System Requirements for TimesFM

System Requirements for TimesFM

Hardware Tiers

TimesFM can run on a variety of hardware configurations. This guide helps you choose the right setup and tune performance for your machine.

Tier 1: Minimal (CPU-Only, 4–8 GB RAM)

• Use case: Light exploration, single-series forecasting, prototyping
• Model: TimesFM 2.5 (200M) only
• Batch size: per_core_batch_size=4
• Context: Limit max_context=512
• Expected speed: ~2–5 seconds per 100-point series

model.compile(timesfm.ForecastConfig(
    max_context=512,
    max_horizon=128,
    per_core_batch_size=4,
    normalize_inputs=True,
    use_continuous_quantile_head=True,
    fix_quantile_crossing=True,
))

Tier 2: Standard (CPU 16 GB or GPU 4–8 GB VRAM)

• Use case: Batch forecasting (dozens of series), evaluation, production prototypes
• Model: TimesFM 2.5 (200M)
• Batch size: per_core_batch_size=32 (CPU) or 64 (GPU)
• Context: max_context=1024
• Expected speed: ~0.5–1 second per 100-point series (GPU)

model.compile(timesfm.ForecastConfig(
    max_context=1024,
    max_horizon=256,
    per_core_batch_size=64,
    normalize_inputs=True,
    use_continuous_quantile_head=True,
    fix_quantile_crossing=True,
))

Tier 3: Production (GPU 16+ GB VRAM or Apple Silicon 32+ GB)

• Use case: Large-scale batch forecasting (thousands of series), long context
• Model: TimesFM 2.5 (200M)
• Batch size: per_core_batch_size=128–256
• Context: max_context=4096 or higher
• Expected speed: ~0.1–0.3 seconds per 100-point series

model.compile(timesfm.ForecastConfig(
    max_context=4096,
    max_horizon=256,
    per_core_batch_size=128,
    normalize_inputs=True,
    use_continuous_quantile_head=True,
    fix_quantile_crossing=True,
))

Tier 4: Legacy Models (v1.0/v2.0 — 500M parameters)

• ⚠️ WARNING: TimesFM v2.0 (500M) requires ≥ 16 GB RAM (CPU) or ≥ 8 GB VRAM (GPU)
• ⚠️ WARNING: TimesFM v1.0 legacy JAX version may require ≥ 32 GB RAM
• Recommendation: Unless you specifically need a legacy checkpoint, use TimesFM 2.5

Memory Estimation

CPU Memory (RAM)

Approximate RAM usage during inference:

Formula: RAM ≈ model_weights + 0.5 GB + (0.2 MB × num_series × context_length / 1000)

GPU Memory (VRAM)

Disk Space

Model weights are downloaded once from Hugging Face Hub and cached in ~/.cache/huggingface/ (or $HF_HOME).

GPU Selection Guide

NVIDIA GPUs (CUDA)

Apple Silicon (MPS)

CPU Only

Works on any CPU with sufficient RAM. Expect 5–20× slower than GPU.

Python and Package Requirements

Optional Dependencies

Operating System Compatibility

Troubleshooting

Out of Memory (OOM)

# Reduce batch size
model.compile(timesfm.ForecastConfig(
    per_core_batch_size=4,  # Start very small
    max_context=512,        # Reduce context
    ...
))

# Process in chunks
for i in range(0, len(inputs), 50):
    chunk = inputs[i:i+50]
    p, q = model.forecast(horizon=H, inputs=chunk)

Slow Inference on CPU

# Ensure matmul precision is set
import torch
torch.set_float32_matmul_precision("high")

# Use smaller context
model.compile(timesfm.ForecastConfig(
    max_context=256,  # Shorter context = faster
    ...
))

Model Download Fails

# Set a different cache directory
export HF_HOME=/path/with/more/space

# Or download manually
huggingface-cli download google/timesfm-2.5-200m-pytorch