Family Llama · 71B parameters

Llama 3.1 70B LatamGPT SFT

SFT fine-tune of Llama 3.1 70B by LatamGPT (CENIA) for Latin American Spanish and Portuguese. 128k ctx, ~41 GB VRAM in Q4.

LatamGPT (CENIA)·License Llama 3.1 Community·Context 125k tokens·Output 2026-05-29·Fits within the 128 GB of the GIGABYTE AI TOP ATOM← Catalog

01What it can do

Strengths
  • LATAM Spanish/Portuguese specialization
  • Base Llama 3.1 70B (proven quality)
  • 128k native context
  • Multilingual (en/es/pt)
Limitations to know
  • —No official Ollama tag (HF install)
  • —Llama 3.1 Community License (>700M MAU clause)
  • —~41 GB VRAM in Q4 (RTX 3090/4090 mini or Mac with 64+ GB)
Architecture
Transformer dense · GQA · base Llama 3.1 70B · 128k context
Training
SFT (supervised fine-tuning) on Llama 3.1 70B by the LatamGPT consortium (CENIA, Chile) for Latin American Spanish and Portuguese.
Ideal for
Latin American Spanish/PortugueseMultilingual chatAcademic research

04Install

Install Ollama for your OS. Check the model and its quantization before downloading. Start with 4096 tokens of context, then check placement with ollama ps. A command below is not proof that a test was run on your machine.

$# HuggingFace : latam-gpt/Llama-3.1-70B-LatamGPT-SFT-1.0
⚠
First download: between 2 and 40 GB depending on the selected quantization. Plan for sufficient disk space; a stable connection is recommended. Subsequent launches are instant.

02Required memory

Approximate GPU VRAM required to run this model, including 4k tokens of context overhead. For a longer context, add ~1 GB per 8k-token increment.

Q4_K_M
The lightest, ~5% loss
41 GB
Q5_K_M
Good quality/size compromise
50 GB
Q8_0
Nearly indistinguishable from FP16
76 GB
FP16
Full precision — server use
142 GB
Fallback CPU · If you don't have a GPU, allow 92 GB of RAM minimum to run this model at reduced speed.

What hardware do you need for Llama 3.1 70B LatamGPT SFT?

To run Llama 3.1 70B LatamGPT SFT locally with Q4 quantization, you need about 41 GB of VRAM. An option to compare: BOSGAME M5 128GB / 2TB (Ryzen AI Max+ 395) — leave some headroom for the system and context; check engine compatibility with the GPU.

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This model in your private ChatGPT, without the cloud

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03Expected speed

Tokens generated per second in Q4_K_M, 4k context. Beyond 20 t/s, reading is comfortable. Below 10 t/s, that's just for testing.

Entry-level
~1t/s
GTX 1650, RX 6600, MBA M2 8GB
Mid-range
~6t/s
RTX 4060, 4070, MBP M3 Pro
High-end
~20t/s
RTX 4090, M4 Max, Radeon 7900