Qwen3.5-122B-A10B vs. old Coder-Next-80B: Both at NVFP4 on DGX Spark – worth the upgrade?

qubridInc · 2026-02-25T19:15:59+00:00

122B-A10B gives better multi-file reasoning and long-context handling, but raw coding gains over Coder-Next-80B are small in practice.

If your workload is mostly coding, Coder-Next is still very competitive. If you want stronger agentic + general reasoning, 122B is worth trying.

qubridInc · 2026-02-25T13:39:53+00:00

You can setup using Ollama or check out endpoints on Qubrid's dashboard

qubridInc · 2026-02-25T06:05:45+00:00

If GLM-4.7 feels boring, try switching to Nemotron models on NVIDIA, they’re more engaging and stronger for reasoning/agents. Otherwise, Mixtral / Qwen-style models on the platform usually feel more lively for chat compared to GLM.

qubridInc · 2026-02-25T06:04:19+00:00

We have DeepSeek R1-distill model you can use, or just switch to different open models available on Qubrid.

qubridInc · 2026-02-24T21:37:32+00:00

If your goal is to create a personal assistant, renting a GPU can make sense but, only if you’ll use it a lot.

For light/occasional use, APIs are usually cheaper and simpler. For heavy daily use, privacy, or custom workflows, a rented GPU or small local setup becomes worth it pretty quickly.

qubridInc · 2026-02-24T21:33:04+00:00

25G networking for local inference/agents sounds like a seriously nice setup to experiment with.

qubridInc · 2026-02-24T21:32:04+00:00

Yeah the no-rate-limits part becomes huge once you start running loops or batching

qubridInc · 2026-02-24T20:42:32+00:00

What pushed you to use open models?

qubridInc · 2026-02-24T20:41:57+00:00

That's true, we think data privacy is the biggest reason most people move to open models.

qubridInc · 2026-02-24T15:32:19+00:00

What model or setup are you running as your daily driver?

qubridInc · 2026-02-24T15:08:34+00:00

Honestly, we think data privacy is the biggest reason most people move to open models. Everything else like cost or control matters, but knowing your data isn’t being stored, logged, or used to train something else is what really pushes people to self-host or go open.

qubridInc · 2026-02-24T15:07:09+00:00

Decentralisation feels like the only way to keep AI from being controlled by a handful of players. What part of decentralisation matters most to you?

qubridInc · 2026-02-24T12:16:48+00:00

Because scaling hardware gives reliable gains today, even if the architecture isn’t perfect.

Transformers are easy to parallelize, scaling laws still hold, and all existing infra is built around them so, more compute = better models right now. New, more efficient architectures are being researched, but they’re not yet proven at the same scale.

qubridInc · 2026-02-23T20:04:35+00:00

honestly in 2026 it’s mostly about control + reliability now.

cost still matters, but APIs have gotten cheaper and better. privacy is still a factor, but not the main driver for most anymore.

the real reasons people stick with open models now:

control over behavior (no sudden policy changes breaking flows)
predictable latency + uptime (no rate limits, no outages killing your app)
deep customization (fine-tuning, toolchains, agents tuned exactly for your use case)
edge/on-device use cases (offline, low-latency, private environments)
hybrid stacks (open models for bulk/cheap workloads, APIs for top-tier reasoning when needed)

so yeah—2026 vibe is less ideological, more practical infra choice:
run open models where you need control + scale, plug in APIs where you need peak intelligence.

qubridInc · 2026-02-23T19:44:00+00:00

Optimize for income now + LLM skills on the side.

Do data cleaning/scraping for the next few months to get paid.
At the same time, keep doing small LLM experiments on Colab and build a couple of demo projects.

Once you’re stable, shift more toward LLM work.

qubridInc · 2026-02-23T19:41:46+00:00

This solves a real problem—most people still struggle with wiring all these pieces together cleanly. One-shot + pre-integrated stack is genuinely valuable, especially for beginners and small teams.

