DeepSeek-V4-Flash-0731 GGUF size and VRAM requirements
unsloth/DeepSeek-V4-Flash-0731-GGUF is a very large language model with 304.18 billion parameters, built on the deepseek4 architecture. It is released under the mit license and has been downloaded 404,341 times.
DeepSeek-V4-Flash-0731-GGUF is a Mixture-of-Experts model with 256 experts, of which 6 are active on each token. Routing only 6 of 256 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 43 transformer layers, a hidden size of 4,096, 64 attention heads. It uses grouped-query attention (64 query heads sharing 1 key/value heads), which already trims KV-cache memory compared with full multi-head attention.
To run unsloth/DeepSeek-V4-Flash-0731-GGUF locally at a 4,096-token context, its quantized versions need between 78.01 GB (IQ1_S, lowest quality) and 151.89 GB (Q8_K_XL, 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 DeepSeek-V4-Flash-0731-GGUF is about 0.34 GB, rising to roughly 86.0 GB at its full 1,048,576-token context. Shorter prompts free up memory for a higher-quality quantization.
Available GGUF quantizations for unsloth/DeepSeek-V4-Flash-0731-GGUF include IQ1_S, IQ1_M, IQ2_XXS, IQ2_M, Q2_K_XL, IQ3_XXS, IQ3_S, Q3_K_M, Q3_K_XL, IQ4_NL, IQ4_XS, Q4_K_XL, Q8_K_XL. The model supports a native context length of up to 1,048,576 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 | 2.17 | Very low | 76.87 GB | 0.34 GB | 78.01 GB | — | Insufficient |
| IQ1_M | 2.29 | Very low | 80.93 GB | 0.34 GB | 82.07 GB | — | Insufficient |
| IQ2_XXS | 2.39 | Very low | 84.62 GB | 0.34 GB | 85.76 GB | — | Insufficient |
| IQ2_M | 2.39 | Very low | 84.68 GB | 0.34 GB | 85.82 GB | — | Insufficient |
| Q2_K_XL | 2.55 | Very low | 90.18 GB | 0.34 GB | 91.32 GB | — | Insufficient |
| IQ3_XXS | 2.74 | Low | 97.05 GB | 0.34 GB | 98.19 GB | — | Insufficient |
| IQ3_S | 3.05 | Low | 108.1 GB | 0.34 GB | 109.23 GB | — | Insufficient |
| Q3_K_M | 3.37 | Fair | 119.28 GB | 0.34 GB | 120.42 GB | — | Insufficient |
| Q3_K_XL | 3.37 | Fair | 119.4 GB | 0.34 GB | 120.54 GB | — | Insufficient |
| IQ4_NL | 3.59 | Fair | 127.28 GB | 0.34 GB | 128.41 GB | — | Insufficient |
| IQ4_XS | 3.59 | Fair | 127.28 GB | 0.34 GB | 128.41 GB | — | Insufficient |
| Q4_K_XL | 4.08 | Fair | 144.44 GB | 0.34 GB | 145.58 GB | — | Insufficient |
| Q8_K_XL | 4.26 | Good | 150.75 GB | 0.34 GB | 151.89 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 unsloth/DeepSeek-V4-Flash-0731-GGUF?
unsloth/DeepSeek-V4-Flash-0731-GGUF is a language model with 304.18 billion parameters, based on the deepseek4 architecture. It is released under the mit license and distributed as GGUF files for local inference.
Is unsloth/DeepSeek-V4-Flash-0731-GGUF a Mixture-of-Experts (MoE) model?
Yes. unsloth/DeepSeek-V4-Flash-0731-GGUF is a Mixture-of-Experts model with 256 experts, of which 6 are activated per token. That makes it faster than a dense model of the same size, but all 256 experts must be loaded into memory, so the VRAM/RAM it needs is driven by the total parameter count, not the active one.
Can I run unsloth/DeepSeek-V4-Flash-0731-GGUF on an 8 GB GPU?
No. unsloth/DeepSeek-V4-Flash-0731-GGUF does not fit on an 8 GB GPU, even with the smallest quantization and system RAM offloading.
Can I run unsloth/DeepSeek-V4-Flash-0731-GGUF on a 16 GB GPU?
No. unsloth/DeepSeek-V4-Flash-0731-GGUF does not fit on a 16 GB GPU, even with the smallest quantization and system RAM offloading.
Can I run unsloth/DeepSeek-V4-Flash-0731-GGUF on a 24 GB GPU?
No. unsloth/DeepSeek-V4-Flash-0731-GGUF does not fit on a 24 GB GPU, even with the smallest quantization and system RAM offloading.
What context length does unsloth/DeepSeek-V4-Flash-0731-GGUF support?
unsloth/DeepSeek-V4-Flash-0731-GGUF supports a native context length of up to 1,048,576 tokens. A longer context grows the KV cache, so it increases the memory needed to run the model.
What is the best quantization for unsloth/DeepSeek-V4-Flash-0731-GGUF?
For unsloth/DeepSeek-V4-Flash-0731-GGUF, 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.