Roleplaiapp Q4_K_M 671B Parameters

VRAM Requirements for
q-2.5-deepseek-r1-veltha-v0.3-Q4_K_M-GGUF Q4_K_M

To run q-2.5-deepseek-r1-veltha-v0.3-Q4_K_M-GGUF locally at Q4_K_M quantization, you need at minimum 390.43 GB of GPU VRAM.

390.43 GB
Required VRAM
377.44 GB
File Size
8K tokens
Context Window
671B
Parameters
Estimated VRAM Required
390.43
GB
Cluster Required
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 230.4 GB short
Budget $40,000+

4× A100 40GB Cluster

160 GB

Distributed inference across 4 A100s. Minimum viable cluster.

⚠️ 230.4 GB short
Balanced $60,000+

2× H100 80GB NVLink

160 GB HBM3

NVLink bridge enables unified 160GB VRAM pool.

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 (Q4_K_M) 377.44 GB
Context Overhead (8,192 tokens × 671B × 2 ÷ 1M) 10.994 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 390.43 GB

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q-2.5-deepseek-r1-veltha-v0.3-Q4_K_M-GGUF — Other Quantizations

Q4_K_M Current
390.43 GB
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Frequently Asked Questions

Can I run q-2.5-deepseek-r1-veltha-v0.3-Q4_K_M-GGUF Q4_K_M on a consumer GPU?
Running q-2.5-deepseek-r1-veltha-v0.3-Q4_K_M-GGUF Q4_K_M locally requires 390.43 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 q-2.5-deepseek-r1-veltha-v0.3-Q4_K_M-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_K_M 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.