Holo4: An Open Model Built to Use Your Computer, Not Just Chat
Holo4 is a new computer-use VLM family built to click, type, run code, and call tools. Here is what the 27B and 35B-A3B models are actually for.
Technical Research & Editorial Team
RAMGPT Editorial Team
The RAMGPT Editorial Team produces source-driven analysis of AI systems, model architectures, inference software, and open-source implementations. Its work emphasizes primary sources, technical verification, reproducible methodology, and careful separation between measured results, third-party claims, and editorial interpretation.
Published work
Articles
25 published
Your Windows llama.cpp Build May Be Leaving VNNI on the Table
A fresh llama.cpp issue shows how MSVC feature detection can silently compile out AVX-VNNI paths even when the CPU supports them.
When Lossless Speculation Stops Being Lossless on Blackwell
Two new llama.cpp reports show how batch-dependent CUDA kernels can break greedy speculative-decoding equivalence on Blackwell GPUs.
Speculative Decoding's Hidden Context-Reuse Failure
A forensic analysis of llama.cpp issue #28049, where accepted tokens past EOG can invalidate reusable state and trigger large re-prefill costs.
LongCat Sparse Lands in llama.cpp — But the Attention Path Is Still Dense
A source-path experiment on the first LongCat-Flash-Lite-Sparse llama.cpp fork: LSA token selection is real, but selected tokens are converted into a mask over full K/V rather than compact sparse attention execution.
DFlash2 Lands in llama.cpp: What PR #27342 Actually Changes
A code-level analysis of llama.cpp's DFlash2 merge: dynamic local convolution, lattice path selection, runtime integration, benchmarks, and review tradeoffs.
Porting Qwen4 to llama.cpp: Qwen3.8-Flash-Next Internals
Implementation strategy for Qwen3.8-Flash-Next in llama.cpp: Gated DeltaNet reuse, Hyper-Connections, PLE, QSA, state handling, CUDA, and GGUF.
Qwen4 in llama.cpp: Dissecting PR #27742
PR #27742 shows Qwen4 needs no new GGML ops; the hard engineering is indexed state, PLE residency, quantizer staging, and backend memory invariants.
Qwen4 in llama.cpp: A Pre-Merge Runtime Audit of PR #27742
A systems audit of Qwen4Exp in llama.cpp: state ownership, indexer-cache lifecycle, multi-slot correctness, quantized KV, graph reservation, and PLE.
1-Bit LLMs Are Not Just Quantization: Why Ternary Models Matter
Why 1-bit and ternary LLMs may reshape local AI, and why tiny weights do not mean one-quarter the inference cost of a Q4 model.
llama.cpp Is More Than a GGUF Runner: A Local Inference Stack Emerges
Why llama.cpp is evolving from a GGUF model runner into a broader local inference stack spanning servers, scheduling, KV caches, and speculative decoding.
AI Image Settings Explained: Seed, Steps, CFG and Samplers
Learn what seed, inference steps, CFG guidance, samplers and schedulers do in AI image generation, why settings change results, and how to tune them.
GGUF Quantization Levels Explained: Q4, Q5, Q6 and Q8
Learn what GGUF quantization labels such as Q4_K_M, Q5_K_M, Q6_K and Q8_0 mean, how they affect model size, quality, RAM, VRAM, and speed.
How Does AI Image Generation Work? Diffusion Models Explained
Learn how AI image generators turn text into images, including noise, denoising, latent space, text encoders, guidance, samplers, seeds, and diffusion steps.
How Does AI Video Generation Work? Text-to-Video vs Image-to-Video
Learn how AI video generators create motion from text or images, including latent video, temporal consistency, diffusion transformers, frames, seeds, and camera control.
How Does Whisper Work? Speech Recognition Explained
Learn how OpenAI Whisper turns audio into text using log-Mel spectrograms, encoder-decoder Transformers, language tokens, timestamps, and autoregressive decoding.
How to Reduce LLM VRAM Usage Without Breaking Performance
Learn how to reduce local LLM VRAM usage with quantization, shorter context, KV-cache compression, CPU offloading, smaller models, and runtime tuning.
Speech-to-Text vs Text-to-Speech: What's the Difference?
Learn the difference between speech-to-text and text-to-speech, how ASR and TTS models work, key accuracy metrics, streaming, voice cloning, and local AI use.
What Does Context Length Mean in an LLM?
Learn what LLM context length means, what counts toward the context window, how tokens, chat history, KV cache, VRAM, truncation, and long-context models work.
What Is LLM Quantization? FP16, INT8 and INT4 Explained
Learn how LLM quantization reduces model memory, what FP16, INT8 and INT4 mean, and how GGUF, AWQ, GPTQ, scales, groups, and quality trade-offs work.
Why Is My Local LLM Slow? Common Causes and Fixes
Learn why a local LLM runs slowly and how model size, GPU offloading, context length, memory bandwidth, CPU threads, and runtime settings affect speed.
GGUF vs AWQ: What's the Difference and Which Should You Use?
GGUF and AWQ solve different parts of local LLM deployment. Learn how they differ in format, quantization, hardware support, speed, and practical use.
How Much VRAM Do You Need for Local LLMs?
Learn how to estimate LLM VRAM requirements from model size, quantization, KV cache, context length, runtime overhead, and GPU offloading.
How to Run an LLM Locally: A Beginner's Guide
Learn how to run an LLM locally on Windows, Linux, or macOS, choose a model and quantization, understand hardware needs, and avoid common mistakes.
What Is a KV Cache? How It Works and Why It Uses VRAM
Learn what an LLM KV cache stores, why it speeds up token generation, how to estimate its VRAM use, and how context length and quantization affect memory.