stories15M_MOE GGUF size and VRAM requirements
ggml-org/stories15M_MOE is a compact language model with 0.04 billion parameters, built on the llama architecture. It is released under the mit license and has been downloaded 67,515 times.
To run ggml-org/stories15M_MOE locally at a 4,096-token context, its quantized versions need between 0.84 GB (GGUF, lowest quality) and 0.89 GB (F16, highest quality) of memory, weights plus KV cache and a system margin included.
For most users the best balance is F16, needing about 0.89 GB. That means ggml-org/stories15M_MOE fits entirely in the VRAM of a 6 GB GPU or larger, running fully on the GPU.
GGUF file size and memory by quantization
Compare real GGUF weight sizes, estimated KV cache and total memory for Q4, Q5, Q8 and every quantization published in this repository.
| Quant. | Bits | Quality | Weights | KV | Total | Speed~ | Verdict |
|---|---|---|---|---|---|---|---|
| GGUF | 3.6 | Fair | 0.02 GB | 0.03 GB | 0.84 GB | 26245.0 t/s | Fits in VRAM |
| Q8_0 | 8.67 | Excellent | 0.04 GB | 0.03 GB | 0.86 GB | 10903.6 t/s | Fits in VRAM |
| F16 | 16.16 | Excellent | 0.07 GB | 0.03 GB | 0.89 GB | 5846.2 t/s | Fits in VRAM |
KV cache computed from the model's exact architecture. Speed is a rough estimate bounded by memory bandwidth.
Frequently asked questions
How much VRAM do you need to run ggml-org/stories15M_MOE?
You need about 0.89 GB of VRAM to run ggml-org/stories15M_MOE entirely on the GPU using the F16 quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run ggml-org/stories15M_MOE on an 8 GB GPU?
Yes. With 8 GB of VRAM you can run ggml-org/stories15M_MOE fully on the GPU using F16 (about 0.89 GB).
Can I run ggml-org/stories15M_MOE on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run ggml-org/stories15M_MOE fully on the GPU using F16 (about 0.89 GB).
Can I run ggml-org/stories15M_MOE on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run ggml-org/stories15M_MOE fully on the GPU using F16 (about 0.89 GB).
What is the best quantization for ggml-org/stories15M_MOE?
If memory allows, higher bits-per-weight means better quality. A common sweet spot is a Q4_K_M or Q5_K_M quantization, which keeps most of the quality while roughly halving the memory versus 8-bit. Pick the highest quantization that still fits in your VRAM.