T Pro IT 2.0 — Hardware Requirements & GPU Compatibility
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T Pro IT 2.0 is a 32.8B-parameter open language model from t-tech. It supports a context window of up to 40,960 tokens. At Q4_K_M it needs about 20.29 GB of VRAM — see which GPUs and Macs can run it below.
Decent on a 24 GB GPU
Runs on 24 GB (Q4_K_M · ~32 tok/s · 16K)Macs from 24 GB (Q4_K_M · ~11 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run T Pro IT 2.0
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve t-tech/T-pro-it-2.0 --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on RTX PRO 4500 (32 GB) (9 GB headroom) · Q4_K_M
- Generation speed
- ~29 tok/s
- generation speed
- Cost per 1M output tokens
- $3.29
- per 1M output tokens
How Much VRAM Does T Pro IT 2.0 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 14.6 GB | 19.6 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 40K context · ~45 tok/s | |
| 16.6 GB | 21.6 GB | 3-bit medium — 3-bit medium quantization | Fits: 40K context · ~39 tok/s | |
| 20.3 GB | 25.3 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 16K context · ~32 tok/s | |
| 24.0 GB | 29 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 2K context · ~27 tok/s | |
| 27.7 GB | 32.7 GB | 6-bit, very good — 6-bit quantization, very good quality | Does not fit: needs 30 GB | |
| 33.4 GB | 38.4 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Does not fit: needs 35.7 GB | |
| 66.2 GB | 71.2 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Does not fit: needs 68.5 GB |
Selected Q4_K_M
4-bit medium quantization22.6GBat 16K context
- Weights
- 19.7 GB
- KV cache
- 2.7 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 1.4 GB free · ~32 tok/s
≥50 tok/s needs ≥1.56 TB/s · RTX 5090 ~57
Which Hardware Can Run T Pro IT 2.0?
Q4_K_M · 20.3 GBT Pro IT 2.0 (Q4_K_M) requires 20.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 27+ GB is recommended. Using the full 41K context window can add up to 6.4 GB, bringing total usage to 26.7 GB. 7 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 122 devices with unified memory can run T Pro IT 2.0, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Studio M1 Max (32 GB).
20 GB
Q4_K_M · best fit Q3_K_M (19.0 GB)
1 GPU
RX 7900 XT~29Simulate output on RX 7900 XT32–40 GB
fits · 9.4 GB free · up to 40K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~57Simulate output on RTX 5090M5 Max36 GB~16Simulate output on Mac Studio M5 Max (36 GB)M532 GB~5Simulate output on MacBook Air 13" M5 (32 GB)Orin~7Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~4Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| MiniCPM V 4.7 35B A3BOpenBMB | — | 22.1 GB | ~169 | same size |
| NVIDIA Nemotron 3.5 Lightning 30B A3B BF16NVIDIA | — | 19.3 GB | ~197 | same size |
| Agents A1InternScience | — | 21.5 GB | ~171 | same size |
| Ornith 1.0 35BDeep Reinforce | — | 21.5 GB | ~171 | same size |
| EXAONE 4.5 33BLGAI-EXAONE | — | 23.9 GB | ~33 | same size |
| Gemma 4 31BGoogle | — | 22.6 GB | ~31 | same size |
Specifications
- Publisher
- t-tech
- Parameters
- 32.8B
- Architecture
- Qwen3ForCausalLM
- Context Length
- 40,960 tokens
- Vocabulary Size
- 151,668
- Release Date
- 2025-07-17
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does T Pro IT 2.0 need?
T Pro IT 2.0 requires 20.3 GB of VRAM at Q4_K_M, or 66.2 GB at BF16. Full 41K context adds up to 6.4 GB (26.7 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 32.8B × 4.8 bits ÷ 8 = 19.7 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 7 GB (at full 41K context)
VRAM usage by quantization
Can NVIDIA GeForce RTX 4090 run T Pro IT 2.0?
Yes, at Q5_K_M (24.0 GB) or lower. Higher quantizations like Q6_K (27.7 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
What's the best quantization for T Pro IT 2.0?
For T Pro IT 2.0, Q4_K_M (20.3 GB) offers the best balance of quality and VRAM usage. Q5_K_M (24.0 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 14.6 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run T Pro IT 2.0 on a Mac?
Yes — Mac Mini M5 Pro (24 GB) and 106 other Macs can run T Pro IT 2.0. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 20.3 GB of usable unified memory (RAM minus macOS overhead).
Can I run T Pro IT 2.0 locally?
Yes — T Pro IT 2.0 can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is T Pro IT 2.0?
At Q4_K_M, T Pro IT 2.0 can reach ~237 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~32 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 20.3 × 0.65 = ~256 tok/s
Estimated speed at Q4_K_M (20.3 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of T Pro IT 2.0?
At Q4_K_M, the download is about 19.66 GB. The full-precision BF16 version is 65.52 GB. The smallest option (Q2_K) is 13.92 GB.
Which GPUs can run T Pro IT 2.0?
7 consumer GPUs can run T Pro IT 2.0 at Q4_K_M. Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090. 1 GPU has plenty of headroom for comfortable inference.
Which devices can run T Pro IT 2.0?
122 devices with unified memory can run T Pro IT 2.0 at Q4_K_M (20.3 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.