DeepSeek V4 Flash 284B
By DeepSeek · China
Updated 2026-07-13
Overview
DeepSeek V4's efficient sibling: 284B MoE with 13B active params, MIT-licensed, 1M context, and the same three-mode reasoning stack. Frontier-adjacent quality at a fraction of the inference cost.
When to pick this model
- Frontier-class reasoning at single-server scale
- Million-token context analysis without datacenter budgets
- MIT-licensed alternatives to V4 Pro
- Workloads choosing between Base and Instruct variants
- Cost-sensitive deployments needing three thinking modes
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 170 GB |
| Q5_K_M | 205 GB |
| Q8_0 | 305 GB |
| FP16 (no quantization) | 568 GB |
VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.
In practice, DeepSeek V4 Flash 284B is server-class even at Q4_K_M (170 GB). Stepping up to Q8_0 nearly doubles the footprint to 305 GB, and unquantized FP16 weights take 568 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, DeepSeek V4 Flash 284B needs roughly 200 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 2 tokens/sec on entry-level GPUs, on the order of 8 tokens/sec on a mid-range card, and up to 22 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches DeepSeek V4 Flash 284B to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.
| GPU memory | Example cards | Best fit for DeepSeek V4 Flash 284B |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Does not fit — needs 170 GB at Q4_K_M |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Does not fit — needs 170 GB at Q4_K_M |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | Does not fit — needs 170 GB at Q4_K_M |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | Does not fit — needs 170 GB at Q4_K_M |
| 32 GB | RTX 5090 | Does not fit — needs 170 GB at Q4_K_M |
Which GPU should you buy to run DeepSeek V4 Flash 284B?
To run DeepSeek V4 Flash 284B locally at Q4, you need ~170 GB of VRAM. The best value for this is a Apple Mac Studio (64+ GB unified memory).
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Strengths
- MIT license
- 1M context window
- Only 13B active params — fast for its total size
- Three thinking modes inherited from V4 Pro
- Base and Instruct variants available
Limitations
- Around 170 GB VRAM in Q4 — still multi-GPU
- Official community quantizations were lagging at launch
- Quality trails V4 Pro on the hardest reasoning tasks
Typical workloads
In our catalog grid, DeepSeek V4 Flash 284B is filed under Efficient Reasoning, Long Context, Workstation — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: multi-step reasoning and math-flavoured tasks; multilingual workloads.
The 976k-token context window is large enough to hold entire codebases' worth of files or long reports in a single prompt, which is what makes local RAG and document analysis practical. The MIT license is permissive, so shipping it inside a commercial product raises no special legal questions.
Architecture & training
Architecture: MoE 284B/13B active · CSA+HCA hybrid · mHC · Muon · mixed FP4+FP8
Training: Targets "efficient reasoning" at reduced cost vs V4 Pro.
The efficient way into the V4 family — MIT, 1M context, and inference cost that won't bankrupt you.
Quick start
# HuggingFace : deepseek-ai/DeepSeek-V4-Flash (GGUF communautaire en cours)Or use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
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Frequently asked questions
How much VRAM does DeepSeek V4 Flash 284B need?
At the recommended Q4_K_M quantization, DeepSeek V4 Flash 284B needs about 170 GB of VRAM. Q8_0 takes 305 GB, and unquantized FP16 weights take 568 GB.
Can DeepSeek V4 Flash 284B run without a GPU?
Yes — with roughly 200 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.
What context window does DeepSeek V4 Flash 284B support?
DeepSeek V4 Flash 284B supports a 976k-token context window (1,000,000 tokens).
Can I use DeepSeek V4 Flash 284B commercially?
Yes. DeepSeek V4 Flash 284B is released under MIT, a permissive open-source license that allows commercial use, modification and redistribution.
How fast is DeepSeek V4 Flash 284B on consumer hardware?
Our compatibility engine estimates on the order of 8 tokens/sec on a mid-range GPU and up to 22 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of DeepSeek V4 Flash 284B should I download first?
Start with Q4_K_M (170 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. It does not fit a single 24 GB consumer card — plan for multi-GPU or server hardware.