Skip the command line.Let AI tune the flags.

All locally in one interface.
Manage your models.Chat with AI.Benchmark performance.Generate images.

What's the difference between Q4_K_M and Q8_0, and which should I use on 12 GB of VRAM?
Q8_0 is nearly lossless but takes roughly twice the memory of Q4_K_M. On 12 GB of VRAM a 14B model in Q4_K_M (about 9 GB) fits with a 32K context, and quality drops only slightly.
Qwen3 14B · Q4_K_M

02 / RUNTIME CONTROL

Interface and terminal. One window.

Control llama.cpp and KVMem (a fork optimized for KV cache) from a GUI with a one-click backend switcher, or drop into the built-in terminal when you want full control. Add a model, click around, watch the status live.

03 / HYBRID WORKFLOW

Split the screen. Work hybrid.

Start Claude Code next to OpenCode and keep both in view. Pick a layout (columns, one big pane with a small one, or stacked) and drag a session in the sidebar to swap places. Your local model and Claude in the cloud, side by side.

HuggingFace · "qwen"GGUF
SearchqwenTrending
meta-llama/Llama-4-Scout-GGUF
For chat · 412K downloads
✓ fits
Qwen/Qwen3.8-Max-GGUF
MoE · 268K downloads
✓ fits
llava-hf/llava-v1.7-GGUF
Image and text · 94K downloads
⚠ too large
Estimated GPU memory14.2 / 24 GB

04 / MODEL DISCOVERY

Browse and download from HuggingFace.

Search, sort (trending / downloads / likes) and filter by category right inside the app. Every model gets a hint on whether it fits your VRAM, accounting for MoE, quantization, and mmproj files for vision models.

MoE-awareVRAM fitVision models

05 / MEASURE, THEN TUNE

Four ways to benchmark.

Not one eyeballed measurement, but four different tests, each answering a different question.

04tests
01tokens/s

Speed

Raw tokens/s via llama-bench, llama.cpp's official benchmark tool. For KVMem (which has no llama-bench of its own) the measurement runs through the live server instead, so it's approximate.

0218 tasks

Quality

18 tasks (math, logic, code, format, knowledge, precision) scored deterministically, with no AI involved.

03wikitext-2

Perplexity

The full wikitext-2 set, the same reference corpus llama.cpp uses in its own examples, so results are directly comparable to published ones.

04A/B

Compare

Your own prompt run on several models side by side, optionally scored by an AI judge.

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v0.1.68 · Windows 10/11 (~110 MB) · macOS Apple Silicon (~130 MB) · Linux coming soon