Lumma 0.6B Tool — Hardware Requirements & GPU Compatibility
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Lumma 0.6B Tool is a 649M-parameter open language model from FrontiersMind. It supports a context window of up to 12,288 tokens. At BF16 it needs about 1.78 GB of VRAM — see which GPUs and Macs can run it below.
89 downloads
5 likesSpecifications
- Publisher
- FrontiersMind
- Parameters
- 649M
- Architecture
- NandiForCausalLM
- Context Length
- 12,288 tokens
- Vocabulary Size
- 131,072
- Release Date
- 2026-07-17
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Lumma 0.6B Tool Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 1.8 GB | 2.7 GB | 1.30 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Lumma 0.6B Tool?
BF16 · 1.8 GBLumma 0.6B Tool (BF16) requires 1.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 3+ GB is recommended. Using the full 12K context window can add up to 0.9 GB, bringing total usage to 2.7 GB. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Lumma 0.6B Tool?
BF16 · 1.8 GB59 devices with unified memory can run Lumma 0.6B Tool, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomFrequently Asked Questions
- How much VRAM does Lumma 0.6B Tool need?
Lumma 0.6B Tool requires 1.8 GB of VRAM at BF16. Full 12K context adds up to 0.9 GB (2.7 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 649M × 16 bits ÷ 8 = 1.3 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 1.4 GB (at full 12K context)
VRAM usage by quantization
BF161.8 GBBF16 + full context2.7 GB- Can I run Lumma 0.6B Tool on a Mac?
Lumma 0.6B Tool requires at least 1.8 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 Lumma 0.6B Tool locally?
Yes — Lumma 0.6B Tool can run locally on consumer hardware. At BF16 quantization it needs 1.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Lumma 0.6B Tool?
At BF16, Lumma 0.6B Tool can reach ~2472 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~368 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 ÷ 1.8 × 0.65 = ~2921 tok/s
Estimated speed at BF16 (1.8 GB)
~2921 tok/s~368 tok/s~2921 tok/s~2472 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Lumma 0.6B Tool?
At BF16, the download is about 1.30 GB.
- Which GPUs can run Lumma 0.6B Tool?
50 consumer GPUs can run Lumma 0.6B Tool at BF16 (1.8 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 50 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Lumma 0.6B Tool?
59 devices with unified memory can run Lumma 0.6B Tool at BF16 (1.8 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, 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.