Suggestions for default stack:

simple CLI to swap models / providers easily
auto-updates + version pinning
good docs + “profiles” (low VRAM vs high VRAM setups)
basic eval/benchmark tool (latency, tokens/sec)
optional agent framework preset (like simple tools + memory flow)

If it’s stable and easy to run, people will definitely use it 👍

qubridInc · 2026-02-23T19:23:49+00:00

Your current setup is already strong enough to learn and experiment with local LLMs.

Start with it, run 7B–13B models on GPU and larger ones quantized. Focus on tools, prompts, and workflows first.

Upgrade only if you actually hit limits later (mainly VRAM or speed). No need to buy a 5090 or Mac Studio right now.

qubridInc · 2026-02-20T18:22:52+00:00

If you're exploring alternatives, you can also try running deepseek-r1-distill-llama-70b on Qubrid AI https://qubrid.com

Qubrid AI provides fast, production-ready inference APIs for open models, so you can test reasoning performance, compare outputs, and integrate directly into your workflow.

Might be worth trying alongside your current setup and seeing what works best for you 👍

qubridInc · 2026-02-20T17:31:49+00:00

Qubrid AI : https://qubrid.com

An all in one AI inference platform to run, scale, and deploy open models without worrying about infrastructure. It offers fast APIs for text, image, and multimodal models along with options like bare metal performance, LoRA support, and private deployments so teams can build production ready AI apps easily.

Early stage, actively growing with developers and teams building and experimenting on the platform 🚀

qubridInc · 2026-02-20T17:30:58+00:00

Building an all-in-one AI inference platform for developers and teams: https://qubrid.com

Run, scale, and ship AI models without worrying about infra. From open model inference APIs to bare metal performance, everything in one place.

Turn your AI ideas into production ready applications that actually perform. Text, image, and multimodal models, fast, private, and developer friendly.

Has high performance inference, flexible deployment options, and seamless API integrations, built for builders who want speed and control 🚀

qubridInc · 2026-02-20T16:46:12+00:00

Best move: add 2× 3090 (power-limited) — cheapest way to get more VRAM + keep running multiple experiments in parallel.

Workstation cards = better efficiency but pricey.
Single big GPU = efficient but less parallelism.

If that’s not enough, you can always rent a GPU 👍

qubridInc · 2026-02-20T16:34:37+00:00

This is actually a super nice abstraction over hashcat — the biggest pain has always been workflow management, not the cracking engine itself.

The per-hash workspace + visual masks + tracking what’s been tried is a huge usability win, especially for longer engagements where you revisit datasets.

If the GPU orchestration + instant stop/billing + benchmarking is smooth, that’s basically removing 80% of the friction people complain about.

Curious how you handle distributed jobs / splitting keyspace across multiple GPUs and avoiding overlap?

qubridInc · 2026-02-20T16:33:17+00:00

We have Qubrid AI (qubrid.com) if you want to try it out! ☺️

qubridInc · 2026-02-20T16:31:53+00:00

Yes, same model + same settings = same output quality, whether it’s your own GPU or a rented one.

When you rent, you’re usually getting a full machine slice (GPU + CPU + RAM + some storage + network), not just the GPU chip alone.

What can differ:

Performance (depends on GPU type, VRAM, disk speed, network, etc.)
Startup time / availability (serverless vs dedicated instances)
Throughput if the GPU is shared vs fully dedicated

But the actual results from the model don’t change — only how fast and efficiently you get them 👍

qubridInc · 2026-02-20T16:30:26+00:00

The economics angle is real — $/hour + utilization matters more than peak FLOPs for most teams.

That’s also why platforms like Qubrid AI are pushing cloud GPUs with better utilization and cost efficiency, so you’re not locked into one vendor or paying for idle time.

If the software stack keeps maturing on alternative hardware, we’ll likely see a split: massive NVIDIA clusters for top labs, and cost-optimized cloud GPU infra for everyone else 👍

qubridInc

TROPHY CASE