AceMath 1.5B Instruct — Hardware Requirements & GPU Compatibility
ChatMath
AceMath 1.5B Instruct is a 1.8B-parameter open language model from NVIDIA. It supports a context window of up to 4,096 tokens. At BF16 it needs about 3.91 GB of VRAM — see which GPUs and Macs can run it below.
1.4K downloads
17 likesSpecifications
- Publisher
- NVIDIA
- Parameters
- 1.8B
- Architecture
- Qwen2ForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2025-01-17
- License
- CC BY-NC 4.0
Get Started
HuggingFace
How Much VRAM Does AceMath 1.5B Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 3.9 GB | 4.0 GB | 3.55 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run AceMath 1.5B Instruct?
BF16 · 3.9 GBAceMath 1.5B Instruct (BF16) requires 3.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 6+ GB is recommended. Using the full 4K context window can add up to 0.1 GB, bringing total usage to 4.0 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run AceMath 1.5B Instruct?
BF16 · 3.9 GB33 devices with unified memory can run AceMath 1.5B Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does AceMath 1.5B Instruct need?
AceMath 1.5B Instruct requires 3.9 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 1.8B × 16 bits ÷ 8 = 3.6 GB
KV Cache + Overhead ≈ 0.3 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 0.4 GB (at full 4K context)
VRAM usage by quantization
BF163.9 GBBF16 + full context4.0 GB- Can I run AceMath 1.5B Instruct on a Mac?
AceMath 1.5B Instruct requires at least 3.9 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 AceMath 1.5B Instruct locally?
Yes — AceMath 1.5B Instruct can run locally on consumer hardware. At BF16 quantization it needs 3.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is AceMath 1.5B Instruct?
At BF16, AceMath 1.5B Instruct can reach ~746 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~168 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 ÷ 3.9 × 0.55 = ~746 tok/s
Estimated speed at BF16 (3.9 GB)
~746 tok/s~168 tok/s~557 tok/s~461 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of AceMath 1.5B Instruct?
At BF16, the download is about 3.55 GB.
- Which GPUs can run AceMath 1.5B Instruct?
35 consumer GPUs can run AceMath 1.5B Instruct at BF16 (3.9 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 35 GPUs have plenty of headroom for comfortable inference.
- Which devices can run AceMath 1.5B Instruct?
33 devices with unified memory can run AceMath 1.5B Instruct at BF16 (3.9 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.