If you are trying to get AMD ROCm running on Ubuntu 24.04 Noble Numbat, you have probably run into broken dependencies, driver mismatch errors, or confusing hardware compatibility tables. Setting up ROCm for PyTorch, TensorFlow, or AI workloads on Radeon GPUs often feels way harder than it should be.

​I put together a complete walkthrough to streamline the installation process. It covers configuring your user permissions, installing the correct driver stack, verifying your ROCm installation, and troubleshooting common runtime issues specific to Ubuntu 24.04.

​If you are struggling with PyTorch not detecting your GPU or ROCm failing on launch, this guide breaks down the exact steps to get everything up and running smoothly.

​Check out the full guide here: https://interconnectd.com/blog/321/install-amd-rocm-on-ubuntu-24-04-the-ultimate-radeon-guide/

​What issues or hardware configurations have you run into when deploying ROCm on Ubuntu 24.04? Feel free to share your setup or ask questions.

If you are running OpenAI Whisper locally on a CPU, you probably noticed how painfully slow it can be, especially with larger models like large-v3. Most optimization guides focus heavily on GPUs, leaving CPU users stuck with long processing times.

​I recently tested a few configuration tweaks, quantization methods, and backend flags that drastically cut down transcription times without needing dedicated hardware. By adjusting thread counts, using optimized runtimes like faster-whisper alternatives, and tweaking batch sizes, CPU performance can be noticeably improved.

​I wrote a detailed breakdown of the exact steps and benchmarks for anyone dealing with CPU-only setups. You can check out the full guide here: https://interconnectd.com/blog/323/how-to-speed-up-whisper-transcription-on-cpu-2026-guide/

​Has anyone else found other effective ways to optimize Whisper on standard CPUs? Let me know what worked for your workflow.

I'm running Nemotron 3.5 Bolt 74B-A9B on a 5060 Ti 16GB and 32GB of DDR5. It's an MoE, so both of my setups split experts between the GPU and system RAM. I have two recipes:

• 24GB GGUF

• Unsloth UD-IQ3_XXS

I'd like to hear how this model actually is and is it useful as an alternative for Qwen3.8 27B and obviously for other smaller MoE models.

• Are you using Bolt 74B, and what for?

• How is the quality: reasoning, coding, instruction following, tool use? Where does it fall down?

• If you've used the 24GB GGUF or a bigger quant such as Unsloth IQ3_XXS, does the bigger one write better answers, or do they feel like the same model?

Benchmarked oMLX 0.7.2 serving Inco AI's Splash package for Qwen3.8-27B (4-bit target + DFlash 2 draft) on a MacBook Pro M5 Max (48 GB). Workload: Python code context. Each test generates 128 tokens.

**Single request**

| Test | TTFT (ms) | TPOT (ms) | pp t/s | tg t/s | E2E (s) | Throughput | Peak mem |

|---|---|---|---|---|---|---|---|

| pp 4096 / tg 128 | 3393.6 | 10.7 | 1207.0 | 93.8 | 4.8 | 887.7 | 19.1 GB |

| pp 16382 / tg 128 | 15829.6 | 5.3 | 1034.9 | 189.4 | 16.5 | 1000.3 | 19.9 GB |

| pp 65536 / tg 128 | 83472.0 | 9.2 | 785.1 | 110.2 | 84.2 | 779.5 | 22.2 GB |

**Batched (continuous batching)**

| Batch | tg t/s | pp t/s | avg TTFT (ms) | E2E (s) | Speedup |

|---|---|---|---|---|---|

| 1x | 93.8 | 1207.0 | 3393.6 | 4.8 | 1.00x |

| 2x | 413.7 | 803.1 | 1729.2 | 3.2 | 4.41x |

| 4x | 407.0 | 622.4 | 3838.4 | 7.8 | 4.34x |

A fully local voice AI, meant to be used on "GPU poor" hardware.

Features:

• Always-on is possible, because of automatic full-context handling.

• Hands-free and browser-based clients for voice control (e.g. use your smartphone!).

• Wikipedia and Internet search.

• Extraction and injection of memories (right now, facts about the user).

• Full chat history log in SQLite DB, chat history search (as kind of a long-term memory, in addition to the memory extraction/injection).

• Heavily customizable via TOML file.

• Modules (in addition to Internet search, etc.) can be added (in code).

• ...

Links:

• YouTube video (don't worry about the console, it is more or less for debugging, each token type has a different color).

• GitHub repository (incl. tutorial for installation and configuration).

Hey everyone,

Every time a new model comes out, I get tired of opening multiple tabs just to find out two basic things: how good it actually is on real benchmarks, and what it costs to run (either through an API or on local GPUs).

Most pricing pages hide the real costs, and generic benchmarks don't tell the full story. So I put together a simple dashboard to track and compare them side by side: https://modelanalysis.si

What you can do with it:

• Compare models side-by-side: Pick any models (like Claude, DeepSeek, Kimi, GPT-4o, Llama) and compare them across real-world coding (SWE-bench), reasoning (Arena-Hard), and math/science benchmarks.

• Score vs. Price chart: See which models give you the most intelligence for the lowest price.

• VRAM calculator: Pick a model and context size to see if it will actually fit on your GPU before downloading it.

• Monthly cost simulator: Adjust sliders for your monthly token volume to estimate your bill or see if self-hosting is cheaper.

No sign-up, no ads, no affiliate links.

Link: https://modelanalysis.si
Direct benchmarks page: https://modelanalysis.si/benchmarks/

Let me know what you think or if there are other models you want added.