tamebi 1.1.3

Detect hardware and estimate LLM model inference capability

tamebi

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 tamebi

or with uv:

uv pip install tamebi

NVIDIA, AMD, and Apple Silicon are all detected automatically — no extra flags or extras needed.

Quick Start

tamebi check

CLI Reference

tamebi check

Detect hardware and show what's runnable. Output has three sections:

1. Hardware — CPU, RAM, GPU, disk, and available inference memory
2. Top Recommendations — the best 3 models for your machine with Ollama run commands
3. 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 with this — 4K vs 128K changes memory dramatically
--batch-size	-b	1	Concurrent requests. Each gets its own KV cache. Set >1 if planning to serve multiple users
--verbose		false	Show detailed detection info (driver versions, etc.)

tamebi models

Show 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 update

Pull the latest model catalog from the remote. The catalog updates automatically in the background but you can force a refresh with this command.

tamebi update

Examples

# 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 update

Supported 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 — 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 (c) 2026 Tamebi. All rights reserved. Proprietary and confidential.