Solstice-AI/DeepSeek-V4.1-Flash-GGUF overview
DeepSeek V4.1 Flash GGUF llama.cpp GGUF of deepseek ai/DeepSeek V4.1 Flash https://huggingface.co/deepseek ai/DeepSeek V4.1 Flash . This is V4.1 Flash Deepseek…
Runs locally from ~7.42 GB disk (8 GB VRAM class GPUs with llama.cpp / guIDE).
Repository Files & Downloads
| File | Type | Quantization | Size | Link |
|---|---|---|---|---|
| DeepSeek-V4.1-Flash-Q3_K_M-00001-of-00009.gguf | GGUF | Q3_K_M | 7.42 GB | Download |
| DeepSeek-V4.1-Flash-Q3_K_M-00002-of-00009.gguf | GGUF | Q3_K_M | 39.40 GB | Download |
| DeepSeek-V4.1-Flash-Q3_K_M-00003-of-00009.gguf | GGUF | Q3_K_M | 41.15 GB | Download |
| DeepSeek-V4.1-Flash-Q3_K_M-00004-of-00009.gguf | GGUF | Q3_K_M | 38.29 GB | Download |
| DeepSeek-V4.1-Flash-Q3_K_M-00005-of-00009.gguf | GGUF | Q3_K_M | 41.78 GB | Download |
| DeepSeek-V4.1-Flash-Q3_K_M-00006-of-00009.gguf | GGUF | Q3_K_M | 40.11 GB | Download |
| DeepSeek-V4.1-Flash-Q3_K_M-00007-of-00009.gguf | GGUF | Q3_K_M | 40.62 GB | Download |
| DeepSeek-V4.1-Flash-Q3_K_M-00008-of-00009.gguf | GGUF | Q3_K_M | 40.67 GB | Download |
| DeepSeek-V4.1-Flash-Q3_K_M-00009-of-00009.gguf | GGUF | Q3_K_M | 33.99 GB | Download |
| DeepSeek-V4.1-Flash-Q4_K_M-00001-of-00011.gguf | GGUF | Q4_K_M | 9.22 GB | Download |
| DeepSeek-V4.1-Flash-Q4_K_M-00002-of-00011.gguf | GGUF | Q4_K_M | 51.50 GB | Download |
| DeepSeek-V4.1-Flash-Q4_K_M-00003-of-00011.gguf | GGUF | Q4_K_M | 40.49 GB | Download |
| DeepSeek-V4.1-Flash-Q4_K_M-00004-of-00011.gguf | GGUF | Q4_K_M | 40.61 GB | Download |
| DeepSeek-V4.1-Flash-Q4_K_M-00005-of-00011.gguf | GGUF | Q4_K_M | 20.34 GB | Download |
| DeepSeek-V4.1-Flash-Q4_K_M-00006-of-00011.gguf | GGUF | Q4_K_M | 51.50 GB | Download |
| DeepSeek-V4.1-Flash-Q4_K_M-00007-of-00011.gguf | GGUF | Q4_K_M | 40.69 GB | Download |
| DeepSeek-V4.1-Flash-Q4_K_M-00008-of-00011.gguf | GGUF | Q4_K_M | 41.90 GB | Download |
| DeepSeek-V4.1-Flash-Q4_K_M-00009-of-00011.gguf | GGUF | Q4_K_M | 40.67 GB | Download |
| DeepSeek-V4.1-Flash-Q4_K_M-00010-of-00011.gguf | GGUF | Q4_K_M | 41.70 GB | Download |
| DeepSeek-V4.1-Flash-Q4_K_M-00011-of-00011.gguf | GGUF | Q4_K_M | 35.58 GB | Download |
| DeepSeek-V4.1-Flash-Q8_0-00001-of-00010.gguf | GGUF | Q8_0 | 40.35 GB | Download |
| DeepSeek-V4.1-Flash-Q8_0-00002-of-00010.gguf | GGUF | Q8_0 | 40.34 GB | Download |
| DeepSeek-V4.1-Flash-Q8_0-00003-of-00010.gguf | GGUF | Q8_0 | 40.34 GB | Download |
| DeepSeek-V4.1-Flash-Q8_0-00004-of-00010.gguf | GGUF | Q8_0 | 40.34 GB | Download |
| DeepSeek-V4.1-Flash-Q8_0-00005-of-00010.gguf | GGUF | Q8_0 | 40.34 GB | Download |
| DeepSeek-V4.1-Flash-Q8_0-00006-of-00010.gguf | GGUF | Q8_0 | 40.34 GB | Download |
| DeepSeek-V4.1-Flash-Q8_0-00007-of-00010.gguf | GGUF | Q8_0 | 26.89 GB | Download |
| DeepSeek-V4.1-Flash-Q8_0-00008-of-00010.gguf | GGUF | Q8_0 | 97.28 GB | Download |
| DeepSeek-V4.1-Flash-Q8_0-00009-of-00010.gguf | GGUF | Q8_0 | 97.28 GB | Download |
| DeepSeek-V4.1-Flash-Q8_0-00010-of-00010.gguf | GGUF | Q8_0 | 9.56 GB | Download |
Model Details
| Model ID | Solstice-AI/DeepSeek-V4.1-Flash-GGUF |
|---|---|
| Author | Solstice-AI |
