On this page
No. An RTX 5090 alone can’t run DeepSeek V4.1 Flash, because the smallest 1-bit build still needs about 122 GB of RAM and VRAM together, and the card has 32 GB. To run it at home you want a PC with about 128 GB of system memory, with or without the 5090. 64 GB works too, but slowly. If you want a strong model on the PC you already own, Off Grid AI Desktop runs Qwen 3.8 27B for free on a 24 GB or 32 GB card, or on 32 GB of system RAM.
Download Off Grid AI Desktop (free, Windows, Linux and Mac)
How big is DeepSeek V4.1 Flash?
DeepSeek’s model card lists 552B backbone parameters plus 196B of “Engram” memory, MIT licence and a context of up to 1 million tokens. It’s a mixture-of-experts model, so it only uses 8B parameters per token while reading your prompt and 16B while writing. That keeps it fast for its size. It doesn’t shrink the memory it needs, because every expert still has to be loaded somewhere.
The Engram part is a lookup table. Unsloth’s run guide says it can stay on your SSD and load rows on demand, which is why a 500 GB model can run on a 128 GB machine at all.
| Unsloth build | RAM + VRAM needed (minimum) | Engram table on SSD | Full download |
|---|---|---|---|
| 1-bit (UD-IQ1_S) | 122.4 GB | 110.6 GB | 233 GB |
| 2-bit (UD-Q2_K_XL) | 199.6 GB | 135.2 GB | 335 GB |
| 3-bit (UD-IQ3_XXS) | 233.5 GB | 161.3 GB | 395 GB |
| 4-bit (UD-Q4_K_XL) | 297.9 GB | 202.8 GB | 501 GB |
Memory figures are from the Unsloth guide and download sizes from the Hugging Face file list, both checked 16 November 2026. Unsloth adds that context and the runtime need more on top, and that 64 GB of RAM works but is slower.
So what does an RTX 5090 get you?
A 5090 has 32 GB of VRAM. That’s a quarter of what the 1-bit build needs, so most of the model sits in system RAM and the card only holds part of it. You still want about 128 GB of RAM, and the 5090 becomes the expensive half of the build.
A 24 GB RTX 4090 is in the same spot. A 16 GB RTX 5080 holds even less. No single consumer card holds DeepSeek V4.1 Flash.
What the hardware costs today
GPU and memory prices have doubled or more since launch. These are the lowest US prices we found on 16 November 2026.
| Part | Memory | Launch price (MSRP) | Lowest deal, Nov 2026 |
|---|---|---|---|
| RTX 5090 | 32 GB VRAM | $1,999 | $6,099 |
| RTX 4090 (used) | 24 GB VRAM | $1,599 | $3,200 |
| RTX 5080 | 16 GB VRAM | $999 | $1,900 |
| 128 GB DDR5 kit | 128 GB RAM | about $329 a year earlier | $1,460 to $3,399 |
The 5090 Founders Edition still lists at $1,999 but was out of stock when we checked. Prices move daily, so check before you buy.
What can you run on the PC you already own?
Qwen 3.8 27B. It’s Alibaba’s dense 27B model with vision, Apache 2.0 licence and a 262K context (model card). Off Grid AI Desktop has it in its catalog as a 16.5 GB 4-bit file plus a 0.9 GB vision file, with 32 GB of memory as its guidance.
| Your PC | What to run | How it fits |
|---|---|---|
| RTX 5090 (32 GB) | Qwen 3.8 27B, 4-bit, or the 29 GB 8-bit build | Whole model on the card |
| RTX 4090 or 3090 (24 GB) | Qwen 3.8 27B, 4-bit | Whole model on the card, room for context |
| 16 GB card (5080, 5070 Ti, 5060 Ti) | Qwen 3.8 27B, 3-bit (10.9 to 13.1 GB) | On the card; or 4-bit with some layers in RAM |
| No GPU, 32 GB RAM | Qwen 3.8 27B, 4-bit | On the CPU, slower |
| 16 GB RAM or less | A smaller model the app marks as a fit | See the fit badges in Models |
File sizes are from the Unsloth Qwen 3.8 27B list.
Set it up on Windows or Linux
- Install Off Grid AI Desktop and open it.
- On an NVIDIA PC, go to Settings → GPU performance. In Use your NVIDIA GPU, choose Download, then Restart app when it asks.
- Open Models, search for Qwen 3.8 27B and download it. If the app shows Tight on RAM or Won’t fit, pick a smaller build or model.
- Start a chat and pick the model. In Model settings, leave the backend on Auto or choose CUDA.
- Send a short prompt, then check the backend’s Now value. It shows what actually loaded.
If the model is bigger than your card, lower the GPU layers slider in the app’s performance settings. The rest of the model runs from system RAM. It works, but replies get slower with every layer you move off the card.
Chats stay on your PC and the model keeps working with no internet after the download.
What goes wrong, and the fix
| Symptom | Check | Fix |
|---|---|---|
| Model won’t load | The fit badge in Models | Pick a smaller quant or model |
| Very slow replies | How many GPU layers are on the card | Use a smaller quant so it all fits on the GPU |
| Now shows CPU on an NVIDIA PC | Whether the NVIDIA pack installed | Download it in Settings → GPU performance and restart |
| Long chats slow down | The Context window in Model settings | Lower it, or start a new chat |
Need the full-size model?
If you want DeepSeek V4.1 Flash itself, Off Grid AI Boost is a flat-price API for open models on our GPUs, with DeepSeek V4.1 Flash and Qwen 3.8 on the list. It starts at $4.99 a month for the first cohort, with regular prices from $9.99 to $199.99. We don’t store your prompts or train on them. That’s our promise today, and the full policy comes with launch.
For scale, the cheapest RTX 5090 we found was $6,099 and Boost’s lowest plan is $4.99 a month. Those are prices only. Boost’s usage limits aren’t published yet, so we’re not claiming it matches a local card on speed or volume.
Boost starts in January; pre-order to lock in half price. API keys arrive by January 2027.
FAQ
Can an RTX 5090 run DeepSeek V4.1 Flash on its own?
No. The smallest build needs about 122 GB of RAM and VRAM together, and the 5090 has 32 GB. You want about 128 GB of system RAM as well, or 64 GB if you can live with it being slow.
What’s the cheapest way to run DeepSeek V4.1 Flash at home?
A machine with 128 GB of memory and a fast SSD for the Engram table, running the 1-bit build. Unsloth says 64 GB works too, but slower.
Does Off Grid AI Desktop run DeepSeek V4.1 Flash?
We haven’t confirmed it loads in the app yet. Unsloth’s guide uses llama.cpp with settings that keep the Engram table on disk. Off Grid AI Desktop is the easy route for Qwen 3.8 27B and smaller models.
What should I run on a 24 GB GPU?
Qwen 3.8 27B at 4-bit. The file is about 16.5 GB, so it fits on the card with room for context.
Is Off Grid AI Desktop free?
Yes. It’s free and open source on Windows, Linux and Mac, with no account.
Try Qwen 3.8 27B on the PC you have before you spend $6,000 on a card. Download Off Grid AI Desktop.