Bartowski Q8_0 25B Parameters

VRAM Requirements for
cerebras_Qwen3-Coder-REAP-25B-A3B-GGUF Q8_0

To run cerebras_Qwen3-Coder-REAP-25B-A3B-GGUF locally at Q8_0 quantization, you need at minimum 28.97 GB of GPU VRAM.

28.97 GB
Required VRAM
26.56 GB
File Size
8K tokens
Context Window
25B
Parameters
Estimated VRAM Required
28.97
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 (Q8_0) 26.56 GB
Context Overhead (8,192 tokens × 25B × 2 ÷ 1M) 0.41 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 28.97 GB

Try a Different Quantization

Use the interactive calculator to compare cerebras_Qwen3-Coder-REAP-25B-A3B-GGUF across all available formats.

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cerebras_Qwen3-Coder-REAP-25B-A3B-GGUF — Other Quantizations

Q2_K 7.81 GB file
10.22 GB
Q2_K_L 7.81 GB file
10.22 GB
Q2_M 7.81 GB file
10.22 GB
Q2_S 7.81 GB file
10.22 GB
Q2_XS 7.81 GB file
10.22 GB
Q2_XXS 7.81 GB file
10.22 GB
Q3_XXS 10.94 GB file
13.35 GB
Q3_XS 10.94 GB file
13.35 GB
Q3_M 10.94 GB file
13.35 GB
Q3_K_XL 10.94 GB file
13.35 GB
Q3_K_M 10.94 GB file
13.35 GB
Q3_K_L 10.94 GB file
13.35 GB
Q3_K_S 10.94 GB file
13.35 GB
Q4_K_S 14.06 GB file
16.47 GB
Q4_XS 14.06 GB file
16.47 GB
Q4_NL 14.06 GB file
16.47 GB
Q4_K_M 14.06 GB file
16.47 GB
Q4_K_L 14.06 GB file
16.47 GB
Q4_1 14.06 GB file
16.47 GB
Q4_0 14.06 GB file
16.47 GB
Q5_K_L 17.19 GB file
19.6 GB
Q5_K_M 17.19 GB file
19.6 GB
Q5_K_S 17.19 GB file
19.6 GB
Q6_K 20.31 GB file
22.72 GB
Q6_K_L 20.31 GB file
22.72 GB
Q8_0 Current
28.97 GB
BF16 50 GB file
52.41 GB
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Frequently Asked Questions

Can I run cerebras_Qwen3-Coder-REAP-25B-A3B-GGUF Q8_0 on a consumer GPU?
Running cerebras_Qwen3-Coder-REAP-25B-A3B-GGUF Q8_0 locally requires 28.97 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 cerebras_Qwen3-Coder-REAP-25B-A3B-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.