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NVIDIATuring

Best AI Models for NVIDIA TITAN RTX (24.0GB)

VRAM:24.0 GB GDDR6·Bandwidth:672.0 GB/s·CUDA Cores:4,608·TDP:280W·MSRP:$2,499

Turing 24 GB prosumer card with Tensor Cores; cheaper used than a 3090 but slower and no FP8/BF16.

24 GB is the enthusiast tier for running AI models locally. It comfortably handles 7B–13B models at high quality and opens the door to larger 30B models at moderate quantization.

This is one of the most popular memory tiers for local AI, found in GPUs like the RTX 4090 and RTX 3090. You can run Llama 3 8B, Mistral 7B, and Qwen 2.5 7B at Q5_K_M or Q6_K quality with fast token generation and generous context windows. Larger 14B models like DeepSeek R1 Distill fit comfortably at Q4_K_M. For even bigger models, 30B class runs at Q2–Q3, but 70B models are generally too heavy for single-GPU inference at this tier.

Runs Well

  • 7B models (Llama 3 8B, Mistral 7B) at Q5–Q8 quality
  • 13B–14B models at Q4–Q5 quality
  • Small models (3B–4B) at FP16 precision
  • Multimodal models like LLaVA 7B

Challenging

  • 30B models only at Q2–Q3 quantization
  • 70B models do not fit in VRAM
  • Large context windows with 14B+ models

What LLMs Can NVIDIA TITAN RTX Run?

105 models · 7 excellent · 15 good

Showing compatibility for NVIDIA TITAN RTX

LLM models compatible with NVIDIA TITAN RTX — ranked by performance
ModelVRAMGrade
Qwen3.6 27B27.8B
Q4_K_M·25.1 t/s tok/s·262K ctx·GREAT FIT
17.4 GBS88
Gemma 3 27B IT27.4B
Q4_K_M·24.1 t/s tok/s·131K ctx·GREAT FIT
18.1 GBS90
Gemma 4 26B A4B IT26.5B
Q4_K_M·26.4 t/s tok/s·262K ctx·GREAT FIT
16.6 GBS85
Gemma 2 27B IT27.2B
Q4_K_M·24.3 t/s tok/s·8K ctx·GREAT FIT
18.0 GBS90
Qwen3 Coder 30B A3B Instruct30.5B
Q4_K_M·23.3 t/s tok/s·262K ctx·GREAT FIT
18.7 GBS86
Qwen3 30B A3B Instruct 250730.5B
Q4_K_M·23.3 t/s tok/s·262K ctx·GREAT FIT
18.7 GBS86
Qwen3 30B A3B Thinking 250730.5B
Q4_K_M·23.3 t/s tok/s·262K ctx·GREAT FIT
18.7 GBS86
Mistral Small 3.2 24B Instruct 250624.0B
Q4_K_M·28.9 t/s tok/s·131K ctx·GOOD FIT
15.1 GBA80
Baichuan 7B7B
BF16·28.4 t/s tok/s·4K ctx·GOOD FIT
15.4 GBA81
GPT OSS 20B21.5B
Q4_K_M·32.9 t/s tok/s·131K ctx·GOOD FIT
13.3 GBA70
Phi 3 Small 8k Instruct7.4B
BF16·28.5 t/s tok/s·8K ctx·GOOD FIT
15.3 GBA81
Mistral Small 24B Instruct 250123.6B
Q4_K_M·29.4 t/s tok/s·33K ctx·GOOD FIT
14.9 GBA78
Magistral Small 250623.6B
Q4_K_M·29.4 t/s tok/s·41K ctx·GOOD FIT
14.9 GBA78
Internlm 7B7B
FP16·28.4 t/s tok/s·2K ctx·GOOD FIT
15.4 GBA81
Internlm Chat 20B20B
Q4_K_M·29.5 t/s tok/s·4K ctx·GOOD FIT
14.8 GBA78
Qwen3 32B32.8B
Q4_K_M·21.5 t/s tok/s·41K ctx·GOOD FIT
20.3 GBA70

NVIDIA TITAN RTX Specifications

Brand
NVIDIA
Architecture
Turing
Compute Capability
7.5 (CUDA SM version)
VRAM
24.0 GB GDDR6
Memory Bandwidth
672.0 GB/s
CUDA Cores
4,608
Tensor Cores
576
FP16 Performance
32.60 TFLOPS
TDP
280W
Release Date
2018-12-18
MSRP
$2,499

Get Started

Ollama (Recommended)

$curl -fsSL https://ollama.com/install.sh | sh
$ollama run llama3:8b

LM Studio

LM Studio

Download LM Studio, search for a model, and run it with one click.

