Qwen3.5 9B Instruct
Local OKExcellentA · 35
Best for: Quality, Coding, Reasoning·Pop: 86/100
Perf: est. 78 tok/s, typical error 5% · first token ~0.2s
Fits in 12 GB VRAM with room to spare. Best for quality, coding, reasoning on RTX 4070 Ti.
Best Local AI Models for RTX 4070 Ti (12GB)
The RTX 4070 Ti has 12GB of GDDR6X VRAM at 504 GB/s, the same memory as the RTX 4070. Token generation follows bandwidth, so it runs an 8B model at est. 73 tok/s, typical error 5%, level with the 4070 (est. 73 tok/s), and fits dense models up to 14B at Q4. The RTX 4070 Ti SUPER that followed it has 16GB at 672 GB/s.
Quick answer
The best local LLM for the RTX 4070 Ti is Qwen3.5 9B Instruct (est. 78 tok/s, typical error 5%) on its 12GB VRAM. It uses ~7GB of VRAM; the RTX 4070 Ti handles dense models up to ~14B at Q4. A 14B model at Q4 runs at est. 44 tok/s, typical error 15%. (ModelFit, 2026)
Sizing rule: a Q4 model needs about 0.6 GB of VRAM per billion parameters, and ModelFit budgets 90% of this card's 12GB for weights, context, and KV-cache. The per-size table below uses that same budget. Other strong fits: Gemma 4 12B (12B, ~8GB) and Ornith 1.0 9B (9B, ~5.6GB). On this card Qwen3.5 9B Instruct runs at est. 78 tok/s, typical error 5%. What it will not run: a 20B MoE (3.6B active) model does not fit in VRAM alone, so it spills to system RAM over PCIe and slows sharply.
$ollama run qwen3.5:9b
- Top pick
- Qwen3.5 9B Instruct
- Decode speed
- est. 78 tok/s, typical error 5%
- VRAM needed
- ~7 GB
Speeds are ModelFit estimates from memory bandwidth and model size, not measured benchmarks. Typical error: the median gap between the ModelFit speed model and measured runs on hardware the model was not fitted on, for this backend and model size.
Cite this page: ModelFit, RTX 4070 Ti VRAM: 12GB for Local LLMs, 14B at est. 44 tok/s, https://modelfit.io/gpu/rtx-4070-ti/, updated October 2026, CC BY 4.0.
Specs
VRAM
12 GB GDDR6X
Speed (8B Q4)
est. 73 tok/stypical error 5%
Bandwidth
504 GB/s
Architecture
Ada Lovelace
Max model size
Up to ~14B dense
Compatibility
10 excellent, 0 workable
Table 1
RTX 4070 Ti estimated tokens/sec by model size
Q4_K_M · ModelFit estimate| Model size | Est. speed | Fit on 12GB |
|---|---|---|
| 7B | est. 84 tok/stypical error 5% | Fits in VRAM |
| 14B | est. 44 tok/stypical error 15% | Fits in VRAM |
| 20B MoE (3.6B active) | est. 73 tok/srough estimate, offload not measured | CPU offload (slow) |
| 32B | est. 2 tok/srough estimate, offload not measured | CPU offload (slow) |
| 35B MoE (3B active) | est. 40 tok/srough estimate, offload not measured | CPU offload (slow) |
| 70B | est. 1 tok/srough estimate, offload not measured | CPU offload (slow) |
| 120B MoE (5.1B active) | est. 25 tok/srough estimate, offload not measured | CPU offload (slow) |
Fig. 1RTX 4070 Ti: estimated speed by model size
Decode speed, generic Q4_K_M model of each size at 4K context, 504 GB/s, typical error 5-15%
Fits in 12 GBCPU offload (slow)
7B84
14B44
20B MoE (3.6B active)73
32B2
35B MoE (3B active)40
70B1
120B MoE (5.1B active)25
tok/s est.ModelFit estimates, not measured benchmarks: decode speed of a generic Q4_K_M model of each size at 4K context on the RTX 4070 Ti's 504 GB/s bandwidth. MoE rows scale by active parameters (decode reads only the active experts), so a 35B MoE runs far faster than a dense 32B. "CPU offload" sizes exceed the 12GB VRAM; dense models slow to a crawl there, MoE models degrade less because hot experts stay GPU-resident. Offload rows are rough estimates: no measured run covers offload, so they carry no typical error. What typical error means.
Fig. 2
Context costs VRAM too.
Qwen3.5 9B Instruct loads ~7 GB of weights; at 16k context the KV cache adds ~0.5 GB (still fits the ~11 GB usable VRAM), and at 64k it adds ~2.0 GB (still fits).
