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.
901 downloads
20 likesSpecifications
- Publisher
- latam-gpt
- Family
- Llama 3
- Parameters
- 70.6B
- Architecture
- LlamaForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2026-06-02
- License
- Llama 3.1 Community
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HuggingFace
How Much VRAM Does Llama 3.1 70B LatamGPT SFT 1.0 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q4_K_M | 4.80 | 43.3 GB | 44.0 GB | 42.33 GB | 4-bit medium quantization — most popular sweet spot |
Which GPUs Can Run Llama 3.1 70B LatamGPT SFT 1.0?
Q4_K_M · 43.3 GBLlama 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 single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Llama 3.1 70B LatamGPT SFT 1.0?
Q4_K_M · 43.3 GB11 devices with unified memory can run Llama 3.1 70B LatamGPT SFT 1.0, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (64 GB).
Runs great
— Plenty of headroom
Related Models
Derivatives (1)
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. 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)
KV Cache + Overhead ≈ 1.7 GB (at full 4K context)
VRAM usage by quantization
Q4_K_M43.3 GBQ4_K_M + full context44.0 GB- Can NVIDIA GeForce RTX 5090 run Llama 3.1 70B LatamGPT SFT 1.0?
No — Llama 3.1 70B LatamGPT SFT 1.0 requires at least 43.3 GB at Q4_K_M, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- Can I run Llama 3.1 70B LatamGPT SFT 1.0 on a Mac?
Llama 3.1 70B LatamGPT SFT 1.0 requires at least 43.3 GB at Q4_K_M, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- 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. At Q4_K_M quantization it needs 43.3 GB of VRAM. 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 ~67 tok/s on AMD Instinct MI300X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 43.3 × 0.55 = ~67 tok/s
Estimated speed at Q4_K_M (43.3 GB)
~67 tok/s~50 tok/s~42 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- 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.
- 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?
11 devices with unified memory can run Llama 3.1 70B LatamGPT SFT 1.0 at Q4_K_M (43.3 GB), including Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB), Mac Studio M2 Ultra (192 GB), Mac Studio M4 Max (128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.