Sumgai Q5_0 36B Parameters

VRAM Requirements for
Mistral-Magistral-Devstral-Instruct-FUSED-CODER-Reasoning-36B-Q5_K_S-GGUF Q5_0

To run Mistral-Magistral-Devstral-Instruct-FUSED-CODER-Reasoning-36B-Q5_K_S-GGUF locally at Q5_0 quantization, you need at minimum 27.34 GB of GPU VRAM.

27.34 GB
Required VRAM
24.75 GB
File Size
8K tokens
Context Window
36B
Parameters
Estimated VRAM Required
27.34
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.

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 (Q5_0) 24.75 GB
Context Overhead (8,192 tokens × 36B × 2 ÷ 1M) 0.59 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 27.34 GB

Try a Different Quantization

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Mistral-Magistral-Devstral-Instruct-FUSED-CODER-Reasoning-36B-Q5_K_S-GGUF — Other Quantizations

Q5_0 Current
27.34 GB
FP16 72 GB file
74.59 GB
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Frequently Asked Questions

Can I run Mistral-Magistral-Devstral-Instruct-FUSED-CODER-Reasoning-36B-Q5_K_S-GGUF Q5_0 on a consumer GPU?
Running Mistral-Magistral-Devstral-Instruct-FUSED-CODER-Reasoning-36B-Q5_K_S-GGUF Q5_0 locally requires 27.34 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 Mistral-Magistral-Devstral-Instruct-FUSED-CODER-Reasoning-36B-Q5_K_S-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_0 quality good enough for production?
Q5_0 is suitable for specialized use cases. Check community benchmarks for specific quality metrics.