latam-gptLlama 3LlamaForCausalLM

Llama 3.1 70B LatamGPT SFT 1.0 — Hardware Requirements & GPU Compatibility

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Llama 3.1 70B LatamGPT SFT 1.0 is a 70.6B-parameter open language model from latam-gpt in the Llama 3 family. It supports a context window of up to 4,096 tokens. At Q4_K_M it needs about 43.30 GB of VRAM — see which GPUs and Macs can run it below.

70.6B params · dense4K contextLlama 3.1 CommunityMay 202684.5K downloads55 likes236 quant downloads

Based on Llama 3.1 70B

Your hardware

Verdicts below are for the reference RTX 4090. Set yours to personalise the page.

Detection reads your browser's GPU name with no data sent anywhere.

Doesn't fit a 24 GB GPU

Smallest consumer fit → Snapdragon X Elite Copilot+ PC · IQ2_M · 25.5 GB · ~4 tok/s

Macs from 32 GB (IQ2_M · ~11 tok/s)

Llama 3.1 Community

Not set up for it?

Reference GPU · RTX 4090 24 GB

Estimates, ±20% · how we estimate

Run Llama 3.1 70B LatamGPT SFT 1.0

One recommended way. Every other method, OS and quant is in the step-by-step guide.

Recommended

Ollama

one command · needs Ollama installed

$ollama run hf.co/jairo/Llama-3.1-70B-LatamGPT-SFT-1.0-GGUF:Q4_K_M

Q4_K_M needs offload on the RTX 4090 · 20.0 GB short · 4K context

Download: latamgpt-Q4_K_M.gguf42.52 GBjairo

install, OS, other quants

Other ways

each opens step-by-step

Run in cloud

Fits on A100 80GB (36 GB headroom) · Q4_K_M

Generation speed: ~31 tok/s; generation speed
Cost per 1M output tokens: $10.44; per 1M output tokens

Deployon Vast.aiBest optionfrom $1.15/hr

Compare GPUs →

or

How Much VRAM Does Llama 3.1 70B LatamGPT SFT 1.0 Need?

Select a quantization level; the listed hardware adheres to it.

Quantization	VRAM	+32K	+128K	File	Quality	On RTX 4090 (reference)
	24.8 GB	—	—	24.1 GB	2-bit · i-quant medium — Importance-weighted 2-bit, medium	Does not fit: needs 24.8 GB
	31.0 GB	—	—	~30 GB	2-bit · K-quant — 2-bit quantization with K-quant improvements	Does not fit: needs 31 GB
	32.7 GB	—	—	31.9 GB	3-bit · i-quant medium — Importance-weighted 3-bit, medium	Does not fit: needs 32.7 GB
	35.4 GB	—	—	34.3 GB	3-bit medium — 3-bit medium quantization	Does not fit: needs 35.4 GB
	38.9 GB	—	—	37.9 GB	4-bit · i-quant compact — Importance-weighted 4-bit, compact	Does not fit: needs 38.9 GB
	40.7 GB	—	—	40.3 GB	4-bit small — 4-bit small quantization	Does not fit: needs 40.7 GB
	40.7 GB	—	—	40.1 GB	4-bit · i-quant non-linear — Importance-weighted 4-bit, non-linear	Does not fit: needs 40.7 GB
	43.3 GB	—	—	42.5 GB	4-bit medium — 4-bit medium quantization — most popular sweet spot	Does not fit: needs 43.3 GB
	49.5 GB	—	—	48.7 GB	5-bit small — 5-bit small quantization	Does not fit: needs 49.5 GB
	51.2 GB	—	—	49.9 GB	5-bit medium — 5-bit medium quantization — good quality/size tradeoff	Does not fit: needs 51.2 GB
	59.2 GB	—	—	57.9 GB	6-bit, very good — 6-bit quantization, very good quality	Does not fit: needs 59.2 GB
	71.5 GB	—	—	75 GB	8-bit, near-lossless — 8-bit quantization, near-lossless	Does not fit: needs 71.5 GB
	142.1 GB	—	—	~141.1 GB	16-bit · bfloat — Brain floating point 16 — preferred for training	Does not fit: needs 142.1 GB

est.calculated, no published file yet

Selected Q4_K_M

4-bit medium quantization

44.0GBat 4K context

Weights: 42.3 GB
KV cache: 1.3 GB
Overhead: 0.3 GB

Doesn't fit RTX 4090 (reference) · needs 20.0 GB more

≥10 tok/s needs ≥663 GB/s · M3 Ultra ~13, M2 Ultra ~13

Or rent A100 80GB from $1.15/hr.

