Aaryan K Q2_0 119B Parameters

VRAM Requirements for
Mistral-Small-4-119B-2603-GGUF Q2_0

To run Mistral-Small-4-119B-2603-GGUF locally at Q2_0 quantization, you need at minimum 41.14 GB of GPU VRAM.

41.14 GB
Required VRAM
37.19 GB
File Size
8K tokens
Context Window
119B
Parameters
Estimated VRAM Required
41.14
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.

⚠️ 1.1 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_0) 37.19 GB
Context Overhead (8,192 tokens × 119B × 2 ÷ 1M) 1.95 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 41.14 GB

Try a Different Quantization

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

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

Q2_0 Current
41.14 GB
Q3_0 52.06 GB file
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
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Frequently Asked Questions

Can I run Mistral-Small-4-119B-2603-GGUF Q2_0 on a consumer GPU?
Running Mistral-Small-4-119B-2603-GGUF Q2_0 locally requires 41.14 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 Q2_0 quality good enough for production?
Q2_0 is suitable for specialized use cases. Check community benchmarks for specific quality metrics.