Can I run DeepSeek R1 Distill Qwen 14B on a Dual RTX 3090?
Key facts
Yes. DeepSeek R1 Distill Qwen 14B runs on a Dual RTX 3090, about 75 tokens per second (tok/s).
It needs about 11.4 GB at standard quality (estimate). Checked . unsloth Q4_K_M GGUF.
How much memory it needs
It uses about 24% of the memory that is free on this machine.
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What a Dual RTX 3090 costs
Dual RTX 3090, 64 GB RAMabout $3,808€3,592
Price details
$3,808€3,592
How we priced this
- Graphics cards (2×)$2,790€2,492
- Memory, 64 GB DDR4 (estimate)$555€600
- Rest of the PC: processor, board, case, power supply, storage (estimate)$463€500
The card is the GeForce RTX 3090 24GB. Used price, the typical price of second-hand cards that actually sold. Source: pcprice.watch, checked 24 Oct 2026.
The total is our estimate from these parts, not the price of a ready-made PC in a shop.
Prices excl. shipping unless marked. Euro prices incl. VAT where the seller charges it. Dollar prices are converted from our euro prices without the 21% EU VAT, before US sales tax.
With 128 GB of normal memory (RAM) instead of 64 GB: Dual RTX 3090, 128 GB RAM, about $5,528€5,452 (with used graphics cards). It runs DeepSeek R1 Distill Qwen 14B the same way: the extra memory only helps with models too big for the cards.
The cheapest graphics card on our list that also runs it (it still needs the rest of a PC): Radeon RX 9060 XT 16GB, $522€564, new (cheapest in-stock board on GPU PRIX's German board list). Fast~23 tok/s
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We never pass off an estimate as a benchmark. Commissions never change which hardware we recommend. How we calculate
A complete machine that runs it at least as fast as you read: Mac Studio M4 Max 36GB, about $2,116€1,890 (used). Comfortable~18.5 tok/s
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DeepSeek R1 Distill Qwen 14B on other graphics cards
Runs well9
The whole model fits in the graphics memory.
Doesn't fit1
Not enough graphics memory: it needs about 11.4 GB.
- RTX 3060 12GBhas 12 GB
More about the model itself, what it is good at and its licence: DeepSeek R1 Distill Qwen 14B.
The numbers behind this answer
A model comes in several sizes. Q4 is the standard one most people use; the bigger ones answer a little better but need more memory. Each row adds up the model file, the memory a conversation takes, and some working space, and compares the total with what is free on this machine.
Swipe the table sideways to see every column.
| Quality | Conversation | Result | Speed | Model file | Conversation memory | Working space | Total needed | Free memory |
|---|---|---|---|---|---|---|---|---|
| Q4 · standard (smallest that keeps quality) | ~12 pages8,192 tokens | Runs | Fast~75 tok/s | 9.0 GBmeasured file | 1.6 GB | 0.8 GB | 11.4 GB | 46.8 GB |
| Q5 · a bit sharper, a bit bigger | ~12 pages8,192 tokens | Runs | Fast~66 tok/s | 10.5 GB | 1.6 GB | 0.8 GB | 12.9 GB | 46.8 GB |
| Q8 · near full quality, almost twice Q4 | ~12 pages8,192 tokens | Runs | Fast~46 tok/s | 15.8 GB | 1.6 GB | 0.8 GB | 18.2 GB | 46.8 GB |
| FP16 · full quality, over three times Q4 | ~12 pages8,192 tokens | Runs | Fast~26 tok/s | 29.5 GB | 1.6 GB | 0.8 GB | 32.0 GB | 46.8 GB |
| Q4 · standard, longer conversation | ~49 pages32,768 tokens | Runs | Fast~52 tok/s | 9.0 GBmeasured file | 6.4 GB | 0.8 GB | 16.2 GB | 46.8 GB |
Sources and method
Memory and speed on this page are our estimate, not a measured test. Checked . unsloth Q4_K_M GGUF.
DeepSeek's dense reasoning distill of DeepSeek-R1 onto Qwen2.5-14B, MIT licence. Hugging Face safetensors metadata lists 14,770,033,664 parameters (14.77B). 48 layers, 8 KV heads, head dim 128, context 131,072. The Q4_K_M GGUF is 8.99 GB (unsloth, 24 Oct 2026). At 8,192 tokens the rounded parts are 8.99 GB weights + 1.61 GB conversation memory + 0.80 GB working space = 11.40 GB, which equals the 11.4 GB usable line of a 12 GB card. The unrounded sum is a fraction over 11.4 GB, so the fit table does not mark Q4 as a full fit on 12 GB. IQ4_XS is an 8.12 GB file, about 10.53 GB in total at 8,192 tokens (our estimate), and that fits a 12 GB card. Only the 2-bit IQ2_XS file (4.70 GB, about 7.11 GB in total) fits an 8 GB card (about 7.4 GB usable). Ollama tag: deepseek-r1:14b.
Measured DeepSeek R1 Distill Qwen 14B on a Dual RTX 3090 yourself? Send us your llama-bench result.
Questions
- Can I run DeepSeek R1 Distill Qwen 14B on a Dual RTX 3090?
- Yes. DeepSeek R1 Distill Qwen 14B runs on a Dual RTX 3090, about 75 tokens per second (tok/s).
- How much memory does DeepSeek R1 Distill Qwen 14B need?
- About 11.4 GB at the standard quality (Q4) for a conversation of up to about 12 pages of text (8,192 tokens), by our estimate: 9.0 GB for the model file (measured), 1.6 GB to hold the conversation and 0.8 GB of working space.
- How fast is DeepSeek R1 Distill Qwen 14B on a Dual RTX 3090?
- Fast: about 75 tokens per second (tok/s). Answers appear faster than you can read them. This is our estimate, not a measured test.
Speeds and memory are our estimate from the hardware specs, not a measured test. Prices are dated and may change. How we calculate