LFM2.5 1.2B MEGABRAIN Thinking Claude Polaris Deepseek GLM — Hardware Requirements & GPU Compatibility
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41 downloads
7 likesSpecifications
- Publisher
- DavidAU
- Family
- DeepSeek
- Parameters
- 1.2B
- Architecture
- Lfm2ForCausalLM
- Context Length
- 128,000 tokens
- Vocabulary Size
- 65,536
- Release Date
- 2026-02-06
- License
- Apache 2.0
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How Much VRAM Does LFM2.5 1.2B MEGABRAIN Thinking Claude Polaris Deepseek GLM Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 2.7 GB | 6.8 GB | 2.34 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run LFM2.5 1.2B MEGABRAIN Thinking Claude Polaris Deepseek GLM?
BF16 · 2.7 GBLFM2.5 1.2B MEGABRAIN Thinking Claude Polaris Deepseek GLM (BF16) requires 2.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 4+ GB is recommended. Using the full 128K context window can add up to 4.1 GB, bringing total usage to 6.8 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run LFM2.5 1.2B MEGABRAIN Thinking Claude Polaris Deepseek GLM?
BF16 · 2.7 GB33 devices with unified memory can run LFM2.5 1.2B MEGABRAIN Thinking Claude Polaris Deepseek GLM, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does LFM2.5 1.2B MEGABRAIN Thinking Claude Polaris Deepseek GLM need?
LFM2.5 1.2B MEGABRAIN Thinking Claude Polaris Deepseek GLM requires 2.7 GB of VRAM at BF16. Full 128K context adds up to 4.1 GB (6.8 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 1.2B × 16 bits ÷ 8 = 2.3 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 4.5 GB (at full 128K context)
VRAM usage by quantization
BF162.7 GBBF16 + full context6.8 GB- Can I run LFM2.5 1.2B MEGABRAIN Thinking Claude Polaris Deepseek GLM on a Mac?
LFM2.5 1.2B MEGABRAIN Thinking Claude Polaris Deepseek GLM requires at least 2.7 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 LFM2.5 1.2B MEGABRAIN Thinking Claude Polaris Deepseek GLM locally?
Yes — LFM2.5 1.2B MEGABRAIN Thinking Claude Polaris Deepseek GLM can run locally on consumer hardware. At BF16 quantization it needs 2.7 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is LFM2.5 1.2B MEGABRAIN Thinking Claude Polaris Deepseek GLM?
At BF16, LFM2.5 1.2B MEGABRAIN Thinking Claude Polaris Deepseek GLM can reach ~1076 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~242 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 ÷ 2.7 × 0.55 = ~1076 tok/s
Estimated speed at BF16 (2.7 GB)
AMD Instinct MI300X~1076 tok/sNVIDIA GeForce RTX 4090~242 tok/sNVIDIA H100 SXM~804 tok/sAMD Instinct MI250X~665 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of LFM2.5 1.2B MEGABRAIN Thinking Claude Polaris Deepseek GLM?
At BF16, the download is about 2.34 GB.
- Which GPUs can run LFM2.5 1.2B MEGABRAIN Thinking Claude Polaris Deepseek GLM?
35 consumer GPUs can run LFM2.5 1.2B MEGABRAIN Thinking Claude Polaris Deepseek GLM at BF16 (2.7 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 LFM2.5 1.2B MEGABRAIN Thinking Claude Polaris Deepseek GLM?
33 devices with unified memory can run LFM2.5 1.2B MEGABRAIN Thinking Claude Polaris Deepseek GLM at BF16 (2.7 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.