CLI
Detect your hardware.
Know what you can run.
tamebi is a CLI tool that automatically detects your machine's hardware (CPU, RAM, GPU, disk) and tells you exactly which LLM models you can run, with estimated memory usage, throughput, and time to first token.
Install
pip install tamebior with uv:
uv pip install tamebiNVIDIA, AMD, and Apple Silicon are all detected automatically. No extra flags or extras needed.
Quick Start
tamebi checkCLI Reference
tamebi checkDetect hardware and show what's runnable. Output has three sections:
- Hardware : CPU, RAM, GPU, disk, and available inference memory
- Top Recommendations : the best 3 models for your machine with Ollama run commands
- Runnable Models : all models that fit, with release date, precision, memory breakdown, speed estimate, and TTFT
| Flag | Short | Default | Description |
|---|---|---|---|
--json | -j | false | Output as JSON instead of rich tables |
--context-length | -c | 4096 | Context length in tokens. KV cache scales linearly; 4K vs 128K changes memory dramatically |
--batch-size | -b | 1 | Concurrent requests. Each gets its own KV cache. Set >1 to serve multiple users |
--verbose | false | Show detailed detection info (driver versions, etc.) |
tamebi modelsShow the full model compatibility matrix: every model in the catalog across all precisions (INT4, INT8, FP16), with fit status and memory at each level.
tamebi models| Flag | Short | Default | Description |
|---|---|---|---|
--context-length | -c | 4096 | Context length for KV cache estimation |
--batch-size | -b | 1 | Batch size for KV cache estimation |
tamebi updatePull the latest model catalog from the remote. The catalog updates automatically in the background but you can force a refresh with this command.
tamebi updateExamples
# Basic hardware check
tamebi check
# JSON output for scripting
tamebi check --json
# Estimate for serving 4 concurrent users with 8K context
tamebi check --batch-size 4 --context-length 8192
# Use each model's native max context window instead of the 4K default
tamebi check --context-length 0
# Browse all models and their compatibility across precisions
tamebi models
# Force-refresh the model catalog
tamebi updateSupported Hardware
| Vendor | Detection Method | Details |
|---|---|---|
NVIDIA | nvidia-ml-py (NVML) | Model, VRAM, CUDA version, compute capability |
AMD | rocm-smi (subprocess) | Model, VRAM (requires ROCm) |
Apple Silicon | system_profiler | Chip model (M1/M2/M3/M4), unified memory |
CPU-only | psutil + py-cpuinfo | Cores, threads, frequency, architecture |
Model Catalog
The catalog is automatically updated weekly and covers the latest releases from major labs including Meta, Mistral, Google, Qwen, DeepSeek, GLM, MiniMax, Kimi, Liquid, and AllenAI. Models are fetched directly from HuggingFace Hub, with no manual maintenance required.
Run tamebi update at any time to pull the latest catalog.
How Estimation Works
Memory is estimated per model and precision:
Total VRAM = Model Weights + KV Cache + Overhead
Model Weights = params (billions) × bytes_per_param
FP16: 2 bytes | INT8: 1 byte | INT4: 0.5 bytes
KV Cache = 2 × layers × num_kv_heads × head_dim × context_len × bytes × batch_size
(GQA-aware: uses KV heads, not Q heads)
Overhead = 15% of weights (activations + fragmentation) + 0.5 GB (NVIDIA only)Performance estimates (tokens/sec, time to first token) are based on hardware-class lookup tables. They show ranges, not exact numbers. Actual performance depends on drivers, software stack, and workload.
License
Copyright © 2026 Tamebi. All rights reserved. Proprietary and confidential.