Vsevolodl Q4_0 70B Parameters

VRAM Requirements for
Llama-3-70B-Instruct-Gradient-1048k-GGUF Q4_0

To run Llama-3-70B-Instruct-Gradient-1048k-GGUF locally at Q4_0 quantization, you need at minimum 42.53 GB of GPU VRAM.

42.53 GB
Required VRAM
39.38 GB
File Size
8K tokens
Context Window
70B
Parameters
Estimated VRAM Required
42.53
GB
Prosumer / Workstation
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

Budget $1,200 – $1,800

2× RTX 3090 (48GB total)

48 GB

Dual-GPU via tensor parallelism. Best cost per GB at this tier.

Balanced $4,000 – $5,500

NVIDIA A6000 (48GB)

48 GB

Single-card 48GB pro GPU. Clean setup, no multi-GPU overhead.

⚠️ 2.5 GB short
Ultimate $8,000 – $12,000

NVIDIA A100 40GB SXM

40 GB HBM2e

Data-centre HBM2e bandwidth. Dramatically faster throughput.

📊 VRAM Calculation Breakdown

Model File Size (Q4_0) 39.38 GB
Context Overhead (8,192 tokens × 70B × 2 ÷ 1M) 1.147 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 42.53 GB

Try a Different Quantization

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

Open Live Calculator →

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 Current
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 48.13 GB file
51.28 GB
Advertisement Zone

Frequently Asked Questions

Can I run Llama-3-70B-Instruct-Gradient-1048k-GGUF Q4_0 on a consumer GPU?
Running Llama-3-70B-Instruct-Gradient-1048k-GGUF Q4_0 locally requires 42.53 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 Q4_0 quality good enough for production?
Q4_K_M is an excellent balance of quality and performance. Perplexity tests show minimal degradation (< 2%) vs FP16 for most models. Suitable for most production applications.