Which Hardware Can Run Llama 3.1 70B LatamGPT SFT 1.0?

Q4_K_M · 43.3 GB

Llama 3.1 70B LatamGPT SFT 1.0 (Q4_K_M) requires 43.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 57+ GB is recommended. Using the full 4K context window can add up to 0.7 GB, bringing total usage to 44.0 GB. No consumer GPU has enough memory. 67 devices with unified memory can run Llama 3.1 70B LatamGPT SFT 1.0, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Pro M2 Ultra (64 GB).

By VRAM tier · typical GPUs and Macs at Q4_K_M

Typical hardwareQuantVRAMtok/sContext that fits

8–16 GB · RTX 4060, RTX 3060 12GB, RTX 5060 Ti 16GBDoesn't fit — smallest quant needs 25.5 GB8 GB tier →12 GB tier →16 GB tier →

24 GB · RTX 4090referenceDoesn't fit — smallest quant needs 25.5 GB24 GB tier →

Mac 32 GB · Mac Mini M4IQ2_M · 25.5 GB · ~3 tok/s · 4K25.5~34KMacs →

Mac 64 GB · Mac Studio M4 MaxQ4_K_M · 44 GB · ~9 tok/s · 4K44~94KMacs →

Alternatives to Llama 3.1 70B LatamGPT SFT 1.0

Llama 3 family · 64 models →

Similar models

6tok/s on RTX 4090

Model	Score	VRAM	tok/s	Why it is similar
NVIDIA Nemotron 3.5 Bolt 74B A9B BF16NVIDIA	—	54.2 GB	—needs > 24 GB	same size
Qwen2.5 VL 72B InstructAlibaba	—	49.7 GB	—needs > 24 GB	same size
Qwen2 VL 72B InstructAlibaba	—	49.7 GB	—needs > 24 GB	same size
Qwen1.5 72B ChatAlibaba	—	86.6 GB	—needs > 24 GB	same size
CodeLlama 70B Instruct HFMeta	—	23.2 GB	~29	same size
StableBeluga2Stability AI	—	23.5 GB	~29	same size

Specifications

Publisher: latam-gpt
Family: Llama 3
Parameters: 70.6B
Architecture: LlamaForCausalLM
Context Length: 4,096 tokens
Vocabulary Size: 128,256
Release Date: 2026-05-29
License: Llama 3.1 Community

Resources

Frequently Asked Questions

How much VRAM does Llama 3.1 70B LatamGPT SFT 1.0 need?

Llama 3.1 70B LatamGPT SFT 1.0 requires 43.3 GB of VRAM at Q4_K_M, or 142.1 GB at BF16. Full 4K context adds up to 0.7 GB (44.0 GB total).

VRAM = Weights + KV Cache + Overhead

Weights = 70.6B × 4.8 bits ÷ 8 = 42.3 GB

KV Cache + Overhead ≈ 1 GB (at 2K context + ~0.3 GB framework)

Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.

KV Cache + Overhead ≈ 1.7 GB (at full 4K context)

VRAM usage by quantization

Q4_K_M

43.3 GB

Q4_K_M + full context

44.0 GB

Learn more about VRAM estimation →

Can NVIDIA GeForce RTX 5090 run Llama 3.1 70B LatamGPT SFT 1.0?

Yes, at Q2_K (31.0 GB) or lower. Higher quantizations like IQ3_M (32.7 GB) exceed the NVIDIA GeForce RTX 5090's 32 GB.

What's the best quantization for Llama 3.1 70B LatamGPT SFT 1.0?

For Llama 3.1 70B LatamGPT SFT 1.0, Q4_K_M (43.3 GB) offers the best balance of quality and VRAM usage. Q5_K_S (49.5 GB) provides better quality if you have the VRAM. The smallest option is IQ2_M at 24.8 GB.

