NVIDIA · Blackwell · 2025
NVIDIA GeForce RTX 5070 Ti for local AI
The best value in the RTX 50 lineup for 16GB of fast CUDA memory.
Updated · Speeds marked ~ are estimates; seemethodology
- VRAM
- 16GB
- Bandwidth
- 896 GB/s
- Power
- 300W
- Runs well
- 6 models
RTX 5070 Ti
Typically about $1,200
Can the RTX 5070 Ti run LLMs?
The RTX 5070 Ti has 16GB of GDDR7 and 896 GB/s of memory bandwidth, which decides how big a model fits and how fast it generates text. It comfortably runs models up to about 15B parameters: the largest in our catalog is Phi-4 at Q5_K_M (13GB with 8K context, ~63 tokens/sec). Our top all-round pick for it is Qwen3.8 27B (Q3_K_M, ~51 tokens/sec). Llama 3.3 70B Instruct only runs with partial CPU offload, at about 1.4 tokens/sec with CPU offload.
Recommendations
Best models for the RTX 5070 Ti
Best overall · also coding, reasoning, vision, largest that fits
Qwen3.8 27BQ3_K_M · 15GB · ~51 tokens/sec
Fits, tightRunner-up
Qwen3.5 9BQ8_0 · 12GB · ~62 tokens/sec
Runs wellFastest good model
Gemma 4 26B A4BQ3_K_M · 14GB · ~145 tokens/sec
Fits, tight
Compatibility
What runs on the RTX 5070 Ti
Every current model in our catalog, at the best quantization that fits in 16GB with 8K tokens of context. "Partial offload" means part of the model runs from system RAM (we assume 64GB), which is much slower.
| Model | Parameters | Best quant | Memory | Speed | Verdict |
|---|---|---|---|---|---|
| Gemma 4 E4B | 8B | Q8_0 | 10 GB | ~70 tok/s | Runs well |
| Qwen3.5 9B | 9B | Q8_0 | 12 GB | ~62 tok/s | Runs well |
| Phi-4 | 14.7B | Q5_K_M | 13 GB | ~63 tok/s | Runs well |
| gpt-oss-20b | 20.9B (3.6B active) | MXFP4 | 15 GB | ~125 tok/s | Fits, tight |
| Gemma 4 26B A4B | 26B (3.8B active) | Q3_K_M | 14 GB | ~145 tok/s | Fits, tight |
| Qwen3.8 27B | 27B | Q3_K_M | 15 GB | ~51 tok/s | Fits, tight |
| Gemma 4 31B | 31B | Q4_K_M | 22 GB | ~5.8 tok/s | Partial offload |
| Qwen3.6 35B-A3B | 35B (3B active) | Q4_K_M | 24 GB | ~48 tok/s | Partial offload |
| GLM-4.5-Air | 106B (12B active) | — | — | — | Won't fitWhat it needs |
| Llama 4 Scout | 109B (17B active) | — | — | — | Won't fitWhat it needs |
| gpt-oss-120b | 117B (5.1B active) | — | — | — | Won't fitWhat it needs |
| Qwen3.5 122B-A10B | 122B (10B active) | — | — | — | Won't fitWhat it needs |
| DeepSeek V4 Flash | 284B (13B active) | — | — | — | Won't fitWhat it needs |
| GLM-5.3-Flash | 320B (18B active) | — | — | — | Won't fitWhat it needs |
| GLM-5.3 | 744B (40B active) | — | — | — | Won't fitWhat it needs |
Performance
RTX 5070 Ti vs. alternatives on Qwen3.8 27B
Generation speed in tokens per second for a single user. Token generation is limited mostly by memory bandwidth, which is why bandwidth predicts these results so well.
RTX 5080
Q3_K_M · 16GB
~55tok/s
RTX 5070 Ti
Q3_K_M · 16GB
~51tok/s
RTX 4070 Ti Super
Q3_K_M · 16GB
~39tok/s
RX 9070 XT
Q3_K_M · 16GB
~30tok/s
Measured
RTX 5070 Ti benchmark results
| Model | Quant | Runtime | Generation | Prompt | Source |
|---|---|---|---|---|---|
| Llama 2 7B Chat | Q4_0 | llama.cpp (CUDA) | 175 tok/s | 6950 tok/s | llama.cpp discussion #15013: Performance of llama.cpp on NVIDIA CUDA · October 2026 |
| Llama 2 7B Chat | Q4_0 | llama.cpp (Vulkan) | 135 tok/s | 6215 tok/s | llama.cpp discussion #10879: Performance of llama.cpp with Vulkan · October 2026 |
Our take
Should you buy the RTX 5070 Ti for AI?
The RTX 5070 Ti shares the RTX 5080's GB203 chip, 16GB of GDDR7 and 256-bit bus, with memory running at 28 Gbps for 896 GB/s. That puts its token generation speed within about 7 percent of the 5080 for models that fit in 16GB. It runs 14B models at 8 bit and 24B models at 4 bit comfortably, and its 300W board power is manageable in most cases. Prices rose to about $1,200 new in October 2026, but it remains the most sensible 16GB NVIDIA card for LLM work.
What we like
- Nearly RTX 5080 bandwidth for less money
- 16GB of GDDR7 at 896 GB/s
- Full CUDA and FP4 support
- 300W is easier to power and cool than the 5080 or 5090
Watch out for
- 16GB rules out 32B dense models without offload
- Street price about $450 over MSRP
Best for: A fast first LLM card for 8B to 24B models · Dual-card builds with 32GB total
Specifications
RTX 5070 Ti specs that matter for AI
- Memory
- 16GB GDDR7
- Memory bandwidth
- 896 GB/s
- Memory bus
- 256-bit
- Architecture
- Blackwell
- Power
- 300W
- Interface
- PCIe 5.0 x16
- Released
- 2025
- Launch price
- $749
- Street price (new)
- ~$1,200 (2026-10)
- Street price (used)
- ~$1,150 (2026-10)
- Software
- CUDA, Vulkan
Cross-shop
Alternatives to the RTX 5070 Ti
Frequently asked questions
How much VRAM does the RTX 5070 Ti have?
The NVIDIA GeForce RTX 5070 Ti has 16GB of GDDR7 with 896 GB/s of memory bandwidth on a 256-bit bus.
What is the largest LLM the RTX 5070 Ti can run?
Fully in GPU memory, the largest model in our catalog that runs comfortably is Phi-4 (14.7B) at Q5_K_M, using about 13GB with 8K context at ~63 tokens/sec.
Can the RTX 5070 Ti run Llama 3.3 70B?
Only with partial CPU offload. At Q4_K_M the model needs about 47GB with 8K context, so part of it spills into system RAM and speed drops to about 1.4 tokens/sec with CPU offload. For usable 70B speeds you want 48GB of VRAM or more (for example two 24GB cards).
What is the best LLM to run on the RTX 5070 Ti?
Our top pick is Qwen3.8 27B at Q3_K_M, which fits in 16GB with room for 8K of context and runs at ~51 tokens/sec. See our full list of the best models for the RTX 5070 Ti for coding, reasoning, and vision picks.
Is the RTX 5070 Ti good for local AI in 2026?
The best value in the RTX 50 lineup for 16GB of fast CUDA memory.