Weights 7.0 GBKV cache · 16k context 0.5 GBHeadroom 3.3 GBRuntime reserve 1.2 GB
The model leaves about 3.3 GB of the usable VRAM budget free after weights and 16k context.
KV-cache figures assume an fp16 cache, the llama.cpp/Ollama default. Standard GQA models use a size-class estimate (8 KV heads x 128 head dim class); hybrid linear-attention models (Qwen3.5/3.6, Qwen3-Next) use the exact per-token cost from their published config, since only their sparse full-attention layers cache KV. A q8_0 KV cache roughly halves either figure. Estimates, not measurements.
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Where to buy the RTX 4070 Ti
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RTX 4070 Ti VRAM for AI: what actually fits?
ModelFit budgets about 10.8GB of the 12GB for weights and context. A 14B model at Q4 fits at est. 44 tok/s, typical error 15%, with little room left for long contexts; 7B-9B models leave plenty. Its extra compute over the RTX 4070 helps prompt reading, not decode speed. For 14B with long contexts or larger mixture-of-experts models, the 16GB RTX 4070 Ti SUPER (est. 94 tok/s, typical error 5% on 8B) is the step up.
Table 2
RTX 4070 Ti vs similar GPUs
| Hardware | Memory | Speed | Bandwidth |
|---|---|---|---|
| RTX 4070 Ti | 12 GB | est. 73 tok/stypical error 5% | 504 GB/s |
| RTX 4070 | 12 GB | est. 73 tok/stypical error 5% | 504 GB/s |
| RTX 5070 | 12 GB | est. 98 tok/stypical error 5% | 672 GB/s |
| RTX 4070 Ti SUPER | 16 GB | est. 94 tok/stypical error 5% | 672 GB/s |
| RTX 5070 Ti | 16 GB | est. 125 tok/stypical error 5% | 896 GB/s |
Recommended models
10 modelsGemma 4 12B
Local OKOKB · 26
Best for: Chat, Coding, Multimodal·Pop: 80/100
Perf: est. 52 tok/s, typical error 15% · first token ~0.3s
Fits in 12 GB VRAM with room to spare. Best for chat, coding, multimodal on RTX 4070 Ti.
Ornith 1.0 9B
Local OKExcellentA · 48
Best for: Agentic coding on small machines·Pop: 76/100
Perf: est. 78 tok/s, typical error 5% · first token ~0.2s
Fits in 12 GB VRAM with room to spare. Best for agentic coding on small machines on RTX 4070 Ti.
Mellum2.1 12B-A2.5B Thinking
Local OKOKB · 25
Best for: Agentic coding, Small machines·Pop: 76/100
Perf: est. 141 tok/s, typical error 15% · first token ~0.1s
Fits in 12 GB VRAM with room to spare. Best for agentic coding, small machines on RTX 4070 Ti.
Gemma 4 E4B (Q8)
Local OKExcellentA · 31
Best for: On-device, Mobile, Chat·Pop: 82/100
Perf: est. 79 tok/s, typical error 5% · first token ~1.1s
Fits in 12 GB VRAM with room to spare. Best for on-device, mobile, chat on RTX 4070 Ti.
LFM2.5 8B-A1B
Local OKExcellentA · 49
Best for: On-device agents, tool calling, multilingual chat·Pop: 72/100
Perf: est. 196 tok/s, typical error 15% · first token ~0.1s
Fits in 12 GB VRAM with room to spare. Best for on-device agents, tool calling, multilingual chat on RTX 4070 Ti.
NVIDIA Nemotron 3.5 Bolt E4B
Local OKOKB · 27
Best for: Voice assistants, Tool calling, Chat·Pop: 75/100
Perf: est. 98 tok/s, typical error 5% · first token ~0.2s
Fits in 12 GB VRAM with room to spare. Best for voice assistants, tool calling, chat on RTX 4070 Ti.
MiMo-V2.6-Distill-Qwen-9B
Local OKExcellentA · 46
Best for: Agentic coding, Small machines·Pop: 60/100
Perf: est. 76 tok/s, typical error 5% · first token ~0.2s
Fits in 12 GB VRAM with room to spare. Best for agentic coding, small machines on RTX 4070 Ti.
Granite Switch 4.1 8B
Local OKExcellentA · 41
Best for: Chat, Decisions, Safety checks·Pop: 64/100
Perf: est. 57 tok/s, typical error 5% · first token ~0.3s
Fits in 12 GB VRAM with room to spare. Best for chat, decisions, safety checks on RTX 4070 Ti.
Ministral 3 8B
Local OKExcellentA · 44
Best for: Chat, Coding, Vision·Pop: 74/100
Perf: est. 65 tok/s, typical error 5% · first token ~0.3s
Fits in 12 GB VRAM with room to spare. Best for chat, coding, vision on RTX 4070 Ti.
