AceGPT v2 8B Chat — Hardware Requirements & GPU Compatibility
Chat
1.9K downloads
3 likesSpecifications
- Publisher
- FreedomIntelligence
- Parameters
- 8.0B
- Architecture
- LlamaForCausalLM
- Context Length
- 8,192 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2024-11-14
- License
- Apache 2.0
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HuggingFace
How Much VRAM Does AceGPT v2 8B Chat Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| FP16 | 16.00 | 16.6 GB | 17.4 GB | 16.06 GB | Full half-precision — baseline for inference |
Which GPUs Can Run AceGPT v2 8B Chat?
FP16 · 16.6 GBAceGPT v2 8B Chat (FP16) requires 16.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 22+ GB is recommended. Using the full 8K context window can add up to 0.8 GB, bringing total usage to 17.4 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 AceGPT v2 8B Chat?
FP16 · 16.6 GB21 devices with unified memory can run AceGPT v2 8B Chat, 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 AceGPT v2 8B Chat need?
AceGPT v2 8B Chat requires 16.6 GB of VRAM at FP16. Full 8K context adds up to 0.8 GB (17.4 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 8.0B × 16 bits ÷ 8 = 16.1 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 1.3 GB (at full 8K context)
VRAM usage by quantization
FP1616.6 GBFP16 + full context17.4 GB- Can I run AceGPT v2 8B Chat on a Mac?
AceGPT v2 8B Chat requires at least 16.6 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 8B Chat locally?
Yes — AceGPT v2 8B Chat can run locally on consumer hardware. At FP16 quantization it needs 16.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is AceGPT v2 8B Chat?
At FP16, AceGPT v2 8B Chat can reach ~175 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~39 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 ÷ 16.6 × 0.55 = ~175 tok/s
Estimated speed at FP16 (16.6 GB)
AMD Instinct MI300X~175 tok/sNVIDIA GeForce RTX 4090~39 tok/sNVIDIA H100 SXM~131 tok/sAMD Instinct MI250X~108 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 8B Chat?
At FP16, the download is about 16.06 GB.
- Which GPUs can run AceGPT v2 8B Chat?
6 consumer GPUs can run AceGPT v2 8B Chat at FP16 (16.6 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 AceGPT v2 8B Chat?
21 devices with unified memory can run AceGPT v2 8B Chat at FP16 (16.6 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.