Dranger003 Q5_0 33B Parameters

VRAM Requirements for
deepseek-coder-33b-instruct-iMat.GGUF Q5_0

To run deepseek-coder-33b-instruct-iMat.GGUF locally at Q5_0 quantization, you need at minimum 25.23 GB of GPU VRAM.

25.23 GB
Required VRAM
22.69 GB
File Size
8K tokens
Context Window
33B
Parameters
Estimated VRAM Required
25.23
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) 22.69 GB
Context Overhead (8,192 tokens × 33B × 2 ÷ 1M) 0.541 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 25.23 GB

Try a Different Quantization

Use the interactive calculator to compare deepseek-coder-33b-instruct-iMat.GGUF across all available formats.

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deepseek-coder-33b-instruct-iMat.GGUF — Other Quantizations

Q2_0 10.31 GB file
12.85 GB
Q3_0 14.44 GB file
16.98 GB
Q4_0 18.56 GB file
21.1 GB
Q5_0 Current
25.23 GB
Q1_0 33 GB file
35.54 GB
FP16 66 GB file
68.54 GB
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Frequently Asked Questions

Can I run deepseek-coder-33b-instruct-iMat.GGUF Q5_0 on a consumer GPU?
Running deepseek-coder-33b-instruct-iMat.GGUF Q5_0 locally requires 25.23 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 deepseek-coder-33b-instruct-iMat.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.