NVIDIA GeForce RTX 3080 Ti
12 GB decides what fits. 912 GB/s decides how fast it runs once it does.
Open in the calculator →Best models for 12 GB, on every card that size →
Find one: Amazon ↗ · eBay (new and used) ↗Store links may pay us a commission. They never decide which card is suggested — the memory arithmetic does.
- Largest popular model that fitsQwen2.5-Coder-14B-Instruct15B params · Q4_K_M · needs 11.4 GB~61 tok/s Faster than you readOpen in the calculator →
- Best fast pickLLaDA2.0-mini16B params · Q4_K_M · needs 11.0 GB~579 tok/s Faster than you readOpen in the calculator →
- Most downloaded that fitsQwen3.5-9B9.7B params · Q4_K_M · needs 7.05 GB~128 tok/s Faster than you readOpen in the calculator →
At 8,192 tokens of context with the whole model in device memory. Speeds are estimates from memory bandwidth, not benchmarks run on this card.
147 of 320 fit entirely
147 of the 320 models the engine can size fit entirely in device memory at Q4_K_M where it is published, otherwise the nearest published format, and 8,192 tokens. 70 run with some layers on system memory (32 GB assumed), and 103 do not run at all.
Featured models · Q4_K_M at 8,192 tokens, including offload
| Model | Needs | Verdict | Decode | Calculator |
|---|---|---|---|---|
| Qwen3.5-2B2.3B parameters | 2.32 GB | fits | ~503 tok/s | Open → |
| NVIDIA-Nemotron-3-Nano-4B-BF164.0B parameters | 3.46 GB | fits | ~233 tok/s | Open → |
| Qwen3-VL-8B-Instruct8.8B parameters | 7.04 GB | fits | ~105 tok/s | Open → |
| Qwen3.5-9B9.7B parameters | 7.05 GB | fits | ~128 tok/s | Open → |
| gemma-4-12B-it12B parameters | 9.54 GB | fits | ~76 tok/s | Open → |
| gpt-oss-20b21B parameters | 13.8 GB | 4 of 24 layers on system RAM (2.13 GB) | ~102 tok/s with offload | Open → |
| Qwen3.8-27B28B parameters | 18.6 GB | 25 of 64 layers on system RAM (6.66 GB) | ~9.4 tok/s with offload | Open → |
| Qwen3.6-27B28B parameters | 18.6 GB | 25 of 64 layers on system RAM (6.66 GB) | ~9.4 tok/s with offload | Open → |
| Kimi-Linear-48B-A3B-Instruct49B parameters | 30.4 GB | 17 of 27 layers on system RAM (18.5 GB) | ~51 tok/s with offload | Open → |
| Qwen3.8-Flash-Next180B parameters | 111.6 GB | needs 101.3 GB of system RAM; 32 GB assumed | does not run | Open → |
| DeepSeek-V4-Flash-Vision-Exp305B parameters | 188.1 GB | needs 178.5 GB of system RAM; 32 GB assumed | does not run | Open → |
| GLM-5.3-Flash321B parameters | 198.3 GB | needs 188.5 GB of system RAM; 32 GB assumed | does not run | Open → |
These examples are selected from prominent labs using the catalogue’s latest Hugging Face 30-day downloads and repository-creation freshness signal, with newer releases guaranteed a place. Offloaded rows assume 32 GB of system RAM, and a speed is only shown for a row that runs. Fitting in memory is not the same as loading: whether the runtime and version you have supports each architecture and format on this machine has not been tested here. Each “Open” link carries the same model, format, context and RAM into the calculator.
Fits entirely in NVIDIA GeForce RTX 3080 Ti memory — 147 of 320 sized models
The most demanding model that fits is Qwen2.5-Coder-14B-Instruct at Q4_K_M: 11.4 GB of the 12 GB, leaving 0.60 GB spare.
Fully resident at 8,192 tokens (or the model’s own maximum, where that is shorter), offload off, at 912 GB/s. Each row is a run of the engine for this configuration; the rows start with current, prominent releases and “fits” is memory, not a tested runtime. Older or less prominent models remain available through this search and “Show all”.
