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collabosm

A local app that rents a GPU in your own Colab account and serves Qwen3.8 on it: 27B on an A100-40G, or Flash-Next (125B-A6B MoE) on an A100-80G. The cost is shown before it starts; chat with images; Codex or any OpenAI client uses a fixed local /v1. Auto-stop, CU ledger, live prefill/decode. Windows, macOS, Linux.

Details

External ID
1386533084
Source
GITHUB
Company
—
Product
collabosm
Website domain
github.com
Launched
Sept. 25, 2026
Cohort
—
Upvotes
90
Upvotes percentile
0.9133358954650269
Tags
colab, exllamav3, google-colab, gpu, llm, llm-inference, openai-api, qwen
Fetched at
Sept. 29, 2026, 5:02 p.m.
Updated at
Sept. 29, 2026, 5:02 p.m.

Enrichment

Theme
lightweight and on-device AI runtimes
Vertical
Horizontal
Function
Model & infra
Audience
Developer
AI stance
AI-native
Project type
Hobby / open-source project
Normalized one-liner
large model inference runtime for google colab
Manually corrected
False

Could you build this?

No Running a 125B MoE on a single A100 at thousands of tokens per second using a pinned-RAM KV tier requires elite low-level CUDA, PCIe transfer scheduling, and inference engine internals.

What it would actually take: Building this requires a bespoke LLM runtime written in C++ and CUDA that utilizes zero-copy pinned host memory and asynchronous PCIe DMA transfers to stream KV tensors without stalling the tensor cores. It necessitates custom MoE routing kernels and memory-bandwidth saturation techniques tailored precisely to A100 architecture. This demands top-tier GPU microarchitecture and deep systems performance engineering expertise.

Competitors

Other products that read as similar to this one — 106 launches clear the similarity bar, closest 8 shown.

Attention rank: #13 of 107 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 297 days after the earliest competitor.

Other launches for this product

Same idea, different domain

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