Tema Q X2 Thinking — Hardware Requirements & GPU Compatibility
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Tema Q X2 Thinking is a 9.4B-parameter open language model from temaq-org. It supports a context window of up to 262,144 tokens. At BF16 it needs about 19.39 GB of VRAM — see which GPUs and Macs can run it below.
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Tema Q X2 Thinking
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
vllm serve temaq-org/Tema_Q-X2-Thinking --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3090 (24 GB) (4 GB headroom) · BF16
- Generation speed
- ~31 tok/s
- generation speed
- Cost per 1M output tokens
- $1.50
- per 1M output tokens
How Much VRAM Does Tema Q X2 Thinking Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 19.4 GB | 20.2 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 128K context · ~34 tok/s |
Selected BF16
Brain floating point 1619.7GBat 16K context
- Weights
- 18.8 GB
- KV cache
- 0.5 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 4.3 GB free · ~34 tok/s
≥50 tok/s needs ≥1.49 TB/s · RTX 5090 ~60
Which Hardware Can Run Tema Q X2 Thinking?
BF16 · 19.4 GBTema Q X2 Thinking (BF16) requires 19.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 26+ GB is recommended. Using the full 262K context window can add up to 8.3 GB, bringing total usage to 27.7 GB. 8 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 122 devices with unified memory can run Tema Q X2 Thinking, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Studio M1 Max (32 GB).
20 GB
fits · 0.3 GB free · up to 16K ctx
1 GPU
RX 7900 XT~25Simulate output on RX 7900 XT32–40 GB
fits · 12.3 GB free · up to 256K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~60Simulate output on RTX 5090M5 Max36 GB~17Simulate output on Mac Studio M5 Max (36 GB)M532 GB~6Simulate output on MacBook Air 13" M5 (32 GB)Orin~7Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~5Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at BF16
Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Qwen3.5 9BAlibaba | 99.4 | 6.6 GB | ~103 | same size |
| Granite Switch 4.1 8BIBM | — | 8.4 GB | ~111 | same size |
| Ornith 1.5 9Bornith-ai | — | 6.6 GB | ~103 | same size |
| Apertus V1.5 8Bswiss-ai | — | 8.5 GB | ~111 | same size |
| MiniCPM O 4 5OpenBMB | — | 8.3 GB | ~105 | same size |
| Qwen3 VL 8B InstructAlibaba | — | 8 GB | ~112 | same size |
Specifications
- Publisher
- temaq-org
- Parameters
- 9.4B
- Architecture
- Qwen3_5ForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-03-05
Frequently Asked Questions
How much VRAM does Tema Q X2 Thinking need?
Tema Q X2 Thinking requires 19.4 GB of VRAM at BF16. Full 262K context adds up to 8.3 GB (27.7 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 9.4B × 16 bits ÷ 8 = 18.8 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 ≈ 8.9 GB (at full 262K context)
VRAM usage by quantization
Can I run Tema Q X2 Thinking on a Mac?
Yes — Mac Mini M5 Pro (24 GB) and 106 other Macs can run Tema Q X2 Thinking. Apple Silicon uses unified memory, so the model shares RAM with the system. At BF16 you need at least 19.4 GB of usable unified memory (RAM minus macOS overhead).
Can I run Tema Q X2 Thinking locally?
Yes — Tema Q X2 Thinking can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Tema Q X2 Thinking?
At BF16, Tema Q X2 Thinking can reach ~248 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~34 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 ÷ 19.4 × 0.65 = ~268 tok/s
Estimated speed at BF16 (19.4 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Tema Q X2 Thinking?
At BF16, the download is about 18.82 GB.
Which GPUs can run Tema Q X2 Thinking?
8 consumer GPUs can run Tema Q X2 Thinking at BF16. Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XT, AMD Radeon RX 7900 XTX. 1 GPU has plenty of headroom for comfortable inference.
Which devices can run Tema Q X2 Thinking?
122 devices with unified memory can run Tema Q X2 Thinking at BF16 (19.4 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.