Showing 40 of 147 models that fit.
| Model | Format | Needs | Spare | Decode | Downloads / 30d | Calculator |
|---|---|---|---|---|---|---|
| Qwen3.5-9BQwen · 9.7B params | Q4_K_M | 7.05 GB | 4.95 GB | ~128 tok/s | 9M | Open → |
| Qwen3.5-4BQwen · 4.7B params | Q4_K_M | 3.98 GB | 8.02 GB | ~230 tok/s | 7.8M | Open → |
| gemma-4-12B-itGoogle · 12B params | Q4_K_M | 9.54 GB | 2.46 GB | ~76 tok/s | 1.9M | Open → |
| Qwen3.5-2BQwen · 2.3B params | Q4_K_M | 2.32 GB | 9.68 GB | ~503 tok/s | 4.9M | Open → |
| gemma-4-E4B-itGoogle · 8.0B params | Q4_K_M | 5.86 GB | 6.14 GB | ~205 tok/s | 4.4M | Open → |
| NVIDIA-Nemotron-3-Nano-4B-BF16NVIDIA · 4.0B params | Q4_K_M | 3.46 GB | 8.54 GB | ~233 tok/s | 3.4M | Open → |
| gemma-4-E2B-itGoogle · 5.1B params | Q4_K_M | 4.02 GB | 7.98 GB | ~542 tok/s | 3M | Open → |
| Qwen3.5-0.8BQwen · 873M params | Q4_K_M | 1.46 GB | 10.5 GB | ~1101 tok/s | 2.6M | Open → |
| Qwen3-VL-8B-InstructQwen · 8.8B params | Q4_K_M | 7.04 GB | 4.96 GB | ~105 tok/s | 14.6M | Open → |
| North-Micro-Vision-InstructCohereLabs · 2.5B params | Q4_K_M | 2.99 GB | 9.01 GB | ~331 tok/s | 180.7K | Open → |
| granite-4.2-8bIBM · 8.8B params | Q4_K_M | 7.49 GB | 4.51 GB | ~96 tok/s | 128.4K | Open → |
| granite-4.1-3bIBM · 3.4B params | Q4_K_M | 3.72 GB | 8.28 GB | ~224 tok/s | 521.8K | Open → |
| LFM2.5-2.6BLiquidAI · 2.7B params | Q4_K_M | 2.61 GB | 9.39 GB | ~359 tok/s | 108.1K | Open → |
| Qwen3-VL-4B-InstructQwen · 4.4B params | Q4_K_M | 4.72 GB | 7.28 GB | ~167 tok/s | 3.5M | Open → |
| Qwen3-VL-2B-InstructQwen · 2.1B params | Q4_K_M | 3.05 GB | 8.95 GB | ~304 tok/s | 2.8M | Open → |
| granite-4.2-3bIBM · 3.7B params | Q4_K_M | 3.72 GB | 8.28 GB | ~224 tok/s | 50.6K | Open → |
| LFM2.5-230MLiquidAI · 230M params | Q4_K_M | 1.05 GB | 10.9 GB | ~2503 tok/s | 88.6K | Open → |
| Qwen3-0.6BQwen · 752M params | Q4_K_M | 2.22 GB | 9.78 GB | ~466 tok/s | 29.7M | Open → |
| granite-4.1-8bIBM · 8.8B params | Q4_K_M | 7.49 GB | 4.51 GB | ~96 tok/s | 179.3K | Open → |
| LFM2.5-VL-3BLiquidAI · 3.1B params | Q4_K_M | 2.85 GB | 9.15 GB | ~359 tok/s | 26.7K | Open → |
| MiMo-V2.6-Distill-Qwen-9BNEWXiaomiMiMo · 9.4B params | Q4_K_M | 6.90 GB | 5.10 GB | ~128 tok/s | 14.6K | Open → |
| Qwen3-4B-Instruct-2507Qwen · 4.0B params | Q4_K_M | 4.72 GB | 7.28 GB | ~167 tok/s | 3.7M | Open → |
| Ling-3.0-tinyinclusionAI · 7.9B params | Q4_K_M | 5.85 GB | 6.15 GB | ~723 tok/s | 17.7K | Open → |
| Qwen3-8BQwen · 8.2B params | Q4_K_M | 7.04 GB | 4.96 GB | ~105 tok/s | 10.7M | Open → |
| Olmo-3-7B-Instructallenai · 7.3B params | Q4_K_M | 7.96 GB | 4.04 GB | ~90 tok/s | 481.7K | Open → |
| Qwen3-4BQwen · 4.0B params | Q4_K_M | 4.51 GB | 7.49 GB | ~167 tok/s | 7.8M | Open → |
| LFM2.5-8B-A1BLiquidAI · 8.5B params | Q4_K_M | 6.06 GB | 5.94 GB | ~560 tok/s | 32.4K | Open → |
| LFM2.5-350MLiquidAI · 354M params | Q4_K_M | 1.13 GB | 10.9 GB | ~1616 tok/s | 69.9K | Open → |
| Hy-MT2-1.8BTencent · 2.0B params | Q4_K_M | 2.47 GB | 9.53 GB | ~372 tok/s | 28.9K | Open → |
| LLaDA2.0-miniinclusionAI · 16B params | Q4_K_M | 11.0 GB | 1.00 GB | ~579 tok/s | 217.2K | Open → |
| LFM2.5-1.2B-InstructLiquidAI · 1.2B params | Q4_K_M | 1.63 GB | 10.4 GB | ~596 tok/s | 119.2K | Open → |
| Ministral-3-14B-Instruct-2512Mistral AI · 14B params | Q4_K_M | 10.4 GB | 1.62 GB | ~67 tok/s | 251K | Open → |
| Qwen3-1.7BQwen · 2.0B params | Q4_K_M | 3.02 GB | 8.98 GB | ~304 tok/s | 3.1M | Open → |
| Nemotron-3.5-Content-SafetyNVIDIA · 4.3B params | Q4_K_M | 3.73 GB | 8.27 GB | ~223 tok/s | 14.1K | Open → |
