AceGPT v2 32B Chat — Hardware Requirements & GPU Compatibility
Chat
AceGPT v2 32B Chat is a 32B-parameter open language model from asas-ai. It supports a context window of up to 32,768 tokens. At FP16 it needs about 64.84 GB of VRAM — see which GPUs and Macs can run it below.
15 downloads
11 likesSpecifications
- Publisher
- asas-ai
- Parameters
- 32B
- Architecture
- LlamaForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 152,064
- Release Date
- 2024-07-06
- License
- Apache 2.0
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HuggingFace
How Much VRAM Does AceGPT v2 32B Chat Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| FP16 | 16.00 | 64.8 GB | 72.9 GB | 64.00 GB | Full half-precision — baseline for inference |
Which GPUs Can Run AceGPT v2 32B Chat?
FP16 · 64.8 GBAceGPT v2 32B Chat (FP16) requires 64.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 85+ GB is recommended. Using the full 33K context window can add up to 8.0 GB, bringing total usage to 72.9 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run AceGPT v2 32B Chat?
FP16 · 64.8 GB5 devices with unified memory can run AceGPT v2 32B Chat, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
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Frequently Asked Questions
- How much VRAM does AceGPT v2 32B Chat need?
AceGPT v2 32B Chat requires 64.8 GB of VRAM at FP16. Full 33K context adds up to 8.0 GB (72.9 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 32B × 16 bits ÷ 8 = 64 GB
KV Cache + Overhead ≈ 0.8 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 8.9 GB (at full 33K context)
VRAM usage by quantization
FP1664.8 GBFP16 + full context72.9 GB- Can NVIDIA GeForce RTX 5090 run AceGPT v2 32B Chat?
No — AceGPT v2 32B Chat requires at least 64.8 GB at FP16, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- Can I run AceGPT v2 32B Chat on a Mac?
AceGPT v2 32B Chat requires at least 64.8 GB at FP16, 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 AceGPT v2 32B Chat locally?
Yes — AceGPT v2 32B Chat can run locally on consumer hardware. At FP16 quantization it needs 64.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is AceGPT v2 32B Chat?
At FP16, AceGPT v2 32B Chat can reach ~45 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 ÷ 64.8 × 0.55 = ~45 tok/s
Estimated speed at FP16 (64.8 GB)
~45 tok/s~34 tok/s~28 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of AceGPT v2 32B Chat?
At FP16, the download is about 64.00 GB.
- Which GPUs can run AceGPT v2 32B Chat?
No single consumer GPU has enough VRAM to run AceGPT v2 32B Chat at FP16 (64.8 GB). Multi-GPU or professional hardware is required.
- Which devices can run AceGPT v2 32B Chat?
5 devices with unified memory can run AceGPT v2 32B Chat at FP16 (64.8 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.