Falcon H1 7B Instruct — Hardware Requirements & GPU Compatibility
ChatFunctions
Falcon H1 7B Instruct is TII UAE's instruction-tuned chat model built on Falcon-H1-7B-Base, using a hybrid architecture that combines standard Transformer attention with Mamba state-space layers in a causal decoder-only design. That hybrid-head approach is meant to keep long-context inference efficient while retaining Transformer-level quality, and the model supports 18 languages including Arabic, Chinese, Japanese, and several European languages. On released benchmarks it beats similarly sized Qwen2.5-7B, Llama-3.1-8B, and Falcon3-7B/10B on most general knowledge, science, and instruction-following tasks. At 7.6 billion parameters it fits a single consumer GPU. Context length is 262,144 tokens. It is released under the Falcon LLM License, a custom TII license that permits commercial use subject to an acceptable-use policy and an attribution requirement in derivative works, and was published in May 2025.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 32K)Best on 24 GB (Q4_K_M · ~130 tok/s · 128K)Macs from 8 GB (IQ2_M · ~24 tok/s)
Other
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Falcon H1 7B Instruct
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
ollama run hf.co/tiiuae/Falcon-H1-7B-Instruct-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 17.7 GB free · 16K context
Download: Falcon-H1-7B-Instruct-Q4_K_M.gguf4.60 GBtiiuae
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (5 GB headroom) · Q4_K_M
- Generation speed
- ~46 tok/s
- generation speed
- Cost per 1M output tokens
- $0.50
- per 1M output tokens
How Much VRAM Does Falcon H1 7B Instruct Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 3.7 GB | 6.5 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 128K context · ~177 tok/s | |
| 3.8 GB | 6.6 GB | 3-bit small — 3-bit small quantization | Fits: 128K context · ~172 tok/s | |
| 4.2 GB | 7 GB | 3-bit medium — 3-bit medium quantization | Fits: 128K context · ~157 tok/s | |
| 4.3 GB | 7.1 GB | 4-bit · legacy — 4-bit legacy quantization | Fits: 128K context · ~153 tok/s | |
| 5.0 GB | 7.8 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 128K context · ~130 tok/s | |
| 5.9 GB | 8.7 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 128K context · ~111 tok/s | |
| 6.7 GB | 9.5 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 128K context · ~97 tok/s | |
| 8.1 GB | 10.8 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 128K context · ~81 tok/s |
Selected Q4_K_M
4-bit medium quantization6.3GBat 16K context
- Weights
- 4.6 GB
- KV cache
- 1.5 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 17.7 GB free · ~130 tok/s
≥50 tok/s needs ≥387 GB/s · RTX 5090 ~231, RTX 3090 Ti ~130
Which Hardware Can Run Falcon H1 7B Instruct?
Q4_K_M · 5.0 GBFalcon H1 7B Instruct (Q4_K_M) requires 5.0 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 7+ GB is recommended. Using the full 262K context window can add up to 23.4 GB, bringing total usage to 28.5 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 151 devices with unified memory can run Falcon H1 7B Instruct, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 1.7 GB free · up to 32K ctx
16 GPUs · 1 device family
RTX 3080~98Simulate output on RTX 3080RTX 2080 Ti~79Simulate output on RTX 2080 TiRTX 3070 Ti~79Simulate output on RTX 3070 TiOrin Nano~13Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)20 GB
fits · 13.7 GB free · up to 128K ctx
1 GPU
RX 7900 XT~95Simulate output on RX 7900 XT32–40 GB
fits · 25.7 GB free · up to 256K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~231Simulate output on RTX 5090M5 Max36 GB~64Simulate output on Mac Studio M5 Max (36 GB)M532 GB~21Simulate output on MacBook Air 13" M5 (32 GB)Orin~26Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~17Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Falcon H1 7B Instruct
Falcon family · 13 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| ALoDLM 8BAmazon | — | 7.6 GB | ~119 | same size |
| Ling 3.0 TinyInclusion AI | — | 5.5 GB | ~364 | same size |
| BitCPM CANN 8BOpenBMB | — | 5.6 GB | ~127 | same size |
| Gemma 4 E4BGoogle | — | 5.6 GB | ~123 | same size |
| Olmo 3 7B InstructAllen AI | — | 8.4 GB | ~114 | same size |
| Phi Mini MoE InstructMicrosoft | — | 5.4 GB | ~239 | same size |
Specifications
- Publisher
- TII UAE
- Family
- Falcon
- Parameters
- 7.6B
- Architecture
- FalconH1ForCausalLM
- Context Length
- 262,144 tokens
- Vocabulary Size
- 130,049
- Release Date
- 2025-05-01
- License
- Other
Frequently Asked Questions
How much VRAM does Falcon H1 7B Instruct need?
Falcon H1 7B Instruct requires 5.0 GB of VRAM at Q4_K_M, or 15.7 GB at BF16. Full 262K context adds up to 23.4 GB (28.5 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 7.6B × 4.8 bits ÷ 8 = 4.6 GB
KV Cache + Overhead ≈ 0.4 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.
KV Cache + Overhead ≈ 23.9 GB (at full 262K context)
VRAM usage by quantization
What's the best quantization for Falcon H1 7B Instruct?
For Falcon H1 7B Instruct, Q4_K_M (5.0 GB) offers the best balance of quality and VRAM usage. Q5_0 (5.2 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 2.6 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Falcon H1 7B Instruct on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Falcon H1 7B Instruct, the smallest at IQ2_M. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 5.0 GB of usable unified memory (RAM minus macOS overhead), from Mac Mini M2 Pro (16 GB).
Can I run Falcon H1 7B Instruct locally?
Yes — Falcon H1 7B Instruct can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Falcon H1 7B Instruct?
At Q4_K_M, Falcon H1 7B Instruct can reach ~952 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~130 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 ÷ 5.04 × 0.65 = ~1032 tok/s
Estimated speed at Q4_K_M (5.0 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Falcon H1 7B Instruct?
At Q4_K_M, the download is about 4.55 GB. The full-precision BF16 version is 15.17 GB. The smallest option (IQ2_XXS) is 2.09 GB.
Which GPUs can run Falcon H1 7B Instruct?
52 consumer GPUs can run Falcon H1 7B Instruct 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 H1 7B Instruct?
151 devices with unified memory can run Falcon H1 7B Instruct at Q4_K_M (5.0 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.