| Pipeline | text-generation |
| License | mit |
| Base model | deepseek-ai/DeepSeek-V4.1-Flash |
| Last modified | 2026-10-15T03:21:13.000Z |
Model README
---
license: mit
base_model: deepseek-ai/DeepSeek-V4.1-Flash
base_model_relation: quantized
library_name: gguf
pipeline_tag: text-generation
tags:
- gguf
- deepseek
- deepseek-v4.1
- llama.cpp
quantized_by: vcruz305
---
DeepSeek-V4.1-Flash GGUF
llama.cpp GGUF of deepseek-ai/DeepSeek-V4.1-Flash.
This is V4.1-Flash (DeepseekV41ForCausalLM), a causal decoder with engram n-gram lookup
tables, hyper-connections and sparse attention. It is not V4-Flash-0731.
Recipe
How to build the engine, serve it, and the gotchas, plus the current status:
vcruz305/DeepSeek-V4.1-Flash-GGUF-DGX-Spark-recipe
Status
These files do not run on upstream llama.cpp yet. Conversion works and is open as
ggml-org/llama.cpp#28696. The runtime is in
progress on the runtime/deepseek41 branch of
vcruz305/llama.cpp: the loader, the engram tables and the
hyper-connections work and are verified against the reference implementation, and the sparse
attention is the remaining piece.
Weights land here as each rung finishes. Anything converted before 2026-09-10 carries
general.architecture = deepseek4 and is being redone as deepseek41.
The architecture string is deepseek41, following llama.cpp's habit of dropping the _v
(deepseek_v2 became deepseek2, deepseek_v3.2 became deepseek32).
2026-09-11 fix: the 4 Engram KV keys (head_count, key_length, max_ngram_size,
layer_ids) were written with a hardcoded deepseek4.engram.* prefix instead of resolving
{arch}.engram.* like every other arch-scoped key in the file. general.architecture and all
38 other arch-scoped keys were already correct (deepseek41.*); only these 4 were wrong, which
would have made the runtime/deepseek41 loader fail to find Engram config on an otherwise
loadable file. Fixed in place via a KV-only rewrite (tensor data untouched, verified
byte-identical by SHA-256) on all five quant rungs' first shard, where GGUF split files store
metadata. Confirmed live: all five now read deepseek41.engram.*.
Files
Ladder in order: Q2_K, Q3_K_M, Q4_K_M. Measured tensor payload:
| File | Quant | Bytes | GiB |
| --- | --- | ---: | ---: |
| DeepSeek-V4.1-Flash-Q2_K.gguf | Q2_K | 264,514,761,248 | 246.3 |
| DeepSeek-V4.1-Flash-Q3_K_M.gguf | Q3_K_M | 347,270,954,112 | 323.4 |
| DeepSeek-V4.1-Flash-Q4_K_M.gguf | Q4_K_M | pending | |
Split into parts, since each exceeds the Hub's single file limit.
Q5_K_M is skipped unless asked for. The routed experts arrive as MXFP4 at 4.25 bpw, so higher rungs
move parts of the mixture up rather than down: Q3_K_M already lands at 0.684 of the Q8_0 staging
file, and Q5_K_M would be close enough to Q8_0 to be poor value.
Most of the file is the two engram tables, roughly 196.6B parameters between them. They follow the
rung, 99,611 to 40,284 MiB each between q8_0 and q3_K.
Apache/MIT from upstream. Credit: DeepSeek. GGUF pack: Victor Cruz (vcruz305).
Run Solstice-AI/DeepSeek-V4.1-Flash-GGUF with guIDE
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