Xiaoniao000 Q2_M 120B Parameters

VRAM Requirements for
huizimao_gpt-oss-120b-uncensored-bf16-GGUF Q2_M

To run huizimao_gpt-oss-120b-uncensored-bf16-GGUF locally at Q2_M quantization, you need at minimum 41.47 GB of GPU VRAM.

41.47 GB
Required VRAM
37.5 GB
File Size
8K tokens
Context Window
120B
Parameters
Estimated VRAM Required
41.47
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.

⚠️ 1.5 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 (Q2_M) 37.5 GB
Context Overhead (8,192 tokens × 120B × 2 ÷ 1M) 1.966 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 41.47 GB

Try a Different Quantization

Use the interactive calculator to compare huizimao_gpt-oss-120b-uncensored-bf16-GGUF across all available formats.

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huizimao_gpt-oss-120b-uncensored-bf16-GGUF — Other Quantizations

Q2_K 37.5 GB file
41.47 GB
Q2_K_L 37.5 GB file
41.47 GB
Q2_M Current
41.47 GB
Q3_K_L 52.5 GB file
56.47 GB
Q3_K_XL 52.5 GB file
56.47 GB
Q3_M 52.5 GB file
56.47 GB
Q4_K_L 67.5 GB file
71.47 GB
Q4_K_M 67.5 GB file
71.47 GB
Q4_NL 67.5 GB file
71.47 GB
Q6_K 97.5 GB file
101.47 GB
BF16 240 GB file
243.97 GB
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Frequently Asked Questions

Can I run huizimao_gpt-oss-120b-uncensored-bf16-GGUF Q2_M on a consumer GPU?
Running huizimao_gpt-oss-120b-uncensored-bf16-GGUF Q2_M locally requires 41.47 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 huizimao_gpt-oss-120b-uncensored-bf16-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 Q2_M quality good enough for production?
Q2_M is suitable for specialized use cases. Check community benchmarks for specific quality metrics.