NVIDIA GeForce RTX 4070
12 GB decides what fits. 504 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~34 tok/s Faster than you readOpen in the calculator →
- Best fast pickLLaDA2.0-mini16B params · Q4_K_M · needs 11.0 GB~320 tok/s Faster than you readOpen in the calculator →
- Most downloaded that fitsQwen3.5-9B9.7B params · Q4_K_M · needs 7.05 GB~71 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 | ~278 tok/s | Open → |
| NVIDIA-Nemotron-3-Nano-4B-BF164.0B parameters | 3.46 GB | fits | ~129 tok/s | Open → |
| Qwen3-VL-8B-Instruct8.8B parameters | 7.04 GB | fits | ~58 tok/s | Open → |
| Qwen3.5-9B9.7B parameters | 7.05 GB | fits | ~71 tok/s | Open → |
| gemma-4-12B-it12B parameters | 9.54 GB | fits | ~42 tok/s | Open → |
| gpt-oss-20b21B parameters | 13.8 GB | 4 of 24 layers on system RAM (2.13 GB) | ~80 tok/s with offload | Open → |
| Qwen3.8-27B28B parameters | 18.6 GB | 25 of 64 layers on system RAM (6.66 GB) | ~8.4 tok/s with offload | Open → |
| Qwen3.6-27B28B parameters | 18.6 GB | 25 of 64 layers on system RAM (6.66 GB) | ~8.4 tok/s with offload | Open → |
| Kimi-Linear-48B-A3B-Instruct49B parameters | 30.4 GB | 17 of 27 layers on system RAM (18.5 GB) | ~48 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 4070 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 504 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 | ~71 tok/s | 9M | Open → |
| Qwen3.5-4BQwen · 4.7B params | Q4_K_M | 3.98 GB | 8.02 GB | ~127 tok/s | 7.8M | Open → |
| gemma-4-12B-itGoogle · 12B params | Q4_K_M | 9.54 GB | 2.46 GB | ~42 tok/s | 1.9M | Open → |
| Qwen3.5-2BQwen · 2.3B params | Q4_K_M | 2.32 GB | 9.68 GB | ~278 tok/s | 4.9M | Open → |
| gemma-4-E4B-itGoogle · 8.0B params | Q4_K_M | 5.86 GB | 6.14 GB | ~113 tok/s | 4.4M | Open → |
| NVIDIA-Nemotron-3-Nano-4B-BF16NVIDIA · 4.0B params | Q4_K_M | 3.46 GB | 8.54 GB | ~129 tok/s | 3.4M | Open → |
| gemma-4-E2B-itGoogle · 5.1B params | Q4_K_M | 4.02 GB | 7.98 GB | ~299 tok/s | 3M | Open → |
| Qwen3.5-0.8BQwen · 873M params | Q4_K_M | 1.46 GB | 10.5 GB | ~609 tok/s | 2.6M | Open → |
| Qwen3-VL-8B-InstructQwen · 8.8B params | Q4_K_M | 7.04 GB | 4.96 GB | ~58 tok/s | 14.6M | Open → |
| North-Micro-Vision-InstructCohereLabs · 2.5B params | Q4_K_M | 2.99 GB | 9.01 GB | ~183 tok/s | 180.7K | Open → |
| granite-4.2-8bIBM · 8.8B params | Q4_K_M | 7.49 GB | 4.51 GB | ~53 tok/s | 128.4K | Open → |
| granite-4.1-3bIBM · 3.4B params | Q4_K_M | 3.72 GB | 8.28 GB | ~124 tok/s | 521.8K | Open → |
| LFM2.5-2.6BLiquidAI · 2.7B params | Q4_K_M | 2.61 GB | 9.39 GB | ~198 tok/s | 108.1K | Open → |
| Qwen3-VL-4B-InstructQwen · 4.4B params | Q4_K_M | 4.72 GB | 7.28 GB | ~93 tok/s | 3.5M | Open → |
| Qwen3-VL-2B-InstructQwen · 2.1B params | Q4_K_M | 3.05 GB | 8.95 GB | ~168 tok/s | 2.8M | Open → |
