Featherless Ai Quants Q3_K_M 498B Parameters

VRAM Requirements for
besimray-miner_id_2_56d9075c-cf98-498b-8ad6-84bc66fb6ee2_1729801840-GGUF Q3_K_M

To run besimray-miner_id_2_56d9075c-cf98-498b-8ad6-84bc66fb6ee2_1729801840-GGUF locally at Q3_K_M quantization, you need at minimum 228.04 GB of GPU VRAM.

228.04 GB
Required VRAM
217.88 GB
File Size
8K tokens
Context Window
498B
Parameters
Estimated VRAM Required
228.04
GB
Cluster Required
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 68.0 GB short
Budget $40,000+

4× A100 40GB Cluster

160 GB

Distributed inference across 4 A100s. Minimum viable cluster.

⚠️ 68.0 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 (Q3_K_M) 217.88 GB
Context Overhead (8,192 tokens × 498B × 2 ÷ 1M) 8.159 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 228.04 GB

Try a Different Quantization

Use the interactive calculator to compare besimray-miner_id_2_56d9075c-cf98-498b-8ad6-84bc66fb6ee2_1729801840-GGUF across all available formats.

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besimray-miner_id_2_56d9075c-cf98-498b-8ad6-84bc66fb6ee2_1729801840-GGUF — Other Quantizations

Q2_K 155.63 GB file
165.79 GB
Q3_K_L 217.88 GB file
228.04 GB
Q3_K_M Current
228.04 GB
Q3_K_S 217.88 GB file
228.04 GB
Q4_K_M 280.13 GB file
290.29 GB
Q4_K_S 280.13 GB file
290.29 GB
Q4_XS 280.13 GB file
290.29 GB
Q5_K_M 342.38 GB file
352.54 GB
Q5_K_S 342.38 GB file
352.54 GB
Q6_K 404.63 GB file
414.79 GB
Q8_0 529.13 GB file
539.29 GB
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Frequently Asked Questions

Can I run besimray-miner_id_2_56d9075c-cf98-498b-8ad6-84bc66fb6ee2_1729801840-GGUF Q3_K_M on a consumer GPU?
Running besimray-miner_id_2_56d9075c-cf98-498b-8ad6-84bc66fb6ee2_1729801840-GGUF Q3_K_M locally requires 228.04 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 besimray-miner_id_2_56d9075c-cf98-498b-8ad6-84bc66fb6ee2_1729801840-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 Q3_K_M quality good enough for production?
Q3_K_M is suitable for specialized use cases. Check community benchmarks for specific quality metrics.