VRAM requirement by quantization

IQ2_M

24.8 GB~62%

Q3_K_M

35.4 GB~83%

IQ4_NL

40.7 GB~88%

Q4_K_M ★

43.3 GB~89%

Q5_K_M

51.2 GB~92%

BF16

142.1 GB~100%

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run Llama 3.1 70B LatamGPT SFT 1.0 on a Mac?

Yes — Mac Studio M1 Max (32 GB) and 84 other Macs can run Llama 3.1 70B LatamGPT SFT 1.0, the smallest at IQ2_M. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 43.3 GB of usable unified memory (RAM minus macOS overhead), from Mac Studio M5 Max (48 GB).

Can I run Llama 3.1 70B LatamGPT SFT 1.0 locally?

Yes — Llama 3.1 70B LatamGPT SFT 1.0 can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is Llama 3.1 70B LatamGPT SFT 1.0?

At Q4_K_M, Llama 3.1 70B LatamGPT SFT 1.0 can reach ~111 tok/s on AMD Instinct MI350X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.

tok/s = (bandwidth GB/s ÷ model GB) × efficiency

Example: NVIDIA B200 → 8000 ÷ 43.3 × 0.65 = ~120 tok/s

Estimated speed at Q4_K_M (43.3 GB)

NVIDIA B200

~120 tok/s

NVIDIA B300

~120 tok/s

AMD Instinct MI350X

~111 tok/s

Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.

Learn more about tok/s estimation →

What's the download size of Llama 3.1 70B LatamGPT SFT 1.0?

At Q4_K_M, the download is about 42.33 GB. The full-precision BF16 version is 141.11 GB. The smallest option (IQ2_M) is 23.81 GB.

Which GPUs can run Llama 3.1 70B LatamGPT SFT 1.0?

No single consumer GPU has enough VRAM to run Llama 3.1 70B LatamGPT SFT 1.0 at Q4_K_M (43.3 GB). Multi-GPU or professional hardware is required.

Which devices can run Llama 3.1 70B LatamGPT SFT 1.0?

67 devices with unified memory can run Llama 3.1 70B LatamGPT SFT 1.0 at Q4_K_M (43.3 GB), including ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB), GMKtec EVO-X2 (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.

Run Llama 3.1 70B LatamGPT SFT 1.0

44.0 GB VRAM at 4K context · needs offload on the RTX 4090 (20.0 GB short)

Works on macOS, Windows and Linux.

Install Ollama (Linux)
$curl -fsSL https://ollama.com/install.sh | sh
Install Ollama (macOS / Windows)
Download and run the app; it installs the ollama command.
Download Ollama ↗
Run Q4_K_M
$ollama run hf.co/jairo/Llama-3.1-70B-LatamGPT-SFT-1.0-GGUF:Q4_K_M
Raise the context window
Type it inside the chat. For the API server instead, start it with OLLAMA_CONTEXT_LENGTH=4096 ollama serve.
$/set parameter num_ctx 4096
A larger context needs more memory than the weights alone.
Use the API
Ollama serves its API on http://localhost:11434.

Works on macOS, Windows and Linux.

Install LM Studio
Desktop app for macOS (Apple silicon), Windows and Linux.
lmstudio.ai ↗
Download the model
In the app press Ctrl/Cmd + Shift + M and paste https://huggingface.co/jairo/Llama-3.1-70B-LatamGPT-SFT-1.0-GGUF, then pick Q4_K_M. Or use the CLI (run LM Studio once first):
$lms get https://huggingface.co/jairo/Llama-3.1-70B-LatamGPT-SFT-1.0-GGUF@Q4_K_M
Open on Hugging Face ↗
Load and chat
Open the model loader, select the model and set Context Length to 4096. Leave GPU offload at maximum if it fits.
Start the local server (optional)
$lms server start

Works on macOS, Windows and Linux.

Install llama.cpp (macOS)
MacPorts, Nix and conda-forge also work.
$brew install llama.cpp
Install options ↗
Install llama.cpp (Windows)
$winget install llama.cpp
Install llama.cpp (Linux)
Download the prebuilt llama-*-bin-ubuntu-* archive for your GPU backend (CPU, Vulkan) from the releases page, unpack it and run the binaries from that folder. Homebrew (brew install llama.cpp) and conda-forge also work on Linux.
llama.cpp releases ↗
Start the server
Downloads the model on first run. The chat UI and an OpenAI-compatible API are at http://localhost:8080.
$llama-server -hf jairo/Llama-3.1-70B-LatamGPT-SFT-1.0-GGUF:Q4_K_M -c 4096 -ngl 99 --port 8080

Works on macOS, Windows and Linux.

