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keys-DeepSeekV4.1-Flash-TR3-Hybrid-1M-Context-8M-KV-Four-DGX-Sparks-with-abliterated-option

DeepSeek-V4.1-Flash TR3-Hybrid: 1M context, 8M+ KV pool (9,452,923 tokens) on 4x DGX Spark, TP4, CUDA graphs + DSpark k=5 + vision + tools. Pool grown via GMU 0.83 + batch 2048. With abliterated option.

Details

External ID
1367953522
Source
GITHUB
Company
—
Product
keys-DeepSeekV4.1-Flash-TR3-Hybrid-1M-Context-8M-KV-Four-DGX-Sparks-with-abliterated-option
Website domain
github.com
Launched
Sept. 13, 2026
Cohort
—
Upvotes
6
Upvotes percentile
0.006853702280297207
Tags
—
Fetched at
Sept. 17, 2026, 1:24 a.m.
Updated at
Sept. 17, 2026, 1:24 a.m.

Enrichment

Theme
DeepSeek model deployment and inference
Vertical
Horizontal
Function
Model & infra
Audience
Developer
AI stance
AI-native
Project type
Hobby / open-source project
Normalized one-liner
deepseek v4.1 model weights and configurations for multi-gpu inference
Manually corrected
False

Could you build this?

No Serving a 1M-context model with an 8M+ KV cache pool across multi-node DGX systems requires massive enterprise GPU infrastructure and deep distributed systems optimization.

What it would actually take: The stack relies on distributed inference engines (vLLM or custom TensorRT-LLM) using Tensor Parallelism (TP4), custom CUDA graph capture, and specialized KV-cache memory offloading. The hardest part is stabilizing memory bandwidth, synchronization, and latency across multi-DGX setups for nearly 10 million KV tokens. This requires hundreds of thousands of dollars in GPU hardware and elite high-performance computing (HPC) engineering skills.

Competitors

Other products that read as similar to this one — 118 launches clear the similarity bar, closest 8 shown.

Attention rank: #116 of 119 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 299 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.