NVIDIA GeForce RTX 3080
10 GB decides what fits. 760 GB/s decides how fast it runs once it does.
Open in the calculator →Best models for 10 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 fitsgemma-4-12B-it12B params · Q4_K_M · needs 9.54 GB~63 tok/s Faster than you readOpen in the calculator →
- Best fast pickMistral-Nemo-Instruct-240712B params · Q4_K_M · needs 9.62 GB~61 tok/s Faster than you readOpen in the calculator →
- Most downloaded that fitsQwen3.5-9B9.7B params · Q4_K_M · needs 7.05 GB~106 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.
137 of 320 fit entirely
137 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. 80 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 | ~419 tok/s | Open → |
| NVIDIA-Nemotron-3-Nano-4B-BF164.0B parameters | 3.46 GB | fits | ~194 tok/s | Open → |
| Qwen3-VL-8B-Instruct8.8B parameters | 7.04 GB | fits | ~88 tok/s | Open → |
| Qwen3.5-9B9.7B parameters | 7.05 GB | fits | ~106 tok/s | Open → |
| gemma-4-12B-it12B parameters | 9.54 GB | fits | ~63 tok/s | Open → |
| gpt-oss-20b21B parameters | 13.8 GB | 8 of 24 layers on system RAM (4.25 GB) | ~62 tok/s with offload | Open → |
| Qwen3.8-27B28B parameters | 18.6 GB | 33 of 64 layers on system RAM (8.79 GB) | ~7.4 tok/s with offload | Open → |
| Qwen3.6-27B28B parameters | 18.6 GB | 33 of 64 layers on system RAM (8.79 GB) | ~7.4 tok/s with offload | Open → |
| Kimi-Linear-48B-A3B-Instruct49B parameters | 30.4 GB | 19 of 27 layers on system RAM (20.7 GB) | ~46 tok/s with offload | Open → |
| Qwen3.8-Flash-Next180B parameters | 111.6 GB | needs 103.6 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 memory — 137 of 320 sized models
The most demanding model that fits is UnslopNemo-12B-v4.1 at Q4_K_M: 9.62 GB of the 10 GB, leaving 0.38 GB spare.
Fully resident at 8,192 tokens (or the model’s own maximum, where that is shorter), offload off, at 760 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 137 models that fit.
| Model | Format | Needs | Spare | Decode | Downloads / 30d | Calculator |
|---|---|---|---|---|---|---|
| Qwen3.5-9BQwen · 9.7B params | Q4_K_M | 7.05 GB | 2.95 GB | ~106 tok/s | 9M | Open → |
| Qwen3.5-4BQwen · 4.7B params | Q4_K_M | 3.98 GB | 6.02 GB | ~191 tok/s | 7.8M | Open → |
| gemma-4-12B-itGoogle · 12B params | Q4_K_M | 9.54 GB | 0.46 GB | ~63 tok/s | 1.9M | Open → |
| Qwen3.5-2BQwen · 2.3B params | Q4_K_M | 2.32 GB | 7.68 GB | ~419 tok/s | 4.9M | Open → |
| gemma-4-E4B-itGoogle · 8.0B params | Q4_K_M | 5.86 GB | 4.14 GB | ~171 tok/s | 4.4M | Open → |
| NVIDIA-Nemotron-3-Nano-4B-BF16NVIDIA · 4.0B params | Q4_K_M | 3.46 GB | 6.54 GB | ~194 tok/s | 3.4M | Open → |
| gemma-4-E2B-itGoogle · 5.1B params | Q4_K_M | 4.02 GB | 5.98 GB | ~451 tok/s | 3M | Open → |
| Qwen3.5-0.8BQwen · 873M params | Q4_K_M | 1.46 GB | 8.54 GB | ~918 tok/s | 2.6M | Open → |
| Qwen3-VL-8B-InstructQwen · 8.8B params | Q4_K_M | 7.04 GB | 2.96 GB | ~88 tok/s | 14.6M | Open → |
| North-Micro-Vision-InstructCohereLabs · 2.5B params | Q4_K_M | 2.99 GB | 7.01 GB | ~276 tok/s | 180.7K | Open → |
| granite-4.2-8bIBM · 8.8B params | Q4_K_M | 7.49 GB | 2.51 GB | ~80 tok/s | 128.4K | Open → |
| granite-4.1-3bIBM · 3.4B params | Q4_K_M | 3.72 GB | 6.28 GB | ~187 tok/s | 521.8K | Open → |
| LFM2.5-2.6BLiquidAI · 2.7B params | Q4_K_M | 2.61 GB | 7.39 GB | ~299 tok/s | 108.1K | Open → |
| Qwen3-VL-4B-InstructQwen · 4.4B params | Q4_K_M | 4.72 GB | 5.28 GB | ~140 tok/s | 3.5M | Open → |
| Qwen3-VL-2B-InstructQwen · 2.1B params | Q4_K_M | 3.05 GB | 6.95 GB | ~253 tok/s | 2.8M | Open → |
| granite-4.2-3bIBM · 3.7B params | Q4_K_M | 3.72 GB | 6.28 GB | ~187 tok/s | 50.6K | Open → |
| LFM2.5-230MLiquidAI · 230M params | Q4_K_M | 1.05 GB | 8.95 GB | ~2086 tok/s | 88.6K | Open → |
