AStar Thought QwQ 32B — Hardware Requirements & GPU Compatibility
ChatReasoning
11 downloads
1 likesSpecifications
- Publisher
- xxang
- Family
- QwQ
- Parameters
- 32.8B
- Architecture
- Qwen2ForCausalLM
- Context Length
- 40,960 tokens
- Vocabulary Size
- 152,064
- Release Date
- 2025-11-01
- License
- Other
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HuggingFace
How Much VRAM Does AStar Thought QwQ 32B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 66.4 GB | 76.6 GB | 65.53 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run AStar Thought QwQ 32B?
BF16 · 66.4 GBAStar Thought QwQ 32B (BF16) requires 66.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 87+ GB is recommended. Using the full 41K context window can add up to 10.2 GB, bringing total usage to 76.6 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run AStar Thought QwQ 32B?
BF16 · 66.4 GB5 devices with unified memory can run AStar Thought QwQ 32B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Related Models
Frequently Asked Questions
- How much VRAM does AStar Thought QwQ 32B need?
AStar Thought QwQ 32B requires 66.4 GB of VRAM at BF16. Full 41K context adds up to 10.2 GB (76.6 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 32.8B × 16 bits ÷ 8 = 65.5 GB
KV Cache + Overhead ≈ 0.9 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 11.1 GB (at full 41K context)
VRAM usage by quantization
BF1666.4 GBBF16 + full context76.6 GB- Can NVIDIA GeForce RTX 5090 run AStar Thought QwQ 32B?
No — AStar Thought QwQ 32B requires at least 66.4 GB at BF16, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- Can I run AStar Thought QwQ 32B on a Mac?
AStar Thought QwQ 32B requires at least 66.4 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 AStar Thought QwQ 32B locally?
Yes — AStar Thought QwQ 32B can run locally on consumer hardware. At BF16 quantization it needs 66.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is AStar Thought QwQ 32B?
At BF16, AStar Thought QwQ 32B can reach ~44 tok/s on AMD Instinct MI300X. 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 ÷ 66.4 × 0.55 = ~44 tok/s
Estimated speed at BF16 (66.4 GB)
AMD Instinct MI300X~44 tok/sNVIDIA H100 SXM~33 tok/sAMD Instinct MI250X~27 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of AStar Thought QwQ 32B?
At BF16, the download is about 65.53 GB.
- Which GPUs can run AStar Thought QwQ 32B?
No single consumer GPU has enough VRAM to run AStar Thought QwQ 32B at BF16 (66.4 GB). Multi-GPU or professional hardware is required.
- Which devices can run AStar Thought QwQ 32B?
5 devices with unified memory can run AStar Thought QwQ 32B at BF16 (66.4 GB), including Mac Pro M2 Ultra (192 GB), Mac Studio M2 Ultra (192 GB), Mac Studio M4 Max (128 GB), NVIDIA DGX A100 640GB. Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.