Llama 3.1 70B LatamGPT SFT

Overview

LatamGPT (CENIA) fine-tuned Llama 3.1 70B via supervised fine-tuning for Latin American Spanish and Portuguese, keeping the 128K context window at roughly 41GB VRAM in Q4.

When to pick this model

• Chat applications targeting Latin American Spanish or Portuguese users
• Academic or research projects on regional language adaptation
• Multilingual deployments needing stronger LATAM dialect coverage than base Llama
• Long-context tasks (128K) in Spanish, Portuguese, or English

VRAM requirements by quantization

Quantization	VRAM required
Q4_K_M (recommended)	41 GB
Q5_K_M	50 GB
Q8_0	76 GB
FP16 (no quantization)	142 GB

VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.

In practice, Llama 3.1 70B LatamGPT SFT spills past single consumer GPUs even at Q4_K_M (41 GB) — think dual-GPU or workstation cards. Stepping up to Q8_0 nearly doubles the footprint to 76 GB, and unquantized FP16 weights take 142 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.

Without a GPU, Llama 3.1 70B LatamGPT SFT needs roughly 92 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 1 tokens/sec on entry-level GPUs, on the order of 6 tokens/sec on a mid-range card, and up to 20 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.

What hardware do you need

The table below matches Llama 3.1 70B LatamGPT SFT to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.

GPU memory	Example cards	Best fit for Llama 3.1 70B LatamGPT SFT
8 GB	RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB	Does not fit — needs 41 GB at Q4_K_M
12 GB	RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop	Does not fit — needs 41 GB at Q4_K_M
16 GB	RTX 5080, RTX 4080 Super, Radeon RX 9070 XT	Does not fit — needs 41 GB at Q4_K_M
24 GB	RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop	Does not fit — needs 41 GB at Q4_K_M
32 GB	RTX 5090	Does not fit — needs 41 GB at Q4_K_M

Which hardware should you buy to run Llama 3.1 70B LatamGPT SFT?

To run Llama 3.1 70B LatamGPT SFT locally at Q4, you need ~41 GB of VRAM. The best value for this today is a Mac Studio M5 Max (2026) (up to 128+ GB unified memory).

Strengths

• Specialized for Latin American Spanish and Portuguese
• Built on the proven Llama 3.1 70B base
• Native 128K context
• Multilingual (English/Spanish/Portuguese)

Limitations

• No official Ollama tag; install via Hugging Face
• Llama 3.1 Community License (restrictions above 700M MAU)
• ~41GB VRAM at Q4 needs at least an RTX 3090/4090 or a 64GB+ Mac

Typical workloads

In our catalog grid, Llama 3.1 70B LatamGPT SFT is filed under LATAM Spanish/Portuguese, Multilingual Chat, Academic Research — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: multilingual workloads.

The 125k-token context window covers long chats and mid-sized documents, though very large retrieval workloads will need chunking. It ships under the Llama 3.1 Community license — commercial use is generally possible but read the specific terms before embedding it in a product.

Architecture & training

Architecture: Dense transformer · GQA · Llama 3.1 70B base · 128K context

Training: Supervised fine-tuning (SFT) on Llama 3.1 70B by the LatamGPT consortium (CENIA, Chile) for Latin American Spanish and Portuguese.

Quick start

# HuggingFace : latam-gpt/Llama-3.1-70B-LatamGPT-SFT-1.0

Or use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.

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Frequently asked questions

How much VRAM does Llama 3.1 70B LatamGPT SFT need?

At the recommended Q4_K_M quantization, Llama 3.1 70B LatamGPT SFT needs about 41 GB of VRAM. Q8_0 takes 76 GB, and unquantized FP16 weights take 142 GB.

Can Llama 3.1 70B LatamGPT SFT run without a GPU?

Yes — with roughly 92 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.

What context window does Llama 3.1 70B LatamGPT SFT support?

Llama 3.1 70B LatamGPT SFT supports a 125k-token context window (128,000 tokens).

Can I use Llama 3.1 70B LatamGPT SFT commercially?

Llama 3.1 70B LatamGPT SFT ships under the Llama 3.1 Community license. Commercial use is generally permitted subject to its terms — review the license text before shipping a product.

How fast is Llama 3.1 70B LatamGPT SFT on consumer hardware?

Our compatibility engine estimates on the order of 6 tokens/sec on a mid-range GPU and up to 20 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.

Which quantization of Llama 3.1 70B LatamGPT SFT should I download first?

Start with Q4_K_M (41 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. It does not fit a single 24 GB consumer card — plan for multi-GPU or server hardware.