AM Thinking V1 — Hardware Requirements & GPU Compatibility
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95 downloads
204 likesSpecifications
- Publisher
- a-m-team
- Parameters
- 32.8B
- Architecture
- Qwen2ForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 152,064
- Release Date
- 2025-05-14
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does AM Thinking V1 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 66.4 GB | 100.2 GB | 65.53 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run AM Thinking V1?
BF16 · 66.4 GBAM Thinking V1 (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 131K context window can add up to 33.8 GB, bringing total usage to 100.2 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run AM Thinking V1?
BF16 · 66.4 GB5 devices with unified memory can run AM Thinking V1, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Related Models
Frequently Asked Questions
- How much VRAM does AM Thinking V1 need?
AM Thinking V1 requires 66.4 GB of VRAM at BF16. Full 131K context adds up to 33.8 GB (100.2 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 ≈ 34.7 GB (at full 131K context)
VRAM usage by quantization
BF1666.4 GBBF16 + full context100.2 GB- Can NVIDIA GeForce RTX 5090 run AM Thinking V1?
No — AM Thinking V1 requires at least 66.4 GB at BF16, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- Can I run AM Thinking V1 on a Mac?
AM Thinking V1 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 AM Thinking V1 locally?
Yes — AM Thinking V1 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 AM Thinking V1?
At BF16, AM Thinking V1 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 AM Thinking V1?
At BF16, the download is about 65.53 GB.
- Which GPUs can run AM Thinking V1?
No single consumer GPU has enough VRAM to run AM Thinking V1 at BF16 (66.4 GB). Multi-GPU or professional hardware is required.
- Which devices can run AM Thinking V1?
5 devices with unified memory can run AM Thinking V1 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.