Clef Flash — Hardware Requirements & GPU Compatibility
Vision
Clef-Flash is Cloudflare's 9-billion-parameter multimodal decision model, post-trained from Qwen3.5-9B. It does not generate free-form text. It takes a state as text, JSON, images or video together with a schema of typed questions, and outputs a probability for each allowed option in one forward pass, using a small joint schema head on the backbone's hidden states. It is meant for classification, routing and other structured decisions, and the card describes it as the smaller, faster variant of Clef. At this size it fits on a single consumer GPU once quantized, although the custom head requires the repository's own loading code. The context length is 262,144 tokens. It is released under the Apache 2.0 license, permitting unrestricted commercial and research use. Published in September 2026, it is the smaller companion to the 27B Clef model.
1.3K downloads
273 likesSpecifications
- Publisher
- Cloudflare
- Parameters
- 9.4B
- Architecture
- Qwen3_5ForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-09-30
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Clef Flash Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 4.6 GB | 38.7 GB | 4.00 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 4.7 GB | 38.8 GB | 4.12 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 5.2 GB | 39.3 GB | 4.59 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 5.3 GB | 39.4 GB | 4.70 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 6.2 GB | 40.3 GB | 5.65 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 7.3 GB | 41.4 GB | 6.70 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 8.3 GB | 42.4 GB | 7.76 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 10.0 GB | 44.1 GB | 9.41 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Clef Flash?
Q4_K_M · 6.2 GBClef Flash (Q4_K_M) requires 6.2 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 262K context window can add up to 34.1 GB, bringing total usage to 40.3 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Clef Flash?
Q4_K_M · 6.2 GB58 devices with unified memory can run Clef Flash, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomWhere to Download Clef Flash
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
Frequently Asked Questions
- How much VRAM does Clef Flash need?
Clef Flash requires 6.2 GB of VRAM at Q4_K_M, or 19.4 GB at BF16. Full 262K context adds up to 34.1 GB (40.3 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 9.4B × 4.8 bits ÷ 8 = 5.6 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 34.7 GB (at full 262K context)
VRAM usage by quantization
Q4_K_M6.2 GBQ4_K_M + full context40.3 GB- What's the best quantization for Clef Flash?
For Clef Flash, Q4_K_M (6.2 GB) offers the best balance of quality and VRAM usage. Q4_K_L (6.3 GB) provides better quality if you have the VRAM. The smallest option is IQ2_M at 3.7 GB.
VRAM requirement by quantization
IQ2_M3.7 GBIQ3_M4.8 GBQ4_15.9 GBQ4_K_M ★6.2 GBQ4_K_L6.3 GBBF1619.4 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Clef Flash on a Mac?
Clef Flash requires at least 3.7 GB at IQ2_M, 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 Clef Flash locally?
Yes — Clef Flash can run locally on consumer hardware. At Q4_K_M quantization it needs 6.2 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Clef Flash?
At Q4_K_M, Clef Flash can reach ~773 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~106 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 ÷ 6.2 × 0.65 = ~837 tok/s
Estimated speed at Q4_K_M (6.2 GB)
~837 tok/s~106 tok/s~837 tok/s~773 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Clef Flash?
At Q4_K_M, the download is about 5.65 GB. The full-precision BF16 version is 18.82 GB. The smallest option (IQ2_M) is 3.18 GB.
- Which GPUs can run Clef Flash?
52 consumer GPUs can run Clef Flash at Q4_K_M (6.2 GB). 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 Clef Flash?
59 devices with unified memory can run Clef Flash at Q4_K_M (6.2 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.