Featherless Ai Quants Q8_0 2000B Parameters

VRAM Requirements for
calliehsu-tiny-llama-shuttle-xpc-cube-en2000-GGUF Q8_0

To run calliehsu-tiny-llama-shuttle-xpc-cube-en2000-GGUF locally at Q8_0 quantization, you need at minimum 2159.77 GB of GPU VRAM.

2159.77 GB
Required VRAM
2125 GB
File Size
8K tokens
Context Window
2000B
Parameters
Estimated VRAM Required
2159.77
GB
Cluster Required
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 1,999.8 GB short
Budget $40,000+

4× A100 40GB Cluster

160 GB

Distributed inference across 4 A100s. Minimum viable cluster.

⚠️ 1,999.8 GB short
Balanced $60,000+

2× H100 80GB NVLink

160 GB HBM3

NVLink bridge enables unified 160GB VRAM pool.

⚠️ 759.8 GB short
Ultimate $3,000,000+

NVIDIA GB200 NVL72

1.4 TB HBM3e

Next-gen Grace Blackwell Superchip. Built for frontier models.

📊 VRAM Calculation Breakdown

Model File Size (Q8_0) 2125 GB
Context Overhead (8,192 tokens × 2000B × 2 ÷ 1M) 32.768 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 2159.77 GB

Try a Different Quantization

Use the interactive calculator to compare calliehsu-tiny-llama-shuttle-xpc-cube-en2000-GGUF across all available formats.

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calliehsu-tiny-llama-shuttle-xpc-cube-en2000-GGUF — Other Quantizations

Q2_K 625 GB file
659.77 GB
Q3_K_L 875 GB file
909.77 GB
Q3_K_M 875 GB file
909.77 GB
Q3_K_S 875 GB file
909.77 GB
Q4_K_M 1125 GB file
1159.77 GB
Q4_K_S 1125 GB file
1159.77 GB
Q4_XS 1125 GB file
1159.77 GB
Q5_K_M 1375 GB file
1409.77 GB
Q5_K_S 1375 GB file
1409.77 GB
Q6_K 1625 GB file
1659.77 GB
Q8_0 Current
2159.77 GB
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Frequently Asked Questions

Can I run calliehsu-tiny-llama-shuttle-xpc-cube-en2000-GGUF Q8_0 on a consumer GPU?
Running calliehsu-tiny-llama-shuttle-xpc-cube-en2000-GGUF Q8_0 locally requires 2159.77 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 calliehsu-tiny-llama-shuttle-xpc-cube-en2000-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 Q8_0 quality good enough for production?
Q8_0 produces near-lossless quality compared to FP16. It's widely used in production deployments where quality is critical and you can afford the extra VRAM.