Alexander Kyng BF16 119B Parameters

VRAM Requirements for
Mistral-Small-119B-2603-ik-GGUF BF16

To run Mistral-Small-119B-2603-ik-GGUF locally at BF16 quantization, you need at minimum 241.95 GB of GPU VRAM.

241.95 GB
Required VRAM
238 GB
File Size
8K tokens
Context Window
119B
Parameters
Estimated VRAM Required
241.95
GB
Cluster Required
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 81.9 GB short
Budget $40,000+

4× A100 40GB Cluster

160 GB

Distributed inference across 4 A100s. Minimum viable cluster.

⚠️ 81.9 GB short
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 (BF16) 238 GB
Context Overhead (8,192 tokens × 119B × 2 ÷ 1M) 1.95 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 241.95 GB

Try a Different Quantization

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

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

Q4_K_R4 66.94 GB file
70.89 GB
Q5_K_R4 81.81 GB file
85.76 GB
BF16 Current
241.95 GB
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Frequently Asked Questions

Can I run Mistral-Small-119B-2603-ik-GGUF BF16 on a consumer GPU?
Running Mistral-Small-119B-2603-ik-GGUF BF16 locally requires 241.95 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-119B-2603-ik-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 BF16 quality good enough for production?
FP16/BF16 is the standard precision used for production inference and serves as the quality baseline. All fine-tuned models are typically served at this precision.