TII UAE·Falcon·FalconForCausalLM

Falcon 40B Instruct — Hardware Requirements & GPU Compatibility

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Falcon 40B Instruct is a 40B-parameter open language model from TII UAE in the Falcon family. At Q4_K_M it needs about 26.40 GB of VRAM — see which GPUs and Macs can run it below.

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Specifications

Publisher
TII UAE
Family
Falcon
Parameters
40B
Architecture
FalconForCausalLM
Vocabulary Size
65,024
License
Apache 2.0

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How Much VRAM Does Falcon 40B Instruct Need?

Select a quantization to see compatible GPUs below.

QuantizationBitsVRAM
Q2_K3.4018.7 GB
Q3_K_S3.5019.3 GB
Q3_K_M3.9021.4 GB
Q4_04.0022 GB
Q4_K_M4.8026.4 GB
Q5_K_M5.7031.4 GB
Q6_K6.6036.3 GB
Q8_08.0044 GB

Which GPUs Can Run Falcon 40B Instruct?

Q4_K_M · 26.4 GB

Falcon 40B Instruct (Q4_K_M) requires 26.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 35+ GB is recommended. 1 GPU can run it, including NVIDIA GeForce RTX 5090.

All compatible consumer-level GPUs are running near their VRAM limit. You may also want to consider professional GPUs (e.g., NVIDIA A100, H100) which offer significantly more VRAM. For more headroom and better throughput, consider a multi-GPU configuration with tensor parallelism (supported by tools like vLLM, llama.cpp, or text-generation-inference).

Decent

Enough VRAM, may be tight

Which Devices Can Run Falcon 40B Instruct?

Q4_K_M · 26.4 GB

15 devices with unified memory can run Falcon 40B Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (36 GB).

Benchmarks

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Related Models

Frequently Asked Questions

How much VRAM does Falcon 40B Instruct need?

Falcon 40B Instruct requires 26.4 GB of VRAM at Q4_K_M, or 44 GB at Q8_0.

VRAM = Weights + KV Cache + Overhead

Weights = 40B × 4.8 bits ÷ 8 = 24 GB

KV Cache + Overhead 2.4 GB (at 2K context + ~0.3 GB framework)

VRAM usage by quantization

26.4 GB

Learn more about VRAM estimation →

Can NVIDIA GeForce RTX 4090 run Falcon 40B Instruct?

Yes, at IQ4_XS (23.6 GB) or lower. Higher quantizations like Q4_K_S (24.8 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.

What's the best quantization for Falcon 40B Instruct?

For Falcon 40B Instruct, Q4_K_M (26.4 GB) offers the best balance of quality and VRAM usage. Q5_K_S (30.3 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 12.1 GB.

VRAM requirement by quantization

IQ2_XXS
12.1 GB
IQ3_XS
18.1 GB
Q3_K_M
21.4 GB
Q4_K_S
24.8 GB
Q4_K_M
26.4 GB
Q8_0
44.0 GB

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run Falcon 40B Instruct on a Mac?

Falcon 40B Instruct requires at least 12.1 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 Falcon 40B Instruct locally?

Yes — Falcon 40B Instruct can run locally on consumer hardware. At Q4_K_M quantization it needs 26.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is Falcon 40B Instruct?

At Q4_K_M, Falcon 40B Instruct can reach ~110 tok/s on AMD Instinct MI300X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.

tok/s = (bandwidth GB/s ÷ model GB) × efficiency

Example: AMD Instinct MI300X5300 ÷ 26.4 × 0.55 = ~110 tok/s

Estimated speed at Q4_K_M (26.4 GB)

~110 tok/s
~83 tok/s
~68 tok/s

Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.

Learn more about tok/s estimation →

What's the download size of Falcon 40B Instruct?

At Q4_K_M, the download is about 24.00 GB. The full-precision Q8_0 version is 40.00 GB. The smallest option (IQ2_XXS) is 11.00 GB.

Which GPUs can run Falcon 40B Instruct?

1 consumer GPU can run Falcon 40B Instruct at Q4_K_M (26.4 GB). Top options include NVIDIA GeForce RTX 5090.

Which devices can run Falcon 40B Instruct?

15 devices with unified memory can run Falcon 40B Instruct at Q4_K_M (26.4 GB), including Mac Mini M4 (32 GB), Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB), Mac Studio M2 Ultra (192 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.