| granite-4.2-3bIBM · 3.7B params | Q4_K_M | 3.72 GB | 8.28 GB | ~124 tok/s | 50.6K | Open → |
| LFM2.5-230MLiquidAI · 230M params | Q4_K_M | 1.05 GB | 10.9 GB | ~1383 tok/s | 88.6K | Open → |
| Qwen3-0.6BQwen · 752M params | Q4_K_M | 2.22 GB | 9.78 GB | ~257 tok/s | 29.7M | Open → |
| granite-4.1-8bIBM · 8.8B params | Q4_K_M | 7.49 GB | 4.51 GB | ~53 tok/s | 179.3K | Open → |
| LFM2.5-VL-3BLiquidAI · 3.1B params | Q4_K_M | 2.85 GB | 9.15 GB | ~198 tok/s | 26.7K | Open → |
| MiMo-V2.6-Distill-Qwen-9BNEWXiaomiMiMo · 9.4B params | Q4_K_M | 6.90 GB | 5.10 GB | ~71 tok/s | 14.6K | Open → |
| Qwen3-4B-Instruct-2507Qwen · 4.0B params | Q4_K_M | 4.72 GB | 7.28 GB | ~93 tok/s | 3.7M | Open → |
| Ling-3.0-tinyinclusionAI · 7.9B params | Q4_K_M | 5.85 GB | 6.15 GB | ~400 tok/s | 17.7K | Open → |
| Qwen3-8BQwen · 8.2B params | Q4_K_M | 7.04 GB | 4.96 GB | ~58 tok/s | 10.7M | Open → |
| Olmo-3-7B-Instructallenai · 7.3B params | Q4_K_M | 7.96 GB | 4.04 GB | ~49 tok/s | 481.7K | Open → |
| Qwen3-4BQwen · 4.0B params | Q4_K_M | 4.51 GB | 7.49 GB | ~93 tok/s | 7.8M | Open → |
| LFM2.5-8B-A1BLiquidAI · 8.5B params | Q4_K_M | 6.06 GB | 5.94 GB | ~309 tok/s | 32.4K | Open → |
| LFM2.5-350MLiquidAI · 354M params | Q4_K_M | 1.13 GB | 10.9 GB | ~893 tok/s | 69.9K | Open → |
| Hy-MT2-1.8BTencent · 2.0B params | Q4_K_M | 2.47 GB | 9.53 GB | ~206 tok/s | 28.9K | Open → |
| LLaDA2.0-miniinclusionAI · 16B params | Q4_K_M | 11.0 GB | 1.00 GB | ~320 tok/s | 217.2K | Open → |
| LFM2.5-1.2B-InstructLiquidAI · 1.2B params | Q4_K_M | 1.63 GB | 10.4 GB | ~329 tok/s | 119.2K | Open → |
| Ministral-3-14B-Instruct-2512Mistral AI · 14B params | Q4_K_M | 10.4 GB | 1.62 GB | ~37 tok/s | 251K | Open → |
| Qwen3-1.7BQwen · 2.0B params | Q4_K_M | 3.02 GB | 8.98 GB | ~168 tok/s | 3.1M | Open → |
| Nemotron-3.5-Content-SafetyNVIDIA · 4.3B params | Q4_K_M | 3.73 GB | 8.27 GB | ~124 tok/s | 14.1K | Open → |
| Hy-MT2-7BTencent · 8.0B params | Q4_K_M | 6.50 GB | 5.50 GB | ~60 tok/s | 15.4K | Open → |
| Qwen3-14BQwen · 15B params | Q4_K_M | 11.1 GB | 0.86 GB | ~35 tok/s | 2.7M | Open → |
| GLM-4.6V-Flashzai-org · 10B params | Q4_K_M | 7.45 GB | 4.55 GB | ~60 tok/s | 103.7K | Open → |
| Qwen2.5-VL-7B-InstructQwen · 8.3B params | Q4_K_M | 6.36 GB | 5.64 GB | ~71 tok/s | 5.8M | Open → |
| SmolLM3-3BHuggingFaceTB · 3.1B params | Q4_K_M | 3.46 GB | 8.54 GB | ~136 tok/s | 617K | Open → |
| Ministral-3-8B-Instruct-2512Mistral AI · 8.9B params | Q4_K_M | 7.14 GB | 4.86 GB | ~57 tok/s | 121.2K | Open → |
Sized at the format most people actually download, not at FP16. The catalogue ordered by downloads →
Rent an RTX 4070 by the hour
What the NVIDIA GeForce RTX 4070 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 4070’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 $146.00 a year.
