Gghfez FP16 123B Parameters

VRAM Requirements for
Devstral-2-123B-Instruct-2512-control-vectors FP16

To run Devstral-2-123B-Instruct-2512-control-vectors locally at FP16 quantization, you need at minimum 250.02 GB of GPU VRAM.

250.02 GB
Required VRAM
246 GB
File Size
8K tokens
Context Window
123B
Parameters
Estimated VRAM Required
250.02
GB
Cluster Required
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 90.0 GB short
Budget $40,000+

4× A100 40GB Cluster

160 GB

Distributed inference across 4 A100s. Minimum viable cluster.

⚠️ 90.0 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 (FP16) 246 GB
Context Overhead (8,192 tokens × 123B × 2 ÷ 1M) 2.015 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 250.02 GB

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Devstral-2-123B-Instruct-2512-control-vectors — Other Quantizations

FP16 Current
250.02 GB
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Frequently Asked Questions

Can I run Devstral-2-123B-Instruct-2512-control-vectors FP16 on a consumer GPU?
Running Devstral-2-123B-Instruct-2512-control-vectors FP16 locally requires 250.02 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 Devstral-2-123B-Instruct-2512-control-vectors?
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 FP16 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.