Video and image models on the RTX 4070 Ti
Runs · Stated: the linked source says a GPU with that much VRAM runs the model. Runs · Published: a dated run on the named card with that much VRAM. unknown: no source yet. Fits with offload: the main model weights fit in VRAM with a 15% margin; the text encoder and VAE run from system RAM, which is slower. Exceeds VRAM: even the main weights are too large for 12 GB. Every VRAM tier: video · image. Ranked with reasons: video models for 12 GB of VRAM · image models for 12 GB of VRAM.
Video
| Model | Fits | Quality | App |
|---|---|---|---|
| HunyuanVideo-1.5Tencent Hunyuan Community License | Runs · PublishedRTX 4070 · 12 GB VRAM · ComfyUI example workflow, 480p I2V distilled fp8, 81 frames · ComfyUI issue #10855 | #38 of 48 in text to video4,292 votessnapshot 2026-09-22 | ComfyUI |
| LTX-2.3LTX-2 Community License Agreement | Runs · PublishedRTX 4060 · 8 GB VRAM · ComfyUI GGUF workflow, 720p 5 s in about 190 s, 32 GB RAM · ComfyUI issue #14345 | No independent score yet | Lightricks/LTX-2 ltx-pipelines, ComfyUI |
| LTX-2.5LTX-2.x Community License Agreement | Runs · Stated12 GB VRAM · LTX Desktop on Windows, weight streaming, 40 GB RAM, 540p · LTX Desktop compatibility matrix (Lightricks) | No independent score yet | Lightricks/LTX-2 ltx-pipelines, ComfyUI |
| MiniMax H3MiniMax H3 Community License Agreement | Runs · Stated12 GB VRAM · Diffusers int8, streamed blocks, 960x544, about 75 GB system RAM · Diffusers docs: MiniMax-H3 memory | #8 of 48 in text to video11,417 votessnapshot 2026-09-22 | stable-diffusion.cpp, ComfyUI |
| Wan2.2 A14BApache License 2.0 | Runs · PublishedRTX 2060 · 6 GB VRAM · ComfyUI Q8 GGUF, 480x720, 81 frames, 7 to 10 min, 28 GB RAM plus a 60 GB page file · ComfyUI issue #12451 | #42 of 48 in text to video10,458 votessnapshot 2026-09-22 | ComfyUI |
| Wan2.2 TI2V 5BApache License 2.0 | Runs · Stated8 GB VRAM · ComfyUI native offloading · ComfyUI docs: Wan2.2Complete: Comfy-Org repackaged FP16 with FP8 scaled UMT518.1 GB download | No independent score yet | ComfyUI |
| Mochi 1 previewApache License 2.0 | unknownComplete: calcuis GGUF Q3_K_M with Q4_0 T5 and FP8 VAE7.7 GB download | #48 of 48 in text to video5,893 votessnapshot 2026-09-22 | Diffusers, ComfyUI |
Quality: blind human votes from Arena (arena.ai), CC BY 4.0, snapshot 2026-09-22. Scores are for the Arena checkpoint, not your local quantized build.
Image
| Model | Fits | Quality | App |
|---|---|---|---|
| FLUX 3 Image KleinFLUX Kommunity License v1.0 | Runs · Stated8 GB VRAM · official ComfyUI int8 files · FLUX 3 Image Klein README (Black Forest Labs) | No independent score yet | Diffusers |
| FLUX.1 [dev]FLUX.1 [dev] Non-Commercial License v1.1.1 | Runs · Stated6 GB VRAM · stable-diffusion.cpp with GGUF weights · stable-diffusion.cpp FLUX docs | #78 of 82 in text to image49,221 votessnapshot 2026-10-07 | Diffusers, ComfyUI |
| Krea 2 TurboKrea 2 Community License Agreement v.1 | Runs · PublishedRTX 4070 · 12 GB VRAM · ComfyUI fp8, dynamic VRAM paging, 8 steps · ComfyUI issue #15365 | No independent score yet | Diffusers, stable-diffusion.cpp |
| Qwen-Image-2.1Qwen Research License Agreement | Runs · PublishedRTX 5070 Ti Laptop · 12 GB VRAM · stable-diffusion.cpp Q6_K, offload to CPU, 1024x1024 · stable-diffusion.cpp issue #2118 | #19 of 82 in text to image11,441 votessnapshot 2026-10-07 | Diffusers, stable-diffusion.cpp |
| SDXL Base 1.0CreativeML Open RAIL++-M License | Runs · Stated8 GB VRAM · SDXL 1.0, no app named · SDXL 1.0 announcement (Stability AI) | No independent score yet | ComfyUI, stable-diffusion.cpp |