Option A: Docker Model Runner
Docker Desktop: Settings > AI > Enable Docker Model Runner. Docker Engine on Linux: install the docker-model-plugin package.
$docker model run hf.co/jairo/Llama-3.1-70B-LatamGPT-SFT-1.0-GGUF:Q4_K_M
Model Runner docs ↗
The quant tag after the repo is how Hugging Face cards write it; if your Docker version rejects it, drop the tag and pick the quant by repo.
Option B: llama.cpp server image (NVIDIA)
Needs the NVIDIA Container Toolkit. Models are cached in the llama-cache volume. Open http://localhost:8080.
$docker run --gpus all -p 8080:8080 -v llama-cache:/root/.cache ghcr.io/ggml-org/llama.cpp:server-cuda -hf jairo/Llama-3.1-70B-LatamGPT-SFT-1.0-GGUF:Q4_K_M -c 4096 -ngl 99 --host 0.0.0.0 --port 8080
Image tags (cuda13, vulkan, rocm) ↗
Cache path inside the image is not in the docs; remove the -v flag if the volume complains.

Linux only.

Install vLLM
Linux only; on Windows run it inside WSL2. NVIDIA GPU shown; AMD ROCm, Intel and CPU builds have their own install pages.
$uv venv --python 3.12 --seed
$source .venv/bin/activate
$uv pip install vllm --torch-backend=auto
vLLM installation ↗
Serve the model
OpenAI-compatible API at http://localhost:8000/v1.
$vllm serve latam-gpt/Llama-3.1-70B-LatamGPT-SFT-1.0 --max-model-len 4096
Runs the original weights, not the Q4_K_M GGUF; needs ~155 GB of GPU memory.

Works on macOS, Windows and Linux.

Install Jan
jan.ai ↗
Find the model
Open the Hub tab, paste this model ID in Search, and choose Q4_K_M if offered.
$jairo/Llama-3.1-70B-LatamGPT-SFT-1.0-GGUF
Use the local API (optional)
Settings > Local API Server > Start Server. It listens on http://127.0.0.1:1337.

Rent GPUs hourly to run Q4_K_M right there. Pricing comes from partner marketplaces and varies.

Available GPU rentals for Q4_K_M, prioritized by value
GPU	Speed	$/1M tokens	Rent
A100 80GBCheapestCheapest36 GB free	~31 tok/s	$10.44	Deploy on Vast.aifrom $1.15/hr (opens in new tab)	36 GB free · ~31 tok/s · $10.44 / 1M tokens
RTX PRO 6000 Max-Q (96 GB)52 GB free	~27 tok/s	$14.27	Deploy on Vast.aifrom $1.38/hr (opens in new tab)	96 GB · 52 GB free · ~27 tok/s · $14.27 / 1M tokens
A100 80GB36 GB free	~31 tok/s	$12.62	Deploy on RunPodfrom $1.39/hr (opens in new tab)	36 GB free · ~31 tok/s · $12.62 / 1M tokens
RTX PRO 6000 (96 GB)52 GB free	~27 tok/s	$17.45	Deploy on RunPodfrom $1.69/hr (opens in new tab)	96 GB · 52 GB free · ~27 tok/s · $17.45 / 1M tokens
RTX PRO 6000 Server (96 GB)52 GB free	~24 tok/s	$19.56	Deploy on RunPodfrom $1.69/hr (opens in new tab)	96 GB · 52 GB free · ~24 tok/s · $19.56 / 1M tokens
H100 (80 GB)36 GB free	~50 tok/s	$14.86	Deploy on RunPodfrom $2.69/hr (opens in new tab)	80 GB · 36 GB free · ~50 tok/s · $14.86 / 1M tokens
H200 (141 GB)97 GB free	~72 tok/s	$15.57	Deploy on Vast.aifrom $4.04/hr (opens in new tab)	141 GB · 97 GB free · ~72 tok/s · $15.57 / 1M tokens

Compare GPUs →