| Qwen3-0.6BQwen · 752M params | Q4_K_M | 2.22 GB | 7.78 GB | ~388 tok/s | 29.7M | Open → |
| granite-4.1-8bIBM · 8.8B params | Q4_K_M | 7.49 GB | 2.51 GB | ~80 tok/s | 179.3K | Open → |
| LFM2.5-VL-3BLiquidAI · 3.1B params | Q4_K_M | 2.85 GB | 7.15 GB | ~299 tok/s | 26.7K | Open → |
| MiMo-V2.6-Distill-Qwen-9BNEWXiaomiMiMo · 9.4B params | Q4_K_M | 6.90 GB | 3.10 GB | ~106 tok/s | 14.6K | Open → |
| Qwen3-4B-Instruct-2507Qwen · 4.0B params | Q4_K_M | 4.72 GB | 5.28 GB | ~140 tok/s | 3.7M | Open → |
| Ling-3.0-tinyinclusionAI · 7.9B params | Q4_K_M | 5.85 GB | 4.15 GB | ~603 tok/s | 17.7K | Open → |
| Qwen3-8BQwen · 8.2B params | Q4_K_M | 7.04 GB | 2.96 GB | ~88 tok/s | 10.7M | Open → |
| Olmo-3-7B-Instructallenai · 7.3B params | Q4_K_M | 7.96 GB | 2.04 GB | ~75 tok/s | 481.7K | Open → |
| Qwen3-4BQwen · 4.0B params | Q4_K_M | 4.51 GB | 5.49 GB | ~140 tok/s | 7.8M | Open → |
| LFM2.5-8B-A1BLiquidAI · 8.5B params | Q4_K_M | 6.06 GB | 3.94 GB | ~466 tok/s | 32.4K | Open → |
| LFM2.5-350MLiquidAI · 354M params | Q4_K_M | 1.13 GB | 8.87 GB | ~1346 tok/s | 69.9K | Open → |
| Hy-MT2-1.8BTencent · 2.0B params | Q4_K_M | 2.47 GB | 7.53 GB | ~310 tok/s | 28.9K | Open → |
| LFM2.5-1.2B-InstructLiquidAI · 1.2B params | Q4_K_M | 1.63 GB | 8.37 GB | ~497 tok/s | 119.2K | Open → |
| Qwen3-1.7BQwen · 2.0B params | Q4_K_M | 3.02 GB | 6.98 GB | ~253 tok/s | 3.1M | Open → |
| Nemotron-3.5-Content-SafetyNVIDIA · 4.3B params | Q4_K_M | 3.73 GB | 6.27 GB | ~186 tok/s | 14.1K | Open → |
| Hy-MT2-7BTencent · 8.0B params | Q4_K_M | 6.50 GB | 3.50 GB | ~91 tok/s | 15.4K | Open → |
| GLM-4.6V-Flashzai-org · 10B params | Q4_K_M | 7.45 GB | 2.55 GB | ~90 tok/s | 103.7K | Open → |
| Qwen2.5-VL-7B-InstructQwen · 8.3B params | Q4_K_M | 6.36 GB | 3.64 GB | ~107 tok/s | 5.8M | Open → |
| SmolLM3-3BHuggingFaceTB · 3.1B params | Q4_K_M | 3.46 GB | 6.54 GB | ~206 tok/s | 617K | Open → |
| Ministral-3-8B-Instruct-2512Mistral AI · 8.9B params | Q4_K_M | 7.14 GB | 2.86 GB | ~86 tok/s | 121.2K | Open → |
| Ministral-3-3B-Instruct-2512Mistral AI · 3.8B params | Q4_K_M | 3.82 GB | 6.18 GB | ~171 tok/s | 114.1K | Open → |
| NVIDIA-Nemotron-Nano-9B-v2NVIDIA · 8.9B params | Q4_K_M | 6.54 GB | 3.46 GB | ~90 tok/s | 333.6K | Open → |
| Qwen3-VL-8B-ThinkingQwen · 8.8B params | Q4_K_M | 7.04 GB | 2.96 GB | ~88 tok/s | 144.2K | Open → |
Sized at the format most people actually download, not at FP16. The catalogue ordered by downloads →
Rent an RTX 3080 by the hour
What the NVIDIA GeForce RTX 3080 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’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 $116.80 a year.
- That is $9.73 a month, and nothing when the machine is stopped.
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- At this usage the electricity alone costs more than renting.
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: 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, 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 | 10 GB |
| Published options | 10 GB |
| Bandwidth | 760 GB/s |
| Memory type | GDDR6X |
| Bus width | 320-bit |
| FP32 peak | Not published |
| Dense matrix peak | Not published without sparsity |
| Power | Not published |
Source ledger
Caveats
- Memory bandwidth is quoted from NVIDIA's RTX Blackwell architecture whitepaper, whose appendix compares each 50-series card against its predecessors. NVIDIA's web specification pages no longer publish it for this generation.
- No peak throughput figure is entered for this card, so no compute roof is priced for it and time to first token is withheld rather than estimated.
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 →