Falcon3 10B Instruct 1.58bit — Hardware Requirements & GPU Compatibility
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829 downloads
23 likesSpecifications
- Publisher
- TII UAE
- Family
- Falcon
- Parameters
- 3.2B
- Architecture
- LlamaForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 131,072
- Release Date
- 2025-01-13
- License
- Other
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HuggingFace
How Much VRAM Does Falcon3 10B Instruct 1.58bit Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 7 GB | 12.0 GB | 6.36 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Falcon3 10B Instruct 1.58bit?
BF16 · 7 GBFalcon3 10B Instruct 1.58bit (BF16) requires 7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 10+ GB is recommended. Using the full 33K context window can add up to 5.0 GB, bringing total usage to 12.0 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080.
Runs great
— Plenty of headroom
Which Devices Can Run Falcon3 10B Instruct 1.58bit?
BF16 · 7 GB33 devices with unified memory can run Falcon3 10B Instruct 1.58bit, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does Falcon3 10B Instruct 1.58bit need?
Falcon3 10B Instruct 1.58bit requires 7 GB of VRAM at BF16. Full 33K context adds up to 5.0 GB (12.0 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 3.2B × 16 bits ÷ 8 = 6.4 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 5.6 GB (at full 33K context)
VRAM usage by quantization
BF167.0 GBBF16 + full context12.0 GB- Can I run Falcon3 10B Instruct 1.58bit on a Mac?
Falcon3 10B Instruct 1.58bit requires at least 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 Falcon3 10B Instruct 1.58bit locally?
Yes — Falcon3 10B Instruct 1.58bit can run locally on consumer hardware. At BF16 quantization it needs 7 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Falcon3 10B Instruct 1.58bit?
At BF16, Falcon3 10B Instruct 1.58bit can reach ~416 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~94 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 ÷ 7.0 × 0.55 = ~416 tok/s
Estimated speed at BF16 (7 GB)
AMD Instinct MI300X~416 tok/sNVIDIA GeForce RTX 4090~94 tok/sNVIDIA H100 SXM~311 tok/sAMD Instinct MI250X~258 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Falcon3 10B Instruct 1.58bit?
At BF16, the download is about 6.36 GB.
- Which GPUs can run Falcon3 10B Instruct 1.58bit?
35 consumer GPUs can run Falcon3 10B Instruct 1.58bit at BF16 (7 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 27 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Falcon3 10B Instruct 1.58bit?
33 devices with unified memory can run Falcon3 10B Instruct 1.58bit at BF16 (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.