Ymcki FP4 48B Parameters

VRAM Requirements for
Kimi-Linear-48B-A3B-Instruct-GGUF FP4

To run Kimi-Linear-48B-A3B-Instruct-GGUF locally at FP4 quantization, you need at minimum 50.79 GB of GPU VRAM.

50.79 GB
Required VRAM
48 GB
File Size
8K tokens
Context Window
48B
Parameters
Estimated VRAM Required
50.79
GB
Data Centre Class
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 2.8 GB short
Budget $3,200 – $4,000

2× RTX 4090 (48GB) + aggressive quant

48 GB

Use a lower quantization to fit. Viable for testing at this scale.

Balanced $15,000 – $22,000

NVIDIA A100 80GB PCIe

80 GB HBM2e

Single-card 80GB. Industry-standard for large model inference.

Ultimate $25,000 – $40,000

NVIDIA H100 80GB SXM5

80 GB HBM3

State-of-the-art inference. 3× the bandwidth of A100.

📊 VRAM Calculation Breakdown

Model File Size (FP4) 48 GB
Context Overhead (8,192 tokens × 48B × 2 ÷ 1M) 0.786 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 50.79 GB

Try a Different Quantization

Use the interactive calculator to compare Kimi-Linear-48B-A3B-Instruct-GGUF across all available formats.

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Kimi-Linear-48B-A3B-Instruct-GGUF — Other Quantizations

Q2_K 15 GB file
17.79 GB
Q3_M 21 GB file
23.79 GB
Q4_K_M 27 GB file
29.79 GB
Q4_XS 27 GB file
29.79 GB
Q5_K_M 33 GB file
35.79 GB
FP4 Current
50.79 GB
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Frequently Asked Questions

Can I run Kimi-Linear-48B-A3B-Instruct-GGUF FP4 on a consumer GPU?
Running Kimi-Linear-48B-A3B-Instruct-GGUF FP4 locally requires 50.79 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 Kimi-Linear-48B-A3B-Instruct-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 FP4 quality good enough for production?
FP4 is suitable for specialized use cases. Check community benchmarks for specific quality metrics.