Instructions to use Voidstackloop/Tessera-125B-MoE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Voidstackloop/Tessera-125B-MoE with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf Voidstackloop/Tessera-125B-MoE:IQ2_S # Run inference directly in the terminal: llama cli -hf Voidstackloop/Tessera-125B-MoE:IQ2_S
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Voidstackloop/Tessera-125B-MoE:IQ2_S # Run inference directly in the terminal: llama cli -hf Voidstackloop/Tessera-125B-MoE:IQ2_S
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf Voidstackloop/Tessera-125B-MoE:IQ2_S # Run inference directly in the terminal: ./llama-cli -hf Voidstackloop/Tessera-125B-MoE:IQ2_S
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf Voidstackloop/Tessera-125B-MoE:IQ2_S # Run inference directly in the terminal: ./build/bin/llama-cli -hf Voidstackloop/Tessera-125B-MoE:IQ2_S
Use Docker
docker model run hf.co/Voidstackloop/Tessera-125B-MoE:IQ2_S
- Llama new
- LM Studio
- Jan
- Ollama
How to use Voidstackloop/Tessera-125B-MoE with Ollama:
ollama run hf.co/Voidstackloop/Tessera-125B-MoE:IQ2_S
- Unsloth Desktop
- Pi
How to use Voidstackloop/Tessera-125B-MoE with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Voidstackloop/Tessera-125B-MoE:IQ2_S
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "Voidstackloop/Tessera-125B-MoE:IQ2_S" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Voidstackloop/Tessera-125B-MoE with Docker Model Runner:
docker model run hf.co/Voidstackloop/Tessera-125B-MoE:IQ2_S
- Lemonade
How to use Voidstackloop/Tessera-125B-MoE with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Voidstackloop/Tessera-125B-MoE:IQ2_S
Run and chat with the model
lemonade run user.Tessera-125B-MoE-IQ2_S
List all available models
lemonade list
- Hermes Agent
How to use Voidstackloop/Tessera-125B-MoE with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Voidstackloop/Tessera-125B-MoE:IQ2_S
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default Voidstackloop/Tessera-125B-MoE:IQ2_S
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Voidstackloop/Tessera-125B-MoE with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Voidstackloop/Tessera-125B-MoE:IQ2_S
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "Voidstackloop/Tessera-125B-MoE:IQ2_S" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Download benchmarks/SPEED_PC.md from Voidstackloop/Tessera-125B-MoE: direct link, hf CLI and curl.
- Browser
- Download file 2.12 kB
-
https://huggingface.co/Voidstackloop/Tessera-125B-MoE/resolve/main/benchmarks/SPEED_PC.md
- Command line
-
hf download hf://Voidstackloop/Tessera-125B-MoE/benchmarks/SPEED_PC.md
-
curl -L -o SPEED_PC.md https://huggingface.co/Voidstackloop/Tessera-125B-MoE/resolve/main/benchmarks/SPEED_PC.md
One-token routed-expert matvec on this PC (2026-10-26)
Tool: bench.c (build with build.bat, run with run.bat <threads>), CPU backend of C:\Projects\llama.cpp-strata\build-clang
(AVX2 + AVX-VNNI, no AVX-512), i7-13620H, DDR5-5200, 10 threads. 10 of 128 experts per op, ids rotated so the weights stream from RAM.
Shapes are k448-like guesses: gate/up k=2048 m=1024, down k=1024 m=2048. A 78 GB download was running in the background (light CPU use).
| type | gate/up Gweights/s (GB/s) | down Gweights/s (GB/s) | note |
|---|---|---|---|
| iq2_s (k448 gate/up) | 96.7 (31.0) | 90.4 (29.0) | compute-bound: about half of what RAM allows |
| iq4_nl (k448 down) | 93.4 (52.5) | 101.2 (57.0) | close to RAM speed |
| iq3_xxs | 61.4 (23.5) | 70.3 (26.9) | the slow one (Strata's model type) |
| q2_K | 183.1 (60.1) | 174.7 (57.3) | what a RAM-speed 2-bit kernel looks like |
| q4_K | 115.9 (65.2) | 118.8 (66.8) | RAM ceiling is about 65 GB/s |
| q4_0 | 112.7 (63.4) | 114.6 (64.5) | |
| q6_K | 71.7 (58.8) | 63.0 (51.7) |
Reading: with k448's types the CPU expert work is about 95 Gweights/s. An IQ2_S kernel at RAM speed would be about 190 Gweights/s. Estimate (not measured on the model): 2.36 B routed weights per token / 95 G = about 25 ms per token if every expert ran on the CPU.
Server baseline on the model (Minecraft closed, Overwolf on)
no MTP 17.5 tok/s; MTP 1 draft 20.3-20.7; MTP 2 drafts 21-22 (acceptance 93%); GPU cache 645 MiB, hit rate about 14%. With Minecraft running: 11.7 no-MTP. Bigger cache + MTP draft fails to load (bug).
All-VRAM test
no MTP, cache 3200 MiB, margin 250, ubatch 512: 20.1-22.5 tok/s, hit 52%, VRAM 7947/8188. MTP2 + cache 1800 (margin 700): 21.4-22.4, hit 29%. Cache 2400+ with MTP: draft fails to load (VRAM). They do not stack.
Cache/draft, long prompts, kernel (2026-10-26)
Draft on CPU + cache 3000: 21.6 tok/s (no gain). Prefill: 5,198 tokens at 164 tok/s; repeat prompt 0.2 s. Decode on prose explanation 14-16 tok/s (draft acceptance 65% vs 93% for code). IQ2_S kernel already AVX2 with table gathers; est. upside under 10%, not written.