Ling-3.0-flash GGUF size and VRAM requirements
AtomicChat/Ling-3.0-flash-GGUF is a very large language model with 127.49 billion parameters, built on the bailingmoe3 architecture. It is released under the mit license and has been downloaded 23,512 times.
Ling-3.0-flash-GGUF is a Mixture-of-Experts model with 512 experts, of which 8 are active on each token. Routing only 8 of 512 experts makes it noticeably faster than a dense model of the same size — but every expert still has to be held in memory, so the numbers below are set by the full parameter count, not the active one. It is built from 42 transformer layers, a hidden size of 2,560, 32 attention heads. Ling-3.0-flash-GGUF uses Multi-head Latent Attention, caching a single compressed latent per layer instead of full keys and values. That keeps the KV cache small even at long context — the memory figures on this page already reflect it.
To run AtomicChat/Ling-3.0-flash-GGUF locally at a 4,096-token context, its quantized versions need between 31.15 GB (IQ1_S, lowest quality) and 232.83 GB (BF16, highest quality) of memory, weights plus KV cache and a system margin included. Context length drives that memory directly: at 4,096 tokens the KV cache for Ling-3.0-flash-GGUF is about 0.18 GB, rising to roughly 5.91 GB at its full 131,072-token context. Shorter prompts free up memory for a higher-quality quantization.
Available GGUF quantizations for AtomicChat/Ling-3.0-flash-GGUF include IQ1_S, IQ1_M, IQ2_XXS, IQ2_XS, IQ2_S, IQ2_M, IQ3_XXS, IQ3_S, IQ3_M, IQ4_XS_STOCK, IQ4_XS_FLAT, IQ4_XXS, GGUF, Q4_K_FLAT, Q4_K_S, IQ4_XS, Q4_K_M_STOCK, IQ4_NL, Q4_K_M, Q4_K_L, Q5_K_S, Q5_K_M_STOCK, Q5_K_M, Q5_K_L, Q6_K_STOCK, Q6_K, Q8_0, BF16. The model supports a native context length of up to 131,072 tokens; a longer context grows the KV cache and the memory needed.
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 |
|---|---|---|---|---|---|---|---|
| IQ1_S | 2.03 | Very low | 30.16 GB | 0.18 GB | 31.15 GB | — | Insufficient |
| IQ1_M | 2.29 | Very low | 33.97 GB | 0.18 GB | 34.95 GB | — | Insufficient |
| IQ2_XXS | 2.46 | Very low | 36.53 GB | 0.18 GB | 37.52 GB | — | Insufficient |
| IQ2_XS | 2.81 | Low | 41.66 GB | 0.18 GB | 42.64 GB | — | Insufficient |
| IQ2_S | 2.95 | Low | 43.71 GB | 0.18 GB | 44.7 GB | — | Insufficient |
| IQ2_M | 3.08 | Low | 45.73 GB | 0.18 GB | 46.71 GB | — | Insufficient |
| IQ3_XXS | 3.58 | Fair | 53.16 GB | 0.18 GB | 54.14 GB | — | Insufficient |
| IQ3_S | 3.63 | Fair | 53.86 GB | 0.18 GB | 54.84 GB | — | Insufficient |
| IQ3_M | 3.9 | Fair | 57.92 GB | 0.18 GB | 58.9 GB | — | Insufficient |
| IQ4_XS_STOCK | 4.17 | Fair | 61.85 GB | 0.18 GB | 62.83 GB | — | Insufficient |
| IQ4_XS_FLAT | 4.3 | Good | 63.85 GB | 0.18 GB | 64.84 GB | — | Insufficient |
| IQ4_XXS | 4.35 | Good | 64.59 GB | 0.18 GB | 65.57 GB | — | Insufficient |
| GGUF | 4.54 | Good | 67.37 GB | 0.18 GB | 68.35 GB | — | Insufficient |
| Q4_K_FLAT | 4.54 | Good | 67.37 GB | 0.18 GB | 68.35 GB | — | Insufficient |
| Q4_K_S | 4.66 | Good | 69.13 GB | 0.18 GB | 70.11 GB | — | Insufficient |
| IQ4_XS | 4.7 | Good | 69.71 GB | 0.18 GB | 70.7 GB | — | Insufficient |
| Q4_K_M_STOCK | 4.72 | Good | 70.11 GB | 0.18 GB | 71.09 GB | — | Insufficient |
| IQ4_NL | 4.98 | Good | 73.85 GB | 0.18 GB | 74.83 GB | — | Insufficient |
| Q4_K_M | 4.98 | Good | 73.85 GB | 0.18 GB | 74.83 GB | — | Insufficient |
