Aaryan K Q5_1 119B Parameters

VRAM Requirements for
Mistral-Small-4-119B-2603-GGUF Q5_1

To run Mistral-Small-4-119B-2603-GGUF locally at Q5_1 quantization, you need at minimum 85.76 GB of GPU VRAM.

85.76 GB
Required VRAM
81.81 GB
File Size
8K tokens
Context Window
119B
Parameters
Estimated VRAM Required
85.76
GB
Cluster Required
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

Budget $40,000+

4× A100 40GB Cluster

160 GB

Distributed inference across 4 A100s. Minimum viable cluster.

Balanced $60,000+

2× H100 80GB NVLink

160 GB HBM3

NVLink bridge enables unified 160GB VRAM pool.

Ultimate $3,000,000+

NVIDIA GB200 NVL72

1.4 TB HBM3e

Next-gen Grace Blackwell Superchip. Built for frontier models.

📊 VRAM Calculation Breakdown

Model File Size (Q5_1) 81.81 GB
Context Overhead (8,192 tokens × 119B × 2 ÷ 1M) 1.95 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 85.76 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 37.19 GB file
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 Current
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 Q5_1 on a consumer GPU?
Running Mistral-Small-4-119B-2603-GGUF Q5_1 locally requires 85.76 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 Q5_1 quality good enough for production?
Q5_1 is suitable for specialized use cases. Check community benchmarks for specific quality metrics.