The Archive

Community discussion on local LLM frontends and limitations of llama-server as default interface.

Benchmark of vision LLMs vs. OCR pipelines on 30 long, image-heavy PDFs from MMLongBench-Doc shows LlamaCloud + Azure premium achieving 59.6%–58.5% accuracy; agentic RAG and native PDF vision approaches compared on cost and accuracy.

llama.cpp server adds native tool support (shell execution, file ops) via experimental --tools flag.

Wanted to share a workflow I tested on a real flight, in case anyone else is trying to set up offline Claude Code. The core idea: using ollama to pull the needed model of what you need, and then use it to run claude code The setup, in order: 1. Pull a model on home wifi the night before. \`ollama pull <model>\` — \~9 GB for a 14B, \~17 GB for a 26B. Don't try this at the gate. 2. In Claude Code, point at Ollama. The cleanest path I found is wrapping it in two aliases: alias claude-local='ollama launch claude --model gemma4:26b' alias claude-cloud='claude' 3. Verify on the ground with...

User reports Apex quantization for Gemma 4 26B achieves 38 tokens/sec at 90K context on RX 9060 XT with llama.cpp.

User configures dual AMD RDNA GPUs (48GB VRAM) with llama-cpp via Vulkan for local inference.

BeeLlama v0.2.0 achieves 4-5x token throughput gains on RTX 3090 via DFlash optimizations for Qwen 27B and Gemma 31B models.

Community fork of llama.cpp optimizes MoE inference on 12GB VRAM by loading only active experts rather than full layers.

cHunter789 releases Qwen-27B IQ4_KS quantization (14.1GB) optimized for 16GB NVIDIA GPUs via ik_llama.cpp.

SupraLabs released Supra-50M, a 50M-parameter Llama-style language model trained on 20B educational tokens with competitive benchmark performance.

llama.cpp b9274 fixes VRAM leak in speculative decoding by properly freeing draft context and decoder resources on server sleep.

Qwen 3.7 open-weights model released; community discussion on LocalLLaMA highlights adoption momentum.

llama.cpp PR #22929 fixes prompt processing performance issue affecting OpenCode and Pi model inference.

User reports 110 tok/s inference speed on Qwen 35B with 12GB VRAM using ik_llama.cpp, outperforming standard llama.cpp after MTP merge.

User shares llama.cpp configuration for running Qwen 3.6 27B locally with ROCm acceleration and optimizations.

llama.cpp PR #23287 optimizes MTP (multi-token prediction) draft sampling by moving logic to backend, improving inference performance.

Early-stage Gemma 4 MTP compilation work-in-progress shared on LocalLLaMA.

LM Studio 0.4.14 adds MTP Speculative Decoding support via llama.cpp 2.15.0 for faster inference.

llama.cpp PR #23269 introduces MTP (Multi-Token Prediction) improvements for faster local LLM inference.

llama.cpp MTP speculative decoding merged; Qwen 27B/35B inference speedups 1.24–2.44× on consumer GPUs/APUs with quantization.

Just wanted to share that I'm pretty happy about Qwen 35b a3b agentic coding performance. I'm running the model in q80 quant, kv cache both q8\_0 as well, with 262144 in 4090 + 5060 ti, via llama.cpp backend with claude code pointing to localhost. For demo/data analytics purposes, it works pretty well. I haven't used it for large codebases, but it definitely is better than gemma4 26b in my use case. One thing that surprises me is that it seems to get better outcome in agentic coding, than chat. When using it with just chat UI, i found the code qwen35b provide a bit too clunky. I wonder o...

Llama.cpp performance improved 1.5–1.8x in recent updates; MTP and prompt processing issues partially resolved.

MTP KV cache quantization in llama.cpp Qwen models reduces VRAM overhead without apparent inference degradation.

OpenBMB releases BitCPM4-CANN family (1B–8B params) with BitNet quantization; awaiting llama.cpp support.

Qwen 3.6 27B performance benchmarks across llama.cpp backends on RTX 3090: ik_llama.cpp achieved 1261 tok/s prefill, 72.9 tok/s decode with 156k context.

llama.cpp PR #23198 optimizes prompt decode by avoiding logits copying in MTP, improving inference speed.

llama.cpp fork adds quantized KV cache support for tensor parallelism across dual GPUs, addressing long-standing inference bottleneck.

Llama.cpp multi-token prediction on Qwen 3.6 27B shows 42% prefill slowdown but 85% token generation speedup on RTX 3090.

Benchmarking llama.cpp MTP (multi-token prediction) on Qwen 3.6 with RTX 5090, comparing inference speed with draft-mtp flag toggled.

User seeks faster inference alternatives to Ollama/LM Studio for local model serving (Gemma, Qwen, OpenBioLLM) on 64GB RAM.