Second State Q8_0 34B Parameters

VRAM Requirements for
Yi-1.5-34B-Chat-GGUF Q8_0

To run Yi-1.5-34B-Chat-GGUF locally at Q8_0 quantization, you need at minimum 38.69 GB of GPU VRAM.

38.69 GB
Required VRAM
36.13 GB
File Size
8K tokens
Context Window
34B
Parameters
Estimated VRAM Required
38.69
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) 36.13 GB
Context Overhead (8,192 tokens × 34B × 2 ÷ 1M) 0.557 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 38.69 GB

Try a Different Quantization

Use the interactive calculator to compare Yi-1.5-34B-Chat-GGUF across all available formats.

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Yi-1.5-34B-Chat-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 23.38 GB file
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 Current
38.69 GB
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Frequently Asked Questions

Can I run Yi-1.5-34B-Chat-GGUF Q8_0 on a consumer GPU?
Running Yi-1.5-34B-Chat-GGUF Q8_0 locally requires 38.69 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 Yi-1.5-34B-Chat-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.