Falcon Mamba 7B — Hardware Requirements & GPU Compatibility
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Falcon Mamba 7B is a 7.3B-parameter open language model from TII UAE in the Falcon family. At Q4_K_M it needs about 4.80 GB of VRAM — see which GPUs and Macs can run it below.
139.5K downloads
243 likesSpecifications
- Publisher
- TII UAE
- Family
- Falcon
- Parameters
- 7.3B
- Architecture
- FalconMambaForCausalLM
- Vocabulary Size
- 65,024
- Release Date
- 2024-12-17
- License
- Other
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HuggingFace
How Much VRAM Does Falcon Mamba 7B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 3.4 GB | — | 3.09 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 3.5 GB | — | 3.18 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 3.9 GB | — | 3.55 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 4.8 GB | — | 4.36 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 5.7 GB | — | 5.18 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 6.6 GB | — | 6.00 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 8 GB | — | 7.27 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Falcon Mamba 7B?
Q4_K_M · 4.8 GBFalcon Mamba 7B (Q4_K_M) requires 4.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 7+ 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 Falcon Mamba 7B?
Q4_K_M · 4.8 GB33 devices with unified memory can run Falcon Mamba 7B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Derivatives (4)
Frequently Asked Questions
- How much VRAM does Falcon Mamba 7B need?
Falcon Mamba 7B requires 4.8 GB of VRAM at Q4_K_M, or 8 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 7.3B × 4.8 bits ÷ 8 = 4.4 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M4.8 GB- What's the best quantization for Falcon Mamba 7B?
For Falcon Mamba 7B, Q4_K_M (4.8 GB) offers the best balance of quality and VRAM usage. Q4_K_L (4.9 GB) provides better quality if you have the VRAM. The smallest option is IQ2_M at 2.7 GB.
VRAM requirement by quantization
IQ2_M2.7 GBIQ3_M3.6 GBQ4_K_S4.5 GBQ4_K_M ★4.8 GBQ5_K_S5.5 GBQ8_08.0 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Falcon Mamba 7B on a Mac?
Falcon Mamba 7B requires at least 2.7 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 Falcon Mamba 7B locally?
Yes — Falcon Mamba 7B can run locally on consumer hardware. At Q4_K_M quantization it needs 4.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Falcon Mamba 7B?
At Q4_K_M, Falcon Mamba 7B can reach ~607 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~137 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 ÷ 4.8 × 0.55 = ~607 tok/s
Estimated speed at Q4_K_M (4.8 GB)
~607 tok/s~137 tok/s~454 tok/s~376 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Falcon Mamba 7B?
At Q4_K_M, the download is about 4.36 GB. The full-precision Q8_0 version is 7.27 GB. The smallest option (IQ2_M) is 2.45 GB.
- Which GPUs can run Falcon Mamba 7B?
35 consumer GPUs can run Falcon Mamba 7B at Q4_K_M (4.8 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 Falcon Mamba 7B?
33 devices with unified memory can run Falcon Mamba 7B at Q4_K_M (4.8 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.