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.
Runs great on a 24 GB GPU
Runs from 8 GB (BF16 · 8K)Best on 24 GB (BF16 · ~104 tok/s · 8K)Macs from 16 GB (BF16 · ~22 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Param 1 2.9B Instruct
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve bharatgenai/Param-1-2.9B-Instruct --max-model-len 8192vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (4 GB headroom) · BF16
- Generation speed
- ~37 tok/s
- generation speed
- Cost per 1M output tokens
- $0.62
- per 1M output tokens
How Much VRAM Does Param 1 2.9B Instruct Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 6.3 GB | — | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 8K context · ~104 tok/s |
Selected BF16
Brain floating point 167.1GBat 8K context
- Weights
- 5.7 GB
- KV cache
- 1.1 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 16.9 GB free · ~104 tok/s
≥50 tok/s needs ≥483 GB/s · RTX 5090 ~185, RTX 3090 Ti ~104
Which Hardware 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. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti. 151 devices with unified memory can run Param 1 2.9B Instruct, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), NVIDIA Jetson Orin Nano 8GB (Super).
≤ 11 GB
≤ 11 GB tier →fits · 0.9 GB free · up to 8K ctx
16 GPUs · 1 device family
RTX 3080~79Simulate output on RTX 3080RTX 2080 Ti~64Simulate output on RTX 2080 TiRTX 3070 Ti~63Simulate output on RTX 3070 TiOrin Nano~11Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)20 GB
fits · 12.9 GB free · up to 8K ctx
1 GPU
RX 7900 XT~76Simulate output on RX 7900 XT32–40 GB
fits · 24.9 GB free · up to 8K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~185Simulate output on RTX 5090M5 Max36 GB~51Simulate output on Mac Studio M5 Max (36 GB)M532 GB~17Simulate output on MacBook Air 13" M5 (32 GB)Orin~21Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~14Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at BF16
Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| LFM2.5 2.6BLiquid AI | — | 2.3 GB | ~321 | same size |
| SmolLM3 3BHugging Face | — | 3.4 GB | ~285 | same size |
| Phi 2Microsoft | — | 2.6 GB | ~248 | same size |
| Nemotron Flash 3BNVIDIA | — | 7.6 GB | ~109 | same size |
| Qwen2.5 3B InstructAlibaba | — | 2.8 GB | ~294 | same size |
| Stablelm 3B 4e1tStability AI | — | 3.3 GB | ~247 | same size |
Specifications
- Publisher
- bharatgenai
- Parameters
- 2.9B
- Architecture
- ParamBharatGenForCausalLM
- Context Length
- 8,192 tokens
- Vocabulary Size
- 256,011
- Release Date
- 2025-06-01
- License
- Apache 2.0
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)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 1.4 GB (at full 8K context)
VRAM usage by quantization
Can I run Param 1 2.9B Instruct on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Param 1 2.9B Instruct. Apple Silicon uses unified memory, so the model shares RAM with the system. At BF16 you need at least 6.3 GB of usable unified memory (RAM minus macOS overhead).
Can I run Param 1 2.9B Instruct locally?
Yes — Param 1 2.9B Instruct can run locally on consumer hardware. 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 ~763 tok/s on AMD Instinct MI350X. 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: NVIDIA B200 → 8000 ÷ 6.29 × 0.65 = ~827 tok/s
Estimated speed at BF16 (6.3 GB)
Real-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?
52 consumer GPUs can run Param 1 2.9B Instruct at BF16. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 40 GPUs have plenty of headroom for comfortable inference.
Which devices can run Param 1 2.9B Instruct?
151 devices with unified memory can run Param 1 2.9B Instruct at BF16 (6.3 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.