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qwen38-flash-next-w4a16-cmp170hx

Qwen3.8-Flash-Next (W4A16) on 2x CMP 170HX: 110 tok/s decode, 7K prefill, 800 tok/s @ 23 concurrent - runs on 38 GB home RAM, no pinned memory

Details

External ID
1372897209
Source
GITHUB
Company
—
Product
qwen38-flash-next-w4a16-cmp170hx
Website domain
github.com
Launched
Sept. 16, 2026
Cohort
—
Upvotes
10
Upvotes percentile
0.28183448629259544
Tags
—
Fetched at
Sept. 20, 2026, 5:45 p.m.
Updated at
Sept. 20, 2026, 5:45 p.m.

Enrichment

Theme
local AI inference and ComfyUI tools
Vertical
Horizontal
Function
Model & infra
Audience
Developer
AI stance
AI-native
Project type
Hobby / open-source project
Normalized one-liner
model inference setup for running qwen on cmp 170hx gpus
Manually corrected
False

Could you build this?

No High-throughput LLM inference execution optimized for specific obscure mining GPUs (CMP 170HX) using 4-bit quantization and CPU-GPU pipelining requires deep CUDA/kernel expertise.

What it would actually take: Requires custom CUDA/Triton kernels adapted specifically to the Ampere GA100 architecture of CMP 170HX cards lacking standard display outputs, along with high-performance W4A16 GEMM implementations (like Marlin, AWQ, or ExLlamaV2). Developers must manually manage PCIe bandwidth, CPU-to-GPU offloading without pinned memory, and multi-stream batch scheduling.

Competitors

Other products that read as similar to this one — 108 launches clear the similarity bar, closest 8 shown.

Attention rank: #89 of 109 (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

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