deepseek-v4-gguf
DeepSeek-V4-Flash GGUF is a quantized version of DeepSeek's V4 Flash model optimized for the ds4 inference engine. It uses aggressive quantization (2-bit and 4-bit) on routed experts while preserving higher precision on decision-critical components (router, attention, shared experts). Available in two main sizes (80.8 GB and 153.3 GB) for different hardware tiers, plus an optional 3.6 GB speculative-decoding auxiliary model. Designed for self-hosted inference on high-memory machines.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | antirez |
| Parameters | Unknown |
| Context window | Unknown |
| License | mit — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 6.4M |
| Likes | 312 |
| Last updated | 2026-05-31 |
| Source | antirez/deepseek-v4-gguf |
What deepseek-v4-gguf is
A GGUF-format Mixture-of-Experts (MoE) model derived from deepseek-ai/DeepSeek-V4-Flash. Quantization strategy: routed experts use IQ2_XXS + Q2_K (80 GB variant) or Q4_K (153 GB variant); router, attention projections, shared experts, and output head quantized to Q8_0; embeddings, router logic, and normalization kept at F16/F32. Supports ds4-specific auxiliary blocks (compressor, indexer, hash-routing tables). Optional MTP model enables speculative decoding. Requires 128–256+ GB RAM depending on variant; compatible with Apple Silicon via Metal acceleration.
Run deepseek-v4-gguf locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="antirez/deepseek-v4-gguf")out = pipe("Explain retrieval-augmented generation in one sentence.", max_new_tokens=128)print(out[0]["generated_text"])Swap in vLLM or Ollama for production-grade serving. DEV.co can stand up the inference stack.
How you'd run it
A typical self-hosted path — open weights, an inference server, your application.
DEV.co builds each layer — from GPU infrastructure to the application.
Best use cases
Running & fine-tuning it
Minimum 128 GB RAM for q2 variant (80.8 GB model); 256+ GB RAM for q4 variant (153.3 GB model). Estimates assume peak VRAM + KV cache + system overhead; exact requirements depend on context length (Unknown) and batch size. Metal acceleration available for Apple Silicon. CPU inference feasible but slow without GPU support. Optional MTP model (3.6 GB) may be loaded alongside main model for speculative decoding.
Not addressed in card. Base model is Mixture-of-Experts; fine-tuning quantized GGUF models typically requires quantization-aware training or LoRA on the original (unquantized) weights. Feasibility Unknown without access to original DeepSeek-V4-Flash architecture details or ds4 fine-tuning support.
When to avoid it — and what to weigh
- Need cloud-first, managed inference — This is a self-hosted model requiring 128–256+ GB RAM and custom setup. Not suitable if you need AWS/Azure/GCP auto-scaling or zero infrastructure overhead.
- Require instruction-tuned or chat-optimized behavior out-of-the-box — Model card labels the files as 'chat-v2' but does not state whether the base model was instruction-tuned. May require fine-tuning or careful prompting for structured tasks.
- Limited hardware (< 128 GB RAM) or legacy inference engines — Quantizations are optimized for ds4. Compatibility with llama.cpp, vLLM, or other engines is stated as 'should work but not guaranteed,' especially for MTP speculative decoding.
- Need guaranteed vendor support or SLAs — Community-maintained GGUF by a third party (antirez). DeepSeek holds copyright on the base model; no SLA or official support channel mentioned.
License & commercial use
MIT license. Card states: 'The base model copyright is held by DeepSeek; the GGUFs are redistributed under the base model's release terms.' MIT is a permissive OSI license allowing commercial use, modification, and distribution with attribution.
MIT license typically permits commercial use, but the card explicitly notes copyright is held by DeepSeek and redistributed 'under the base model's release terms.' Recommend verifying DeepSeek's original release terms (not provided in DATA) to confirm no additional restrictions apply. If using DeepSeek's model for production, contact DeepSeek or review their official license statement.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Unknown |
| Documentation | Adequate |
| License clarity | Needs review |
| Deployment complexity | High |
| DEV.co fit | Strong |
| Assessment confidence | Medium |
Model weights are quantized GGUF artifacts. Integrity depends on download source (HuggingFace CDN). No explicit code signing, attestation, or supply-chain security mentioned. ds4 is open-source (GitHub link provided); review its code for vulnerabilities if deploying in production. Quantization itself does not constitute a security mechanism; model biases or alignment properties Unknown.
Alternatives to consider
Llama 3.1 405B (Meta)
Permissive license, larger adoption, broader inference engine support (vLLM, TGI, llama.cpp). Requires similar or higher VRAM (405B quantized ~160+ GB). Better documentation and community tooling.
Qwen2.5-72B (Alibaba)
Smaller footprint, permissive Apache 2.0 license, strong instruction-tuning. Requires less RAM (72B quantized ~20–40 GB); may sacrifice raw capability vs. 400B+ MoE models.
Mistral 8x22B MoE (Mistral AI)
Mixture-of-Experts like DeepSeek-V4-Flash, Apache 2.0 license, better broad inference engine compatibility. Smaller total parameters (~140B active) than DeepSeek-V4-Flash; trade-off in raw capability.
Ship deepseek-v4-gguf with senior software developers
Review the quantization strategy, verify DeepSeek's commercial terms, test compatibility with your inference stack (ds4, llama.cpp, or vLLM), and validate hardware requirements before committing to self-hosted deployment.
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deepseek-v4-gguf FAQ
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Evaluate DeepSeek-V4-Flash GGUF for Your Deployment
Review the quantization strategy, verify DeepSeek's commercial terms, test compatibility with your inference stack (ds4, llama.cpp, or vLLM), and validate hardware requirements before committing to self-hosted deployment.