Param2 17B A2.4B Thinking — Hardware Requirements & GPU Compatibility
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2.2K downloads
53 likesSpecifications
- Publisher
- bharatgenai
- Parameters
- 17.2B
- Architecture
- Param2MoEForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 128,008
- Release Date
- 2026-03-13
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HuggingFace
How Much VRAM Does Param2 17B A2.4B Thinking Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 34.7 GB | 34.8 GB | 34.30 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Param2 17B A2.4B Thinking?
BF16 · 34.7 GBParam2 17B A2.4B Thinking (BF16) requires 34.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 46+ GB is recommended. Using the full 4K context window can add up to 0.1 GB, bringing total usage to 34.8 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Param2 17B A2.4B Thinking?
BF16 · 34.7 GB13 devices with unified memory can run Param2 17B A2.4B Thinking, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (36 GB).
Runs great
— Plenty of headroom
Related Models
Frequently Asked Questions
- How much VRAM does Param2 17B A2.4B Thinking need?
Param2 17B A2.4B Thinking requires 34.7 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 17.2B × 16 bits ÷ 8 = 34.3 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 0.5 GB (at full 4K context)
VRAM usage by quantization
BF1634.7 GBBF16 + full context34.8 GB- Can NVIDIA GeForce RTX 5090 run Param2 17B A2.4B Thinking?
No — Param2 17B A2.4B Thinking requires at least 34.7 GB at BF16, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- Can I run Param2 17B A2.4B Thinking on a Mac?
Param2 17B A2.4B Thinking requires at least 34.7 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 Param2 17B A2.4B Thinking locally?
Yes — Param2 17B A2.4B Thinking can run locally on consumer hardware. At BF16 quantization it needs 34.7 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Param2 17B A2.4B Thinking?
At BF16, Param2 17B A2.4B Thinking can reach ~84 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 ÷ 34.7 × 0.55 = ~84 tok/s
Estimated speed at BF16 (34.7 GB)
AMD Instinct MI300X~84 tok/sNVIDIA H100 SXM~63 tok/sAMD Instinct MI250X~52 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Param2 17B A2.4B Thinking?
At BF16, the download is about 34.30 GB.
- Which GPUs can run Param2 17B A2.4B Thinking?
No single consumer GPU has enough VRAM to run Param2 17B A2.4B Thinking at BF16 (34.7 GB). Multi-GPU or professional hardware is required.
- Which devices can run Param2 17B A2.4B Thinking?
13 devices with unified memory can run Param2 17B A2.4B Thinking at BF16 (34.7 GB), including Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB), Mac Studio M2 Ultra (192 GB), Mac Studio M4 Max (128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.