tonyd2wild/deepseek-v4-flash-dspark-abliterated-uncensored-2x-dgx-spark — explained in plain English
Analysis updated 2026-05-18
Serve a long-context language model across two networked DGX Spark machines.
Run coding agent fleets against a locally hosted model API.
Benchmark tokens-per-second and concurrent request throughput on multi-GPU hardware.
Troubleshoot garbled or repeated output under heavy concurrent load.
| tonyd2wild/deepseek-v4-flash-dspark-abliterated-uncensored-2x-dgx-spark | aarav90-cpu/fetchit | adammartinez271828/x4-analysis | |
|---|---|---|---|
| Stars | 10 | 10 | 10 |
| Language | Python | Python | Python |
| Setup difficulty | hard | easy | easy |
| Complexity | 5/5 | 2/5 | 2/5 |
| Audience | ops devops | developer | general |
Figures from each repo's GitHub metadata at analysis time.
Requires two networked DGX Spark machines with matching container images, InfiniBand or RoCE networking, and manual editing of environment files for each node.
This repository is a deployment recipe for running a large language model called DeepSeek V4 Flash DSpark across two connected DGX Spark machines, a type of dedicated AI hardware from Nvidia. The model weights themselves were created by someone else, known as Keys or drowzeys, who produced an unrestricted, less filtered version of the original model and hosts it on Hugging Face. This repository does not include those weights. It only provides the configuration, scripts, and container setup needed to run them, and gives full credit to the original creator. The project is a fork of an earlier recipe built for the standard version of the same model, with only the model weights swapped out for the unrestricted variant. Everything else, including the tuned settings, patches, and performance benchmarks, stays the same. Running it requires two DGX Spark units networked together over a fast connection, using a setup called tensor parallelism to split the model across both machines. The recipe includes shell scripts to build the container image, download and verify the model files, and start the service, plus environment variable files you edit to match your own network addresses and hardware paths. Once running, the model is served through a web API that other programs, including coding assistants, can connect to and send requests. The README documents specific performance numbers such as tokens generated per second and how many requests it can handle at once, along with a long, calibrated context window that lets the model consider very large amounts of text at a time. It also includes troubleshooting notes for problems like garbled output or repeated text under heavy concurrent use, along with guidance on the safe upper limit for context length versus figures that were pushed further as an experiment rather than a guaranteed quality benchmark. This project is aimed at people already running fleets of AI coding agents on multi node hardware who want long context and stable output under concurrent use. The full README is longer than what was shown.</content>
A deployment recipe for running an unrestricted variant of the DeepSeek V4 Flash DSpark language model across two DGX Spark machines with a long context window.
Mainly Python. The stack also includes Python, Docker, vLLM.
Setup difficulty is rated hard, with roughly 1day+ to a first successful run.
Mainly ops devops.
This repo across BitVibe Labs
double-check against the repo, no cap.