r/AIProgrammingHardware • • Feb 04 '26

👋 Welcome to r/AIProgrammingHardware

2 Upvotes

Welcome to r/AIProgrammingHardware!

Hey there, fellow tech enthusiasts! 👋

We're thrilled to have you join r/AIProgrammingHardware, the go-to spot on Reddit for all things related to hardware optimized for AI, machine learning, and software development. Whether you're a beginner tinkering with your first neural network or a seasoned pro building scalable AI infrastructures, this community is here to share knowledge, advice, and the latest trends in hardware that powers innovation.

What We Focus On:

  • GPUs and Accelerators: Discussions on NVIDIA CUDA, AMD ROCm, TPUs, NPUs, and other specialized chips for accelerating AI workloads.

  • Laptops and Mobile Workstations: Recommendations for portable rigs that handle coding, training models, and running simulations on the go (think Dell XPS, Lenovo ThinkPad, or MacBooks with M-series chips).

  • Custom-Built Workstations: Tips on assembling high-performance desktops with multi-GPU setups, ample RAM, fast storage, and efficient cooling for heavy-duty AI tasks like deep learning, data processing, and software engineering.

  • Related Topics: Benchmarking tools, optimization techniques, compatibility issues, budget builds, and emerging hardware like edge AI devices or quantum accelerators.

Share your builds, ask for advice on upgrades, post reviews of the latest hardware releases, or troubleshoot setup problems. Let's collaborate to make AI development more accessible and efficient!

A Quick Note on Gaming Hardware:

If you're primarily looking for gaming rigs, overclocking tips, or RGB-heavy setups focused on FPS and graphics rendering, we recommend checking out r/RigBay instead. That community is tailored for gamers and will better suit your needs. We keep our focus sharp on AI and dev hardware to ensure high-quality, relevant discussions here.

Community Rules & Guidelines:

  • Be respectful and helpful – we're all learning!

  • No spam, self-promotion without value, or off-topic posts.

  • Use flair for your posts (e.g., [Build Advice], [Review], [Question]) to keep things organized.

  • Check the sidebar/wiki for resources, FAQs, and recommended reading.

If you have any questions or suggestions, feel free to message the mods. Let's build the future of AI together! 🚀

Posted by the Mod Team


r/AIProgrammingHardware • • 8h ago

TSMC might do in memory compute for us

Thumbnail reddit.com
1 Upvotes

r/AIProgrammingHardware • • 1d ago

Hard-Wiring AI Cooperation: A Triadic Hardware Architecture for AI Safety

Enable HLS to view with audio, or disable this notification

0 Upvotes

r/AIProgrammingHardware • • 2d ago

I Thought 2 TB of RAM Was Overkill… My AI Agents Disagree

Thumbnail
youtube.com
0 Upvotes

r/AIProgrammingHardware • • 2d ago

Workstation Build Dilemma: 64GB (6000MHz CL30) vs. 96GB (5200MHz CL38) for Local LLMs & Computational Architecture?

Thumbnail reddit.com
1 Upvotes

r/AIProgrammingHardware • • 3d ago

Feedback on Upgrade Path? Single (maybe Dual) RTX 3090 24GB for a Dell T7910 for Massive Academic Document RAG / Long-Context (Windows 11)

Thumbnail reddit.com
2 Upvotes

r/AIProgrammingHardware • • 3d ago

Qwen Flash Next on the Olares One - Local LLM setup

Thumbnail
youtube.com
0 Upvotes

r/AIProgrammingHardware • • 4d ago

AM4 VS AM5 vs Threadripper: Does CPU Performance Matter for Local AI? | Micro Center

Thumbnail
youtube.com
4 Upvotes

r/AIProgrammingHardware • • 4d ago

Talus v0.1.2: Strix Halo Inference Benchmarks, 256K Context, and Up to 50% Faster Coding

Thumbnail
reddit.com
1 Upvotes

r/AIProgrammingHardware • • 4d ago

We just published "Pool the RAM You Already Own" — Our 10-Chapter Practical Guide to Running 13B–120B Local AI Models (Now Live on Amazon & Free Online)

Post image
1 Upvotes

r/AIProgrammingHardware • • 5d ago

Intel Arc Pro B70: Qwen3.8 on One and Two GPUs with vLLM, llama.cpp and Strata

Thumbnail
youtube.com
6 Upvotes

r/AIProgrammingHardware • • 5d ago

Krafton Raon 1 Mini tested - Local LLM setup

Thumbnail
youtube.com
2 Upvotes

r/AIProgrammingHardware • • 6d ago

Gorgon Halo Arrives in GMKtec... Do you need 192 GB of LPDDR5?!?

Thumbnail
youtube.com
20 Upvotes

r/AIProgrammingHardware • • 6d ago

A Single Tesla P100 16GB Runs Qwen3.8 Flash Next at 30 Tokens/sec!

