Vsevolodl Q5_K_S 70B Parameters

VRAM Requirements for
Llama-3-70B-Instruct-Gradient-1048k-GGUF Q5_K_S

To run Llama-3-70B-Instruct-Gradient-1048k-GGUF locally at Q5_K_S quantization, you need at minimum 51.28 GB of GPU VRAM.

51.28 GB
Required VRAM
48.13 GB
File Size
8K tokens
Context Window
70B
Parameters
Estimated VRAM Required
51.28
GB
Data Centre Class
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 3.3 GB short
Budget $3,200 – $4,000

2× RTX 4090 (48GB) + aggressive quant

48 GB

Use a lower quantization to fit. Viable for testing at this scale.

Balanced $15,000 – $22,000

NVIDIA A100 80GB PCIe

80 GB HBM2e

Single-card 80GB. Industry-standard for large model inference.

Ultimate $25,000 – $40,000

NVIDIA H100 80GB SXM5

80 GB HBM3

State-of-the-art inference. 3× the bandwidth of A100.

📊 VRAM Calculation Breakdown

Model File Size (Q5_K_S) 48.13 GB
Context Overhead (8,192 tokens × 70B × 2 ÷ 1M) 1.147 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 51.28 GB

Try a Different Quantization

Use the interactive calculator to compare Llama-3-70B-Instruct-Gradient-1048k-GGUF across all available formats.

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Llama-3-70B-Instruct-Gradient-1048k-GGUF — Other Quantizations

Q2_K 21.88 GB file
25.03 GB
Q3_K_L 30.63 GB file
33.78 GB
Q3_K_M 30.63 GB file
33.78 GB
Q3_K_S 30.63 GB file
33.78 GB
Q4_0 39.38 GB file
42.53 GB
Q4_K_M 39.38 GB file
42.53 GB
Q4_K_S 39.38 GB file
42.53 GB
Q5_0 48.13 GB file
51.28 GB
Q5_K_M 48.13 GB file
51.28 GB
Q5_K_S Current
51.28 GB
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Frequently Asked Questions

Can I run Llama-3-70B-Instruct-Gradient-1048k-GGUF Q5_K_S on a consumer GPU?
Running Llama-3-70B-Instruct-Gradient-1048k-GGUF Q5_K_S locally requires 51.28 GB VRAM, which exceeds consumer GPUs. You'll need prosumer cards like the NVIDIA A6000 (48GB) or an A100 (80GB).
What happens if I don't have enough VRAM?
If your GPU VRAM is insufficient, llama.cpp and similar tools will offload model layers to system RAM (CPU inference). This is much slower — expect 10-50× the generation latency compared to full GPU inference.
Can I use multiple GPUs to run Llama-3-70B-Instruct-Gradient-1048k-GGUF?
Yes! Tools like llama.cpp, vLLM, and Ollama support tensor parallelism across multiple GPUs. For example, 2× RTX 3090 (24GB each) gives you 48GB total VRAM, which can run many large models.
Is Q5_K_S quality good enough for production?
Q5_K_S is suitable for specialized use cases. Check community benchmarks for specific quality metrics.