Param 1 2.9B Instruct — Hardware Requirements & GPU Compatibility
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Param 1 2.9B Instruct is a 2.9B-parameter open language model from bharatgenai. It supports a context window of up to 8,192 tokens. At BF16 it needs about 6.29 GB of VRAM — see which GPUs and Macs can run it below.
1.4K downloads
19 likesSpecifications
- Publisher
- bharatgenai
- Parameters
- 2.9B
- Architecture
- ParamBharatGenForCausalLM
- Context Length
- 8,192 tokens
- Vocabulary Size
- 256,011
- Release Date
- 2026-02-24
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Param 1 2.9B Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 6.3 GB | 7.1 GB | 5.72 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Param 1 2.9B Instruct?
BF16 · 6.3 GBParam 1 2.9B Instruct (BF16) requires 6.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 9+ GB is recommended. Using the full 8K context window can add up to 0.8 GB, bringing total usage to 7.1 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run Param 1 2.9B Instruct?
BF16 · 6.3 GB33 devices with unified memory can run Param 1 2.9B Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does Param 1 2.9B Instruct need?
Param 1 2.9B Instruct requires 6.3 GB of VRAM at BF16. Full 8K context adds up to 0.8 GB (7.1 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 2.9B × 16 bits ÷ 8 = 5.7 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 1.4 GB (at full 8K context)
VRAM usage by quantization
BF166.3 GBBF16 + full context7.1 GB- Can I run Param 1 2.9B Instruct on a Mac?
Param 1 2.9B Instruct requires at least 6.3 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 Param 1 2.9B Instruct locally?
Yes — Param 1 2.9B Instruct can run locally on consumer hardware. At BF16 quantization it needs 6.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Param 1 2.9B Instruct?
At BF16, Param 1 2.9B Instruct can reach ~463 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~104 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 ÷ 6.3 × 0.55 = ~463 tok/s
Estimated speed at BF16 (6.3 GB)
~463 tok/s~104 tok/s~346 tok/s~287 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Param 1 2.9B Instruct?
At BF16, the download is about 5.72 GB.
- Which GPUs can run Param 1 2.9B Instruct?
35 consumer GPUs can run Param 1 2.9B Instruct at BF16 (6.3 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 28 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Param 1 2.9B Instruct?
33 devices with unified memory can run Param 1 2.9B Instruct at BF16 (6.3 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.