Ling Flash 2.0 — Hardware Requirements & GPU Compatibility
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Ling-flash-2.0 is Inclusion AI's non-reasoning chat and code model, a mixture-of-experts system with about 102.9 billion total parameters but only roughly 6.15 billion activated per token (4.8 billion non-embedding), built on the Ling 2.0 architecture with a roughly 1/32 expert activation ratio, aux-loss-free routing, multi-token-prediction layers, and partial RoPE. Trained on more than 20 trillion tokens with supervised fine-tuning and multi-stage reinforcement learning, Inclusion AI reports it matches dense models of around 40 billion parameters on complex reasoning, code generation, and frontend-development benchmarks despite its small active-parameter count, while running several times faster thanks to its sparsity. Its small active-parameter footprint keeps generation fast, but all of its parameters must stay in memory, so it needs a multi-GPU setup or a high-memory workstation even once quantized. Context length is natively 32,768 tokens, extendable to 128,000 tokens with YaRN. It is released under the MIT license, permitting unrestricted commercial and research use. It was published in September 2025, alongside the reasoning-focused Ring-flash-2.0 built on the same base.
2.8K downloads
219 likesSpecifications
- Publisher
- Inclusion AI
- Parameters
- 102.9B
- Architecture
- BailingMoeV2ForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 157,184
- Release Date
- 2025-09-17
- License
- MIT
Get Started
HuggingFace
How Much VRAM Does Ling Flash 2.0 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 44.2 GB | 46.2 GB | 43.73 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 45.5 GB | 47.5 GB | 45.01 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 50.6 GB | 52.6 GB | 50.16 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 51.9 GB | 53.9 GB | 51.44 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 62.2 GB | 64.2 GB | 61.73 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 73.7 GB | 75.8 GB | 73.31 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 85.3 GB | 87.3 GB | 84.88 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 103.3 GB | 105.3 GB | 102.89 GB | 8-bit quantization, near-lossless |
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 Ling Flash 2.0?
Q4_K_M · 62.2 GBLing Flash 2.0 (Q4_K_M) requires 62.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 81+ GB is recommended. Using the full 33K context window can add up to 2.0 GB, bringing total usage to 64.2 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Ling Flash 2.0?
Q4_K_M · 62.2 GB22 devices with unified memory can run Ling Flash 2.0, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio (M3 Ultra, 96GB).
Runs great
— Plenty of headroom
Where to Download Ling Flash 2.0
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Benchmarks
Benchmark details →Frequently Asked Questions
- How much VRAM does Ling Flash 2.0 need?
Ling Flash 2.0 requires 62.2 GB of VRAM at Q4_K_M, or 206.2 GB at BF16. Full 33K context adds up to 2.0 GB (64.2 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 102.9B × 4.8 bits ÷ 8 = 61.7 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 2.5 GB (at full 33K context)
VRAM usage by quantization
Q4_K_M62.2 GBQ4_K_M + full context64.2 GB- Can NVIDIA GeForce RTX 5090 run Ling Flash 2.0?
Yes, at IQ2_XS (31.3 GB) or lower. Higher quantizations like IQ2_S (32.6 GB) exceed the NVIDIA GeForce RTX 5090's 32 GB.
- What's the best quantization for Ling Flash 2.0?
For Ling Flash 2.0, Q4_K_M (62.2 GB) offers the best balance of quality and VRAM usage. Q4_K_L (63.5 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 28.7 GB.
VRAM requirement by quantization
IQ2_XXS28.7 GBIQ3_XS42.9 GBQ4_051.9 GBQ4_K_M ★62.2 GBQ4_K_L63.5 GBBF16206.2 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Ling Flash 2.0 on a Mac?
Ling Flash 2.0 requires at least 28.7 GB at IQ2_XXS, 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 Ling Flash 2.0 locally?
Yes — Ling Flash 2.0 can run locally on consumer hardware. At Q4_K_M quantization it needs 62.2 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Ling Flash 2.0?
At Q4_K_M, Ling Flash 2.0 can reach ~139 tok/s on AMD Instinct MI350X. 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 ÷ 62.2 × 0.65 = ~404 tok/s
Estimated speed at Q4_K_M (62.2 GB)
~404 tok/s~404 tok/s~342 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Ling Flash 2.0?
At Q4_K_M, the download is about 61.73 GB. The full-precision BF16 version is 205.78 GB. The smallest option (IQ2_XXS) is 28.29 GB.
- Which GPUs can run Ling Flash 2.0?
No single consumer GPU has enough VRAM to run Ling Flash 2.0 at Q4_K_M (62.2 GB). Multi-GPU or professional hardware is required.
- Which devices can run Ling Flash 2.0?
23 devices with unified memory can run Ling Flash 2.0 at Q4_K_M (62.2 GB), including ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), 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.