Dhara 70M — Hardware Requirements & GPU Compatibility
ChatCode
Dhara 70M is a 71M-parameter open language model from codelion. It supports a context window of up to 1,024 tokens. At BF16 it needs about 0.49 GB of VRAM — see which GPUs and Macs can run it below.
357 downloads
47 likesSpecifications
- Publisher
- codelion
- Parameters
- 71M
- Architecture
- DharaForMaskedDiffusion
- Context Length
- 1,024 tokens
- Vocabulary Size
- 50,304
- Release Date
- 2025-12-30
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Dhara 70M Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 0.5 GB | — | 0.14 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Dhara 70M?
BF16 · 0.5 GBDhara 70M (BF16) requires 0.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 1+ GB is recommended. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Dhara 70M?
BF16 · 0.5 GB33 devices with unified memory can run Dhara 70M, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Dhara 70M need?
Dhara 70M requires 0.5 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 71M × 16 bits ÷ 8 = 0.1 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
BF160.5 GB- Can I run Dhara 70M on a Mac?
Dhara 70M requires at least 0.5 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 Dhara 70M locally?
Yes — Dhara 70M can run locally on consumer hardware. At BF16 quantization it needs 0.5 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Dhara 70M?
At BF16, Dhara 70M can reach ~5949 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~1337 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 ÷ 0.5 × 0.55 = ~5949 tok/s
Estimated speed at BF16 (0.5 GB)
~5949 tok/s~1337 tok/s~4447 tok/s~3678 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Dhara 70M?
At BF16, the download is about 0.14 GB.
- Which GPUs can run Dhara 70M?
35 consumer GPUs can run Dhara 70M at BF16 (0.5 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 Dhara 70M?
33 devices with unified memory can run Dhara 70M at BF16 (0.5 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.