Short answer: DeepSeek V4 Flash won’t run on a single consumer GPU, but it’s the most attainable 300B-class model to hit in months. The 284B-parameter MoE model needs about 92 GB of combined memory even at 1-bit quantization (an 82.5 GB file), well under half of what GLM-5.2 requires. Realistic options are a 24GB GPU plus 96GB of system RAM, a Mac Studio with 128GB+ unified memory, a dual-GPU workstation, or renting a single 80GB+ cloud card by the hour. This guide covers the exact numbers for each path.
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Update, October 7, 2026: DeepSeek released V4.1 Flash on September 10, 2026, and it is a much larger model, not a drop-in upgrade. Its model card lists a 552B-parameter backbone plus a 196B “Engram” memory table, and the Hugging Face checkpoint totals about 763B parameters, roughly 2.5 times the size of V4 Flash 0731. It stays MIT licensed and activates only 8B parameters per token during prefill and 16B during decode. Every memory figure in this guide applies to V4 Flash 0731. Do not use these numbers to size hardware for V4.1.
DeepSeek V4 Flash Architecture
DeepSeek V4 Flash 0731 landed at the end of July 2026 and immediately hit the top of Hacker News, driven by its ARC-AGI benchmark results: 89.0% on ARC-AGI-1 and 61.4% on ARC-AGI-2 at max reasoning effort, both near frontier-model territory, at $0.02 and $0.04 per task on DeepSeek’s API. It ships MIT-licensed with full open weights.
DeepSeek V4 Flash 0731
The original V4 Flash model card lists 13B activated parameters per token out of 284B. For the 0731 release DeepSeek only says the activated count is “far smaller” than DeepSeek-V4-Pro’s while beating it on benchmarks, which is the efficiency story behind the low API pricing. As with any MoE model, that efficiency applies to compute, not memory: all 284B parameters have to be loaded, since different tokens route to different experts. See our LLM quantization guide for why that gap between “active” and “total” params matters for hardware planning.
Memory Requirements by Quantization
These figures are for Unsloth’s dynamic GGUF quantizations (102 variants were live on Hugging Face within days of release). The table shows each file’s size and the combined memory (VRAM plus system RAM) Unsloth says you need to run it, since llama.cpp can split MoE layers between GPU and CPU.
| Quantization | File Size / Memory Needed | Realistic Hardware |
|---|---|---|
| 1-bit (UD-IQ1_S) | 82.5 GB / about 92 GB | 24GB GPU + 96GB RAM |
| 2-bit (UD-Q2_K_XL) | 96.8 GB / about 102 GB | 128GB Mac Studio or GPU + 96GB RAM |
| 3-bit (UD-Q3_K_XL) | 128 GB / about 135 GB | 2x 80GB GPUs, or GPU + 128GB RAM |
| 4-bit (UD-Q4_K_XL) | 155 GB / about 162 GB | 2x 96GB RTX PRO 6000 or 2x H200 |
| 8-bit (UD-Q8_K_XL) | 162 GB / about 169 GB | 2x 96GB RTX PRO 6000 or 2x H200 |
Key insight: every quant level here is smaller than GLM-5.2’s minimum 223 GB. The 1-bit build is small enough that a single high-VRAM datacenter card, or a well-specced consumer GPU with generous system RAM, can actually hold it. That’s a meaningfully lower bar than most 300B+ open models set this year.
Four Ways to Run DeepSeek V4 Flash
Path 1: One 24GB GPU + 96GB System RAM
llama.cpp keeps attention layers and active experts on a single RTX 4090 or 5090 and streams the rest of the 1-bit quant’s MoE experts from system RAM. A 24GB card plus 96GB of RAM gives 120GB combined, enough headroom over the roughly 92GB the 1-bit quant needs for context and OS overhead. This is the cheapest entry point for a 300B-class model we’ve covered, see our RAM for local LLMs guide for the platform requirements.
Path 2: Mac Studio with 128GB+ Unified Memory
A Mac Studio with 128GB unified memory runs the 2-bit GGUF through llama.cpp’s Metal backend without any offloading complexity. It’s the simplest turnkey option and, unlike GLM-5.2’s 256GB requirement, doesn’t force you into Apple’s most expensive configuration.