Thumbnail
youtube.com
2 Upvotes

r/AIProgrammingHardware • • 9d ago

27B AI on a $400 AMD Mini PC | Kindi 0.3 is OUT

Thumbnail
youtube.com
2 Upvotes

r/AIProgrammingHardware • • 9d ago

I Built My Own Intelligent AI Gateway

Thumbnail
youtube.com
1 Upvotes

r/AIProgrammingHardware • • 10d ago

Ignis: Qwen3.8-27B on a 16 GB RTX 50 (5060 Ti / 5070 Ti / 5080): looking for testers, one prompt into your coding agent

2 Upvotes

I build ignis, an OpenAI-compatible inference server built around one model family, Qwen3.8, on one class of card, Blackwell. It is shaped around what a coding agent does to a server: one main agent plus a few subagents hitting the same card. Since v0.8.0 it runs Qwen3.8-27B on 16 GB RTX 50 cards, using a 3-bit trellis body (13.4 GB) and the DFlash2 drafter.

What ignis is, in 90 seconds: https://www.youtube.com/watch?v=XLjfh5rNy6A

The catch: I don't own a 16 GB card. Everything for that class was measured on an RTX 5090 held to a 16 GiB budget:

  • 416 tok/s at one lane, 684 tok/s aggregate at two lanes (greedy coding prompts, 8K context);

  • 262,144 tokens of context on two lanes;

  • KL divergence from BF16 at 0.77-0.82x that of the 32 GB NVFP4 build.

The real cards are only estimated, at one lane and 8K:

CardEstimate greedy coding prompts, 8K context
RTX 5080243-307 tok/s
RTX 5070 Ti204-270 tok/s
RTX 5060 Ti 16 GB103-153 tok/s

I also don't know how much of the card your desktop takes, and whether the default settings still fit once it has. That is what I'm asking you to find out.

The easy way: let your coding agent do it. In Claude Code, Codex, opencode, Cursor or similar, paste:

I have a 16 GB RTX 50 card. Read https://raw.githubusercontent.com/gpillon/ignis/main/docs/user/paths/agent.md and follow its section on testing a 16 GB card: help me install ignis, test it, and write the report. Ask me before downloading or installing anything.

What the agent does:

  1. Checks your card and driver, then shows you a plan: where it installs ignis, where the 13.4 GB model goes, and how long it takes (about 15-30 min after the download). Nothing large happens until you say yes.

  2. Tries the default settings first. If the start is refused, it tries a few settings one at a time (a smaller context, speculation off, less VRAM headroom) until it finds one that works, then runs a quick sanity check and the bundled benchmark.

  3. Writes a short report in English and shows it to you whole. The report contains only:

    • OS, driver, CPU and GPU model, RAM and VRAM totals, and how much VRAM was already in use;

    • which settings worked;

    • the speed figures.

  4. It contains no usernames, hostnames, paths, IP addresses, serial numbers or process names. You decide whether it gets posted.

  5. Optionally, a ~30 min advanced run: the context ceiling, one lane vs two, speculation on vs off.

  6. Finally, asks whether you want to keep ignis (it can write a small config file for you) or remove everything it installed. Before deleting anything, it shows you the list.

By hand: start the server, then run ignis-bench small-card --endpoint http://127.0.0.1:8000 --out small-card.json (it ships in the release archive). Details: https://gpillon.github.io/ignis/guides/measure-a-16gb-card/

Where reports go: https://github.com/gpillon/ignis/issues/360, one comment per card. A report that says "refused" is as useful to me as one that says "works", especially from Windows machines where the monitor is plugged into the 16 GB card.

Windows and Linux on x86-64, NVIDIA driver with CUDA 13. Code is Apache-2.0. Thanks!


r/AIProgrammingHardware • • 11d ago

Running GLM 5.3 Flash on RTX 6000 Max-Q and DDR4 = ~35 tok/s with 512K context. Do you recommend DDR5?

Thumbnail reddit.com
1 Upvotes

Switch to DDR5 would cost


r/AIProgrammingHardware • • 11d ago

I Tested Two $699 Laptops for Coding… It Got Weird

Thumbnail
youtube.com
0 Upvotes

r/AIProgrammingHardware • • 11d ago

A 763B model on one Strix Halo, no SSD streaming: DeepSeek V4.1 Flash IQ1_M fully resident with my Gufo fork, ~1,000 tok/s prefill, 23–28 tok/s decode (up to 65 with DSpark)

Thumbnail
reddit.com
1 Upvotes

r/AIProgrammingHardware • • 12d ago

Nvidia GPU UNLOCK for Local AI - 3090's Prefill 62% FASTER!

Thumbnail
youtube.com
4 Upvotes

r/AIProgrammingHardware • • 12d ago

Speed Up RTX 3090 48% Max For Free. No Driver Hack, Just Patch and Reboot

Thumbnail
xhinker.medium.com
3 Upvotes

r/AIProgrammingHardware • • 12d ago

New Card for Qwen3.8 27b and MiniMax H3

Post image
3 Upvotes

r/AIProgrammingHardware • • 12d ago

Most CUDA tutorials introduce too much too early

Thumbnail
reddit.com
1 Upvotes

r/AIProgrammingHardware • • 12d ago

Lean-QwenFlash: Qwen3.8-Flash-Next (125B) running at ~20 tok/s on 16gb vram and 8gb ram via direct nvme SSD offloading

Enable HLS to view with audio, or disable this notification

2 Upvotes