- That is $12.17 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 54.8 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.
Referral links Vast.ai, RunPod and Novita pay us a share of what you spend if you sign up through these buttons. It costs you nothing, and it never decides an order or a recommendation: both are computed from the live price and the speed, and options that pay us nothing are listed and recommended on the same terms. How we rank
Find the NVIDIA GeForce RTX 4070: 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 4070, model by model
One page per model: whether it fits this device, at which formats, and how fast.
- Qwen3-0.6B
- Qwen3-VL-8B-Instruct
- gemma-4-26B-A4B-it
- Qwen3-8B
- gemma-4-31B-it
- Qwen3.5-9B
- Qwen2.5-0.5B-Instruct
- Qwen2.5-7B-Instruct
- Qwen3.5-4B
- Qwen3-4B
- Qwen2.5-1.5B-Instruct
- Qwen3.8-27B
All 70Fewer
- gpt-oss-20b
- Qwen2.5-VL-7B-Instruct
- GLM-5.3-Flash
- Qwen3.5-2B
- dolphin-2.9.1-yi-1.5-34b
- DeepSeek-V4-Flash-0731
- gpt-oss-120b
- gemma-4-E4B-it
- Qwen2.5-3B-Instruct
- Qwen3-32B
- Qwen3-4B-Instruct-2507
- DeepSeek-V3.2
- Qwen3-VL-4B-Instruct
- pythia-160m
- NVIDIA-Nemotron-3-Nano-4B-BF16
- Qwen3.6-35B-A3B
- Qwen3-1.7B
- gemma-4-E2B-it
- OTel-2.0-LLM-31B-IT
- Qwen3-VL-2B-Instruct
- Qwen3-14B
- Qwen3.5-0.8B
- JiRackUltra_1b
- Qwen3.6-27B
- Qwen2.5-VL-3B-Instruct
- Qwen2.5-Coder-7B-Instruct
- Mistral-7B-Instruct-v0.3
- Qwen2.5-32B-Instruct
- gemma-4-12B-it
- Qwen3.5-27B
- SmolLM2-135M-Instruct
- SmolLM2-135M
- GLM-4.7-Flash
- Mistral-7B-Instruct-v0.2
- Qwen2.5-Coder-14B-Instruct
- Qwen2.5-14B-Instruct
- Qwen3.5-35B-A3B
- Qwen3-30B-A3B
- Qwen2.5-0.5B
- Ornith-1.0-35B
- pythia-70m-deduped
- DeepSeek-V3-0324
- Qwen3.8-Flash-Next
- GLM-5.3
- TinyLlama-1.1B-Chat-v1.0
- DeepSeek-V3
- Kimi-K3
- NVIDIA-Nemotron-3-Super-120B-A12B-BF16
- MiniMax-M2.7
- DeepSeek-V4-Flash
- MiniCPM5-2B
- DeepSeek-R1
- DeepSeek-R1-Distill-Qwen-1.5B
- Ornith-1.0-9B
- DeepSeek-V4-Flash-Vision-Exp
- DeepSeek-Coder-V2-Lite-Instruct
- Qwen2.5-Coder-32B-Instruct
- NVIDIA-Nemotron-3-Nano-30B-A3B-BF16
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 | 504 GB/s |
| Memory type | GDDR6X |
| Bus width | 192-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 4070 →