Dev Quasar 8 Q8_0 20241022B Parameters

VRAM Requirements for
mlfoundations-dev.oh-dcft-v3.1-claude-3-5-haiku-20241022-GGUF Q8_0

To run mlfoundations-dev.oh-dcft-v3.1-claude-3-5-haiku-20241022-GGUF locally at Q8_0 quantization, you need at minimum 21837716.78 GB of GPU VRAM.

21837716.78 GB
Required VRAM
21506085.88 GB
File Size
8K tokens
Context Window
20241022B
Parameters
Estimated VRAM Required
21837716.78
GB
Cluster Required
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 21,837,556.8 GB short
Budget $40,000+

4× A100 40GB Cluster

160 GB

Distributed inference across 4 A100s. Minimum viable cluster.

⚠️ 21,837,556.8 GB short
Balanced $60,000+

2× H100 80GB NVLink

160 GB HBM3

NVLink bridge enables unified 160GB VRAM pool.

⚠️ 21,836,316.8 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) 21506085.88 GB
Context Overhead (8,192 tokens × 20241022B × 2 ÷ 1M) 331628.904 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 21837716.78 GB

Try a Different Quantization

Use the interactive calculator to compare mlfoundations-dev.oh-dcft-v3.1-claude-3-5-haiku-20241022-GGUF across all available formats.

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mlfoundations-dev.oh-dcft-v3.1-claude-3-5-haiku-20241022-GGUF — Other Quantizations

Q2_K 6325319.38 GB file
6656950.28 GB
Q3_K_L 8855447.13 GB file
9187078.03 GB
Q3_K_M 8855447.13 GB file
9187078.03 GB
Q3_K_S 8855447.13 GB file
9187078.03 GB
Q4_K_M 11385574.88 GB file
11717205.78 GB
Q4_K_S 11385574.88 GB file
11717205.78 GB
Q5_K_M 13915702.63 GB file
14247333.53 GB
Q5_K_S 13915702.63 GB file
14247333.53 GB
Q6_K 16445830.38 GB file
16777461.28 GB
Q8_0 Current
21837716.78 GB
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Frequently Asked Questions

Can I run mlfoundations-dev.oh-dcft-v3.1-claude-3-5-haiku-20241022-GGUF Q8_0 on a consumer GPU?
Running mlfoundations-dev.oh-dcft-v3.1-claude-3-5-haiku-20241022-GGUF Q8_0 locally requires 21837716.78 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 mlfoundations-dev.oh-dcft-v3.1-claude-3-5-haiku-20241022-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.