| Q4_K_L | 5.27 | Very good | 78.21 GB | 0.18 GB | 79.19 GB | — | Insufficient |
| Q5_K_S | 5.49 | Very good | 81.43 GB | 0.18 GB | 82.42 GB | — | Insufficient |
| Q5_K_M_STOCK | 5.54 | Very good | 82.28 GB | 0.18 GB | 83.27 GB | — | Insufficient |
| Q5_K_M | 5.61 | Very good | 83.3 GB | 0.18 GB | 84.28 GB | — | Insufficient |
| Q5_K_L | 5.98 | Very good | 88.79 GB | 0.18 GB | 89.78 GB | — | Insufficient |
| Q6_K_STOCK | 6.42 | Very good | 95.22 GB | 0.18 GB | 96.2 GB | — | Insufficient |
| Q6_K | 6.75 | Excellent | 100.13 GB | 0.18 GB | 101.11 GB | — | Insufficient |
| Q8_0 | 8.35 | Excellent | 123.97 GB | 0.18 GB | 124.95 GB | — | Insufficient |
| BF16 | 15.62 | Excellent | 231.85 GB | 0.18 GB | 232.83 GB | — | Insufficient |
KV cache computed from the model's exact architecture. Speed is a rough estimate bounded by memory bandwidth.
Frequently asked questions
What kind of model is AtomicChat/Ling-3.0-flash-GGUF?
AtomicChat/Ling-3.0-flash-GGUF is a language model with 127.49 billion parameters, based on the bailingmoe3 architecture. It is released under the mit license and distributed as GGUF files for local inference.
Is AtomicChat/Ling-3.0-flash-GGUF a Mixture-of-Experts (MoE) model?
Yes. AtomicChat/Ling-3.0-flash-GGUF is a Mixture-of-Experts model with 512 experts, of which 8 are activated per token. That makes it faster than a dense model of the same size, but all 512 experts must be loaded into memory, so the VRAM/RAM it needs is driven by the total parameter count, not the active one.
How does AtomicChat/Ling-3.0-flash-GGUF handle long context?
AtomicChat/Ling-3.0-flash-GGUF uses Multi-head Latent Attention, which caches one compressed latent per layer instead of full keys and values. Its KV cache therefore grows much more slowly than a standard model of the same size, so long contexts add relatively little memory.
How much VRAM do you need to run AtomicChat/Ling-3.0-flash-GGUF?
You need about 31.15 GB of VRAM to run AtomicChat/Ling-3.0-flash-GGUF entirely on the GPU using the IQ1_S quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run AtomicChat/Ling-3.0-flash-GGUF on an 8 GB GPU?
No. AtomicChat/Ling-3.0-flash-GGUF does not fit on an 8 GB GPU, even with the smallest quantization and system RAM offloading.
Can I run AtomicChat/Ling-3.0-flash-GGUF on a 16 GB GPU?
Partially. AtomicChat/Ling-3.0-flash-GGUF only fits on a 16 GB GPU by offloading part of it to system RAM (with IQ2_M), which runs but is slower.
Can I run AtomicChat/Ling-3.0-flash-GGUF on a 24 GB GPU?
Partially. AtomicChat/Ling-3.0-flash-GGUF only fits on a 24 GB GPU by offloading part of it to system RAM (with Q4_K_M_STOCK), which runs but is slower.
What context length does AtomicChat/Ling-3.0-flash-GGUF support?
AtomicChat/Ling-3.0-flash-GGUF supports a native context length of up to 131,072 tokens. A longer context grows the KV cache, so it increases the memory needed to run the model.
What is the best quantization for AtomicChat/Ling-3.0-flash-GGUF?
For AtomicChat/Ling-3.0-flash-GGUF, a strong default is Q4_K_M_STOCK, which needs about 71.09 GB and keeps most of the quality while roughly halving the memory versus 8-bit. With VRAM to spare, Q5_K_M or Q6_K add a little more quality; if you are tight on memory, a smaller quantization still runs. Pick the highest quantization that fits your VRAM.