Tema Q X2 Thinking — Hardware Requirements & GPU Compatibility
Chat
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.
46 downloads
3 likesSpecifications
- Publisher
- temaq-org
- Parameters
- 9.4B
- Architecture
- Qwen3_5ForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-03-06
Get Started
HuggingFace
How Much VRAM Does Tema Q X2 Thinking Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 19.4 GB | 53.5 GB | 18.82 GB | Brain floating point 16 — preferred for training |
Which GPUs 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 34.1 GB, bringing total usage to 53.5 GB. 6 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run Tema Q X2 Thinking?
BF16 · 19.4 GB21 devices with unified memory can run Tema Q X2 Thinking, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomRelated Models
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 34.1 GB (53.5 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)
KV Cache + Overhead ≈ 34.7 GB (at full 262K context)
VRAM usage by quantization
BF1619.4 GBBF16 + full context53.5 GB- Can I run Tema Q X2 Thinking on a Mac?
Tema Q X2 Thinking requires at least 19.4 GB at BF16, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Tema Q X2 Thinking locally?
Yes — Tema Q X2 Thinking can run locally on consumer hardware. At BF16 quantization it needs 19.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Tema Q X2 Thinking?
At BF16, Tema Q X2 Thinking can reach ~150 tok/s on AMD Instinct MI300X. 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: AMD Instinct MI300X → 5300 ÷ 19.4 × 0.55 = ~150 tok/s
Estimated speed at BF16 (19.4 GB)
~150 tok/s~34 tok/s~112 tok/s~93 tok/sReal-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?
6 consumer GPUs can run Tema Q X2 Thinking at BF16 (19.4 GB). Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XT, AMD Radeon RX 7900 XTX. 1 GPU have plenty of headroom for comfortable inference.
- Which devices can run Tema Q X2 Thinking?
21 devices with unified memory can run Tema Q X2 Thinking at BF16 (19.4 GB), including Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.