Teddylee777 FP32 10.8B Parameters

VRAM Requirements for
EEVE-Korean-Instruct-10.8B-v1.0-gguf FP32

To run EEVE-Korean-Instruct-10.8B-v1.0-gguf locally at FP32 quantization, you need at minimum 45.38 GB of GPU VRAM.

45.38 GB
Required VRAM
43.2 GB
File Size
8K tokens
Context Window
10.8B
Parameters
Estimated VRAM Required
45.38
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.

⚠️ 5.4 GB short
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 (FP32) 43.2 GB
Context Overhead (8,192 tokens × 10.8B × 2 ÷ 1M) 0.177 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 45.38 GB

Try a Different Quantization

Use the interactive calculator to compare EEVE-Korean-Instruct-10.8B-v1.0-gguf across all available formats.

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EEVE-Korean-Instruct-10.8B-v1.0-gguf — Other Quantizations

Q4_0 6.08 GB file
8.26 GB
Q4_1 6.08 GB file
8.26 GB
Q4_K_M 6.08 GB file
8.26 GB
Q4_K_S 6.08 GB file
8.26 GB
Q5_0 7.43 GB file
9.61 GB
Q5_K_M 7.43 GB file
9.61 GB
Q5_K_S 7.43 GB file
9.61 GB
Q6_K 8.78 GB file
10.96 GB
Q8_0 11.48 GB file
13.66 GB
FP16 21.6 GB file
23.78 GB
FP32 Current
45.38 GB
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Frequently Asked Questions

Can I run EEVE-Korean-Instruct-10.8B-v1.0-gguf FP32 on a consumer GPU?
Running EEVE-Korean-Instruct-10.8B-v1.0-gguf FP32 locally requires 45.38 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 EEVE-Korean-Instruct-10.8B-v1.0-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 FP32 quality good enough for production?
FP32 is suitable for specialized use cases. Check community benchmarks for specific quality metrics.