The Bloke Q5_0 34B Parameters

VRAM Requirements for
CapyTessBorosYi-34B-200K-DARE-Ties-GGUF Q5_0

To run CapyTessBorosYi-34B-200K-DARE-Ties-GGUF locally at Q5_0 quantization, you need at minimum 25.94 GB of GPU VRAM.

25.94 GB
Required VRAM
23.38 GB
File Size
8K tokens
Context Window
34B
Parameters
Estimated VRAM Required
25.94
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) 23.38 GB
Context Overhead (8,192 tokens × 34B × 2 ÷ 1M) 0.557 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 25.94 GB

Try a Different Quantization

Use the interactive calculator to compare CapyTessBorosYi-34B-200K-DARE-Ties-GGUF across all available formats.

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CapyTessBorosYi-34B-200K-DARE-Ties-GGUF — Other Quantizations

Q2_K 10.63 GB file
13.19 GB
Q3_K_L 14.88 GB file
17.44 GB
Q3_K_M 14.88 GB file
17.44 GB
Q3_K_S 14.88 GB file
17.44 GB
Q4_0 19.13 GB file
21.69 GB
Q4_K_M 19.13 GB file
21.69 GB
Q4_K_S 19.13 GB file
21.69 GB
Q5_0 Current
25.94 GB
Q5_K_M 23.38 GB file
25.94 GB
Q5_K_S 23.38 GB file
25.94 GB
Q6_K 27.63 GB file
30.19 GB
Q8_0 36.13 GB file
38.69 GB
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Frequently Asked Questions

Can I run CapyTessBorosYi-34B-200K-DARE-Ties-GGUF Q5_0 on a consumer GPU?
Running CapyTessBorosYi-34B-200K-DARE-Ties-GGUF Q5_0 locally requires 25.94 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 CapyTessBorosYi-34B-200K-DARE-Ties-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.