Aaryan K Q3_0 119B Parameters

VRAM Requirements for
Mistral-Small-4-119B-2603-GGUF Q3_0

To run Mistral-Small-4-119B-2603-GGUF locally at Q3_0 quantization, you need at minimum 56.01 GB of GPU VRAM.

56.01 GB
Required VRAM
52.06 GB
File Size
8K tokens
Context Window
119B
Parameters
Estimated VRAM Required
56.01
GB
Data Centre Class
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 8.0 GB short
Budget $3,200 – $4,000

2× RTX 4090 (48GB) + aggressive quant

48 GB

Use a lower quantization to fit. Viable for testing at this scale.

Balanced $15,000 – $22,000

NVIDIA A100 80GB PCIe

80 GB HBM2e

Single-card 80GB. Industry-standard for large model inference.

Ultimate $25,000 – $40,000

NVIDIA H100 80GB SXM5

80 GB HBM3

State-of-the-art inference. 3× the bandwidth of A100.

📊 VRAM Calculation Breakdown

Model File Size (Q3_0) 52.06 GB
Context Overhead (8,192 tokens × 119B × 2 ÷ 1M) 1.95 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 56.01 GB

Try a Different Quantization

Use the interactive calculator to compare Mistral-Small-4-119B-2603-GGUF across all available formats.

Open Live Calculator →

Mistral-Small-4-119B-2603-GGUF — Other Quantizations

Q2_0 37.19 GB file
41.14 GB
Q3_0 Current
56.01 GB
Q4_0 66.94 GB file
70.89 GB
Q4_1 66.94 GB file
70.89 GB
Q5_0 81.81 GB file
85.76 GB
Q5_1 81.81 GB file
85.76 GB
Q6_0 96.69 GB file
100.64 GB
Q8_0 126.44 GB file
130.39 GB
Advertisement Zone

Frequently Asked Questions

Can I run Mistral-Small-4-119B-2603-GGUF Q3_0 on a consumer GPU?
Running Mistral-Small-4-119B-2603-GGUF Q3_0 locally requires 56.01 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 Mistral-Small-4-119B-2603-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 Q3_0 quality good enough for production?
Q3_0 is suitable for specialized use cases. Check community benchmarks for specific quality metrics.