Falcon 7B — Hardware Requirements & GPU Compatibility
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Falcon 7B was one of the first truly competitive open-source large language models, released in mid-2023 by the Technology Innovation Institute in Abu Dhabi. Trained on the massive RefinedWeb dataset, it demonstrated that carefully curated web data could rival models trained on more traditionally assembled corpora. At 7 billion parameters, Falcon 7B helped establish the 7B class as the sweet spot for local inference, offering genuine language understanding on consumer GPUs with as little as 6 GB of VRAM.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 16K)Best on 24 GB (Q4_K_M · ~138 tok/s · 32K)Macs from 16 GB (Q4_K_M · ~29 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Falcon 7B
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
ollama run hf.co/mradermacher/falcon-7b-i1-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 16.7 GB free · 16K context
Download: falcon-7b.i1-Q4_K_M.gguf4.98 GBmradermacher
Ways to runOther ways
each opens step-by-stepRun in cloud
Your own private copy · Q4_K_M · 24 GB GPU
- The easiest way to try this model
- Running in a few minutes, easy setup
- Chat right here or connect any app
$5+ bonus for new users
Runs on RTX 3080 Ti (12 GB) (4 GB headroom) · Q4_K_M
- Generation speed
- ~125 tok/s
- generation speed
- Cost per 1M output tokens
- $0.27
- per 1M output tokens
How Much VRAM Does Falcon 7B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 3.4 GB | 8.7 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~194 tok/s | |
| 3.5 GB | 8.8 GB | 3-bit small — 3-bit small quantization | Fits: 32K context · ~189 tok/s | |
| 3.9 GB | 9.2 GB | 3-bit medium — 3-bit medium quantization | Fits: 32K context · ~169 tok/s | |
| 4.0 GB | 9.3 GB | 4-bit · legacy — 4-bit legacy quantization | Fits: 32K context · ~165 tok/s | |
| 4.8 GB | 10.1 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 32K context · ~138 tok/s | |
| 5.7 GB | 11 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~116 tok/s | |
| 6.5 GB | 11.9 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~100 tok/s | |
| 7.9 GB | 13.3 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 32K context · ~83 tok/s |
Selected Q4_K_M
4-bit medium quantization7.3GBat 16K context
- Weights
- 4.1 GB
- KV cache
- 2.9 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 16.8 GB free · ~138 tok/s
≥50 tok/s needs ≥366 GB/s · RTX 5090 ~245, RTX 3090 Ti ~138
Which Hardware Can Run Falcon 7B?
Q4_K_M · 4.8 GBFalcon 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. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 151 devices with unified memory can run Falcon 7B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 0.8 GB free · up to 16K ctx
16 GPUs · 2 device families
RTX 3080~104Simulate output on RTX 3080RTX 2080 Ti~84Simulate output on RTX 2080 TiRTX 3070 Ti~83Simulate output on RTX 3070 TiOrin Nano~14Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~10Simulate output on iPhone 1720 GB
fits · 12.8 GB free · up to 32K ctx
1 GPU
RX 7900 XT~101Simulate output on RX 7900 XT32–40 GB
fits · 24.8 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~245Simulate output on RTX 5090M5 Max36 GB~68Simulate output on Mac Studio M5 Max (36 GB)M532 GB~23Simulate output on MacBook Air 13" M5 (32 GB)Orin~28Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~18Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
How good is Falcon 7B?
llmrun Score blends public benchmark boards into one number; 100 = the average open model.
Not enough data yet for an llmrun Score.
A Score needs results on enough independent boards. How the Composite Score is built
Benchmarks
The 6 boards that weigh most in the llmrun Score, of 10 with a result for this model; badges show who ran the test.
- MMLU
Epoch AI · Apr 2023Lab-reported
Lab-reported35.0%
#78/85 open
- BIG-Bench Hard
Epoch AI · Apr 2023Lab-reported
Lab-reported28.8%
#37/38 open
- GSM8K
Epoch AI · Apr 2023Lab-reported
Lab-reported6.8%
#61/62 open
- HellaSwag
Epoch AI · Apr 2023Lab-reported
Lab-reported78.1%
#18/47 open
- ARC Challenge
Epoch AI · Apr 2023Lab-reported
Lab-reported47.9%
#32/56 open
- WinoGrande
Epoch AI · Apr 2023Lab-reported
Lab-reported67.2%
#29/51 open
Alternatives to Falcon 7B
Falcon family · 13 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Qwen2.5 7B InstructAlibaba | 65.1 | 5.8 GB | ~131 | same size |
| Olmo 3 7B InstructAllen AI | — | 8.4 GB | ~114 | same size |
| Phi Mini MoE InstructMicrosoft | — | 5.4 GB | ~239 | same size |
| DeepSeek R1 Distill Qwen 7BDeepSeek | — | 5.8 GB | ~131 | same size |
| Eurus 2 7B PRIMEPRIME-RL | — | 5.1 GB | ~131 | same size |
| Mathstral 7B v0.1Mistral AI | — | 6.8 GB | ~133 | same size |
Specifications
- Publisher
- TII UAE
- Family
- Falcon
- Parameters
- 7.2B
- Architecture
- FalconForCausalLM
- Vocabulary Size
- 65,024
- Release Date
- 2023-04-24
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does Falcon 7B need?
Falcon 7B requires 4.8 GB of VRAM at Q4_K_M, or 15.9 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 7.2B × 4.8 bits ÷ 8 = 4.3 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
VRAM usage by quantization
What's the best quantization for Falcon 7B?
For Falcon 7B, Q4_K_M (4.8 GB) offers the best balance of quality and VRAM usage. Q5_K_S (5.5 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 2.2 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Falcon 7B on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Falcon 7B. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 4.8 GB of usable unified memory (RAM minus macOS overhead).
Can I run Falcon 7B locally?
Yes — Falcon 7B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Falcon 7B?
At Q4_K_M, Falcon 7B can reach ~1008 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~138 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 ÷ 4.76 × 0.65 = ~1092 tok/s
Estimated speed at Q4_K_M (4.8 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Falcon 7B?
At Q4_K_M, the download is about 4.33 GB. The full-precision BF16 version is 14.43 GB. The smallest option (IQ2_XXS) is 1.98 GB.
Which GPUs can run Falcon 7B?
52 consumer GPUs can run Falcon 7B at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
Which devices can run Falcon 7B?
151 devices with unified memory can run Falcon 7B at Q4_K_M (4.8 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, 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.