GPUs to Consider Over NVIDIA TITAN RTX

Similar GPUs and upgrades with more VRAM or higher bandwidth for AI

NVIDIA GeForce RTX 5090

NVIDIA · Blackwell

32 GB
1792.0 GB/s21,760 CUDA575W TDP$1,999

NVIDIA GeForce RTX 3090 Ti

NVIDIA · Ampere

24 GB
1008.0 GB/s10,752 CUDA450W TDP$1,999

NVIDIA GeForce RTX 4090

NVIDIA · Ada Lovelace

24 GB
1008.0 GB/s16,384 CUDA450W TDP$1,599

AMD Radeon RX 7900 XTX

AMD · RDNA 3

24 GB
960.0 GB/s6,144 SP355W TDP$999

NVIDIA GeForce RTX 3090

NVIDIA · Ampere

24 GB
936.2 GB/s10,496 CUDA350W TDP$1,499

NVIDIA GeForce RTX 5090 Laptop GPU

NVIDIA · Blackwell

24 GB
896.0 GB/s10,496 CUDA150W TDP
All GPUs with 24+ GB VRAM →

Frequently Asked Questions

Can NVIDIA TITAN RTX run Qwen3.6 27B?

Yes, the NVIDIA TITAN RTX with 24 GB can run Qwen3.6 27B, Gemma 3 27B IT, Gemma 4 26B A4B IT, and 1264 other models. 69 models run at excellent quality, and 153 at good quality. Check the compatibility table above for the full list with VRAM usage and estimated speed.

Is NVIDIA TITAN RTX good for AI?

The NVIDIA TITAN RTX has 24 GB of GDDR6, making it excellent for running local AI models. It supports 222 models at good quality or better. With 672.0 GB/s memory bandwidth, it delivers solid token generation speeds. This is an enthusiast-grade GPU that handles most popular open-source LLMs.

How many parameters can NVIDIA TITAN RTX handle?

With 24 GB, the NVIDIA TITAN RTX supports models from 3B to 30B parameters depending on quantization level. At Q4_K_M (the recommended sweet spot), you can fit roughly 40B parameters. This means 7B models at high quality (Q6/Q8) or 30B+ models at Q4.

What quantization should I use on NVIDIA TITAN RTX?

For the best balance of quality and speed on the NVIDIA TITAN RTX, start with Q4_K_M — it preserves ~85% of the original model quality while keeping VRAM usage reasonable. With 24+ GB, you have the headroom to run 7B models at Q5_K_M or even Q6_K for noticeably better output quality. For larger 30B models, Q4_K_M remains the sweet spot.

How fast is NVIDIA TITAN RTX for AI inference?

With 672.0 GB/s memory bandwidth, the NVIDIA TITAN RTX achieves approximately 97 tokens/sec on a 7B model at Q4_K_M — that's very fast, well above conversational speed. A 14B model runs at ~49 tok/s. Token generation speed scales inversely with model size — smaller models are significantly faster.

tok/s = (672 GB/s ÷ model GB) × efficiency

Smaller models = faster inference. Memory bandwidth is the main bottleneck for token generation speed.

Estimated speed on NVIDIA TITAN RTX

~25 tok/s
~24 tok/s
~26 tok/s
~24 tok/s

Real-world results typically within ±20%. Speed depends on quantization kernel, batch size, and software stack.

Learn more about tok/s estimation →

What's the best model for NVIDIA TITAN RTX?

The top-rated models for the NVIDIA TITAN RTX are Qwen3.6 27B, Gemma 3 27B IT, Gemma 4 26B A4B IT. The best choice depends on your use case: coding assistants benefit from code-tuned models, while general chat works well with instruction-tuned models like Llama or Qwen.

What power supply and cooling does NVIDIA TITAN RTX need?

The NVIDIA TITAN RTX has a TDP of 280 W. A good rule of thumb is to provide at least double the GPU's TDP to cover the rest of the system — that means a 650 W PSU or larger. A mid-tower case with one intake and one rear exhaust is usually sufficient. Keep dust filters clean, as sustained inference generates continuous heat rather than the brief spikes typical of gaming.

Anything to watch out for with NVIDIA TITAN RTX?

Turing 24 GB prosumer card with Tensor Cores; cheaper used than a 3090 but slower and no FP8/BF16.