| Hy-MT2-7BTencent · 8.0B params | Q4_K_M | 6.50 GB | 5.50 GB | ~109 tok/s | 15.4K | Open → |
| Qwen3-14BQwen · 15B params | Q4_K_M | 11.1 GB | 0.86 GB | ~63 tok/s | 2.7M | Open → |
| GLM-4.6V-Flashzai-org · 10B params | Q4_K_M | 7.45 GB | 4.55 GB | ~108 tok/s | 103.7K | Open → |
| Qwen2.5-VL-7B-InstructQwen · 8.3B params | Q4_K_M | 6.36 GB | 5.64 GB | ~128 tok/s | 5.8M | Open → |
| SmolLM3-3BHuggingFaceTB · 3.1B params | Q4_K_M | 3.46 GB | 8.54 GB | ~247 tok/s | 617K | Open → |
| Ministral-3-8B-Instruct-2512Mistral AI · 8.9B params | Q4_K_M | 7.14 GB | 4.86 GB | ~103 tok/s | 121.2K | Open → |
Sized at the format most people actually download, not at FP16. The catalogue ordered by downloads →
Rent an RTX 3080 Ti by the hour
What the NVIDIA GeForce RTX 3080 Ti costs on Vast.ai and RunPod right now, per machine, from one card to eight. Every price is read from the provider's own API.
Buy one or rent one?
Your price, your hours, your electricity. Everything else is arithmetic.
Watts start at the NVIDIA GeForce RTX 3080 Ti’s published board power, an upper bound: decoding rarely holds a card at its limit. Rent starts at the cheapest live price (Vast.ai). The electricity price is a placeholder — put yours in.
At 4 h a day, renting costs $204.40 a year.
- That is $17.03 a month, and nothing when the machine is stopped.
- Owning would cost $127.75 a year in electricity, on top of the price.
- Every $1,000 of purchase price takes 13.0 years of this use to earn back.
Enter the price you would pay to see the exact break-even.
On-demand prices for the whole machine. Vast hosts below 98% measured reliability are left out; RunPod’s Community Cloud is vetted third-party hosts and its Secure Cloud is data-centre capacity. Every rentable card compared.
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Find the NVIDIA GeForce RTX 3080 Ti: Amazon ↗ · eBay (new and used) ↗Store links may pay us a commission. They never decide which card is suggested — the memory arithmetic does.
The RTX 3080 Ti, model by model
One page per model: whether it fits this device, at which formats, and how fast.
The specification behind every figure
What the manufacturer publishes for this device, and the pages it was read from.
Manufacturer specification
| Memory scope | dedicated |
|---|---|
| Capacity | 12 GB |
| Published options | 12 GB |
| Bandwidth | 912 GB/s |
| Memory type | GDDR6X |
| Bus width | 384-bit |
| FP32 peak | Not published |
| Dense matrix peak | Not published without sparsity |
| Power | Not published |
Source ledger
Caveats
- NVIDIA does not publish this card's memory data rate or bandwidth. The figure is the arithmetic on the interface width and data rate ASUS publishes for the named card — a primary source for that SKU — and is labelled derived for that reason. Cards from other partners use the same memory specification unless their own page says otherwise.
- The partner's "OC Edition" overclock applies to the GPU clock, not to the memory data rate quoted.
- No board power is recorded: the partner page's power figure is for its own cooler and power design, not NVIDIA's reference.
- No dense matrix throughput is published for this device in a form this catalogue accepts, so no compute roof is priced and time to first token is withheld.
Published capacity is a hardware ceiling, not guaranteed free runtime memory. The calculator shows the runtime reserve separately rather than folding it into a single number.
Run the diagnostic on the NVIDIA GeForce RTX 3080 Ti →