Dev Quasar Q1_M 2025B Parameters

VRAM Requirements for
CohereLabs.command-a-reasoning-08-2025-GGUF Q1_M

To run CohereLabs.command-a-reasoning-08-2025-GGUF locally at Q1_M quantization, you need at minimum 2060.18 GB of GPU VRAM.

2060.18 GB
Required VRAM
2025 GB
File Size
8K tokens
Context Window
2025B
Parameters
Estimated VRAM Required
2060.18
GB
Cluster Required
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 1,900.2 GB short
Budget $40,000+

4× A100 40GB Cluster

160 GB

Distributed inference across 4 A100s. Minimum viable cluster.

⚠️ 1,900.2 GB short
Balanced $60,000+

2× H100 80GB NVLink

160 GB HBM3

NVLink bridge enables unified 160GB VRAM pool.

⚠️ 660.2 GB short
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 (Q1_M) 2025 GB
Context Overhead (8,192 tokens × 2025B × 2 ÷ 1M) 33.178 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 2060.18 GB

Try a Different Quantization

Use the interactive calculator to compare CohereLabs.command-a-reasoning-08-2025-GGUF across all available formats.

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CohereLabs.command-a-reasoning-08-2025-GGUF — Other Quantizations

Q2_K 632.81 GB file
667.99 GB
Q2_S 632.81 GB file
667.99 GB
Q2_XS 632.81 GB file
667.99 GB
Q2_XXS 632.81 GB file
667.99 GB
Q3_K_M 885.94 GB file
921.12 GB
Q4_K_M 1139.06 GB file
1174.24 GB
Q5_K_M 1392.19 GB file
1427.37 GB
Q6_K 1645.31 GB file
1680.49 GB
Q1_M Current
2060.18 GB
Q8_0 2151.56 GB file
2186.74 GB
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Frequently Asked Questions

Can I run CohereLabs.command-a-reasoning-08-2025-GGUF Q1_M on a consumer GPU?
Running CohereLabs.command-a-reasoning-08-2025-GGUF Q1_M locally requires 2060.18 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 CohereLabs.command-a-reasoning-08-2025-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 Q1_M quality good enough for production?
Q1_M is suitable for specialized use cases. Check community benchmarks for specific quality metrics.