Dev Quasar Q5_K_M 702B Parameters

VRAM Requirements for
ai-sage.GigaChat3-702B-A36B-preview-bf16-GGUF Q5_K_M

To run ai-sage.GigaChat3-702B-A36B-preview-bf16-GGUF locally at Q5_K_M quantization, you need at minimum 496.13 GB of GPU VRAM.

496.13 GB
Required VRAM
482.63 GB
File Size
8K tokens
Context Window
702B
Parameters
Estimated VRAM Required
496.13
GB
Cluster Required
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 336.1 GB short
Budget $40,000+

4× A100 40GB Cluster

160 GB

Distributed inference across 4 A100s. Minimum viable cluster.

⚠️ 336.1 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 (Q5_K_M) 482.63 GB
Context Overhead (8,192 tokens × 702B × 2 ÷ 1M) 11.502 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 496.13 GB

Try a Different Quantization

Use the interactive calculator to compare ai-sage.GigaChat3-702B-A36B-preview-bf16-GGUF across all available formats.

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ai-sage.GigaChat3-702B-A36B-preview-bf16-GGUF — Other Quantizations

Q2_K 219.38 GB file
232.88 GB
Q2_XS 219.38 GB file
232.88 GB
Q3_K_M 307.13 GB file
320.63 GB
Q4_K_M 394.88 GB file
408.38 GB
Q5_K_M Current
496.13 GB
Q6_K 570.38 GB file
583.88 GB
Q8_0 745.88 GB file
759.38 GB
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Frequently Asked Questions

Can I run ai-sage.GigaChat3-702B-A36B-preview-bf16-GGUF Q5_K_M on a consumer GPU?
Running ai-sage.GigaChat3-702B-A36B-preview-bf16-GGUF Q5_K_M locally requires 496.13 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 ai-sage.GigaChat3-702B-A36B-preview-bf16-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_M quality good enough for production?
Q5_K_M is suitable for specialized use cases. Check community benchmarks for specific quality metrics.