Path 3: Dual-GPU Workstation (4-bit, Near-Lossless)
The 4-bit quant is a 155GB file and needs about 162GB to run, so two 80GB cards (160GB) fall just short. Two 96GB RTX PRO 6000 Blackwell cards or two H200s (141GB each) hold it entirely in VRAM with no CPU offload penalty. This is the setup for anyone who wants near-full quality and fast, consistent throughput rather than the accuracy tradeoffs of 1-bit or 2-bit.
Path 4: Rent Cloud GPUs (Best for Trying It First)
A two-card 80GB node (or a single 141GB H200) is enough to test the 1-bit or 2-bit quant before you buy anything, and a 3-4x 80GB node covers the near-lossless 4-bit build. Vast.ai usually has the lowest hourly prices on single and multi-GPU configurations, and RunPod gives new users a $5 credit to test with. See our cloud GPU comparison for current pricing.
DeepSeek V4 Flash vs GLM-5.2: Which Is Actually Self-Hostable?
Both are open-weight MoE models that made headlines this summer, but they sit on opposite ends of the self-hosting difficulty curve. GLM-5.2 is a 744B-parameter model that needs 223GB+ even at its smallest usable quant. DeepSeek V4 Flash is less than half the total size at 284B parameters, and its smallest usable quant is an 82.5GB file needing about 92GB to run, well under half of GLM-5.2’s floor.
For most home setups, that difference decides the question before you even look at benchmarks: GLM-5.2 realistically requires a 256GB Mac Studio, a large-RAM workstation, or serious multi-GPU spend. DeepSeek V4 Flash fits a much wider range of existing hardware, including rigs enthusiasts already own. Our GLM-5.2 hardware guide has the full breakdown if you’re weighing both.
Pro tip: whichever path you pick, quantize the KV cache. Running llama.cpp with –cache-type-k q4_1 –cache-type-v q4_1 stretches your usable context window roughly 3x at the same memory budget, especially useful given DeepSeek V4 Flash’s 384K max output length.
FAQ
Can you run DeepSeek V4 Flash on a single GPU?
Not the full model, but you’re closer than with most 300B+ models. The smallest usable quant (1-bit, UD-IQ1_S) is an 82.5 GB file and needs about 92 GB of combined memory to run. A single 24GB GPU can’t hold that alone, but paired with 96 GB of system RAM for MoE offloading, or a single 96GB+ workstation or datacenter card, it fits.
How much VRAM does DeepSeek V4 Flash need?
DeepSeek’s official weights are about 156 GB, because they already ship in mixed FP4 and FP8 precision. Unsloth’s GGUF files range from 82.5 GB at 1-bit to 162 GB at 8-bit, and need roughly 92 GB to 169 GB of combined memory to run. There’s no version that fits in 24 GB of VRAM alone; every practical setup either offloads to system RAM or spreads across multiple GPUs.
What’s the cheapest way to run DeepSeek V4 Flash locally?
One 24GB GPU (RTX 4090 or 5090) plus 96 GB of system RAM using llama.cpp MoE offloading, running the 1-bit quant. That’s a realistic enthusiast build, unlike 700B-class MoE models that demand 256 GB+ of RAM just to start.
Is DeepSeek V4 Flash easier to self-host than GLM-5.2?
Yes, significantly. GLM-5.2 needs around 245 GB even at 2-bit. DeepSeek V4 Flash’s smallest usable quant needs about 92 GB, well under half of that. It’s the first 300B-class open-weight model in months that a well-specced single-GPU workstation can realistically run, even if only at 1-bit.
Why is DeepSeek V4 Flash getting so much attention?
It scored 89.0% on ARC-AGI-1 and 61.4% on ARC-AGI-2, both near-frontier results, at $0.02 and $0.04 per task respectively, which is dramatically cheaper than comparable closed models. It’s also MIT-licensed with 102 community quantizations already live on Hugging Face within days of release.
Related Reading
Unified Memory PCs for Local LLMs
DGX Spark, Mac Studio, Ryzen AI Max compared
GLM-5.2 Hardware Guide
The 744B model that needs 245GB+ just to start
LLM Quantization Guide
GGUF, GPTQ, AWQ and what the bit levels really cost
RAM for Local LLMs
MoE offloading setups, explained in depth
Qwen3.8-27B Hardware Guide
Skip offloading entirely, this one fits one GPU
Multi-GPU Ollama Setup
How this model actually splits across cards
Cloud GPU Providers
Rent 80GB-class cards by the hour to test first
Building for Big MoE Models?
Need a workstation that can hold 96GB+ of system RAM? Check our Tailored Builds page.