| Stable Diffusion 3.5 LargeStability AI Community License | Runs · Stated11 GB VRAM · FP8 TensorRT build on RTX 40 and 50 · Stability AI: SD3.5 TensorRT models | #81 of 82 in text to image23,396 votessnapshot 2026-10-07 | ComfyUI |
| Z-Image-TurboApache 2.0 | Runs · Stated4 GB VRAM · stable-diffusion.cpp with GGUF weights · stable-diffusion.cpp Z-Image docs | #59 of 82 in text to image24,808 votessnapshot 2026-10-07 | Diffusers, stable-diffusion.cpp |
| FLUX.2 [klein]Apache 2.0 (4B), FLUX Non-Commercial License (9B) | Fits with offloadComplete: Klein 4B Q8_0 GGUF12.5 GB download | No independent score yet | Diffusers, stable-diffusion.cpp |
| MOSS-ImageApache 2.0 | Fits with offloadComplete: UD-Q4_K_XL GGUF13.2 GB download | No independent score yet | stable-diffusion.cpp |
| FLUX.2 [dev]FLUX Non-Commercial License | Exceeds VRAMComplete: Q4_K_M GGUF34.8 GB download | #38 of 82 in text to image77,988 votessnapshot 2026-10-07 | Diffusers, ComfyUI, stable-diffusion.cpp |
Quality: blind human votes from Arena (arena.ai), CC BY 4.0, snapshot 2026-10-07. Scores are for the Arena checkpoint, not your local quantized build.
Similar GPUs for local AI
- RTX 4070 Ti SUPER16GB · est. 94 tok/s
- RTX 407012GB · est. 73 tok/s
- RTX 507012GB · est. 98 tok/s
- RTX 5070 Ti16GB · est. 125 tok/s
Speeds are estimates for Qwen3 8B Q4_K_M, typical error 5%.
Compatible model families
- QwenAlibaba Cloud: Qwen3.8 27B local flagship (Apache 2.0, vision)
- LlamaMeta: Most popular open-weight model family
- DeepSeekDeepSeek AI: V4.1 Flash: multimodal 552B MoE, API only
- MistralMistral AI: Excellent performance-per-parameter ratio
- GemmaGoogle DeepMind: Gemma 4 E4B: 4.5B effective, runs on 8GB Macs
- PhiMicrosoft: Best quality-per-gigabyte at small sizes
RTX 4070 Ti FAQ: common questions
How much VRAM does the RTX 4070 Ti have?
The RTX 4070 Ti has 12GB of GDDR6X VRAM at 504 GB/s. The RTX 4070 Ti SUPER is a different card with 16GB. ModelFit budgets about 10.8GB of the 4070 Ti's 12GB for weights and context, enough for dense models up to 14B at Q4.
What size LLM can I run on an RTX 4070 Ti?
Dense models up to 14B at Q4 quantization. Qwen3 8B (~6.5GB) runs at est. 73 tok/s, typical error 5% with room for context, and a 14B model at Q4 fits at est. 44 tok/s, typical error 15% with a shorter one. 27B and 32B dense models spill into system RAM.
RTX 4070 Ti vs RTX 4070 Ti SUPER for local LLMs?
The SUPER has 16GB against 12GB and 672 against 504 GB/s, so it holds larger models and runs 29% faster on 8B (est. 94 tok/s, typical error 5% vs est. 73 tok/s). For local AI the SUPER is the better card; the 4070 Ti makes sense if you already own one.
RTX 4070 Ti vs RTX 5070 for local AI?
Both have 12GB, so they load the same models. The RTX 5070 streams 672 GB/s of GDDR7 against 504 GB/s, which makes it 34% faster on 8B models (est. 98 tok/s, typical error 5% vs est. 73 tok/s).
How fast is a 27B-class model on the RTX 4070 Ti?
The RTX 4070 Ti's 12GB of VRAM cannot fit a 32B model comfortably. The largest size class it fits is 14B, at est. 44 tok/s, typical error 15%. The largest individual model in the catalog that fits is Ministral 3 14B (14B).
Related guides & benchmarks
More local AI guides
- Best LLM for 12GB VRAMEvery 12 GB card side by side, each with its best pick and speed.
- How Much RAM (or VRAM) Do You Need for a Local LLM?The model-size-to-memory matrix: what each VRAM and RAM tier actually runs.
- Best LLM for MacBook (Apple Silicon)Unified-memory Macs run bigger models per dollar. Picks by RAM tier, M1 to M5.
- Local AI image generator on MacCompare image models by RAM and app.
- Local image generation on MacImage models and apps by Mac RAM.
Browse all NVIDIA GPUs for AI
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