Source
GITHUB
Company
—
Product
mirai-s-ada
Website domain
github.com
Launched
Oct. 6, 2026
Cohort
—
Upvotes
51
Upvotes percentile
0.7526291723822588
Tags
—
Fetched at
Oct. 8, 2026, 5:02 p.m.
Updated at
Oct. 8, 2026, 5:02 p.m.

Enrichment

Niche
local AI inference and ComfyUI tooling
Vertical
Horizontal
Function
Model & infra
Audience
Developer
AI stance
AI-native
Project type
Hobby / open-source project
Normalized one-liner
optimized gguf model runner on consumer gpus
Manually corrected
False

Could you build this?

No Running and optimizing a 27B parameter LLM on a 12 GB consumer GPU with custom tiered caching and multi-token prediction drafting requires deep low-level CUDA, memory paging, and llama.cpp systems engineering.

What it would actually take: A production implementation requires writing custom C++/CUDA memory allocators to tier KV caches between system RAM and VRAM, alongside specialized GGUF quantization and speculative decoding pipelines. Developers need deep knowledge of GPU memory bandwidth, tensor parallelism, and inference engine internals (e.g., llama.cpp or vLLM forks).

Competitors

Other products that read as similar to this one — 41 launches clear the similarity bar, closest 8 shown.

Attention rank: #8 of 42 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 303 days after the earliest competitor.

Other launches for this product

Same idea, different domain

Nobody's really built a model & infra tool for Fintech yet.