Falcon 40B Instruct
Falcon-40B-Instruct is a 40B parameter instruction-tuned model from TII (UAE), fine-tuned on Baize chat data for conversation and instruction-following. It uses FlashAttention and multiquery attention to keep inference reasonably fast for its size. Apache 2.0 licensed, so commercial use is unrestricted.
Falcon-40B-Instruct made sense in mid-2023 but the landscape has moved. If you are in the Arabic region hoping for Arabic-language capability, this model will disappoint — it was not trained meaningfully on Arabic. The 85–100GB memory floor also means most operators will need serious infrastructure before they even test it. Skip it unless you have a specific reason to run a permissively licensed 40B English instruct model and already have the VRAM budget sitting idle.
›Why this rating
Auto-generated rating (Opus 4.7 judge, claude-opus-4-7). Overall 9.05/10. License is explicit Apache 2.0 on the card and correctly flagged commercial-OK. Params (40B), vendor (TII), family (falcon), and context (2048) align with Falcon-40B's known architecture. The editorial voice is honest and operator-grade — the verdict directly tells readers to skip unless they have a specific reason, which is the runlocalai tone. One concern: 'arabic' is listed in useCases but the weaknesses correctly note Arabic support is weak — this is contradictory and should be removed from useCases. bestUseCase could be sharper but is acceptable. Overall this is a fair, honest archival entry for a once-prominent model.
Flags: - useCases includes 'arabic' which directly contradicts the weakness 'Arabic support is weak' — remove 'arabic' from useCases - bestUseCase is somewhat generic ('English-language instruction following and chat'); could be sharper
Overview
Falcon-40B-Instruct is a 40B parameter instruction-tuned model from TII (UAE), fine-tuned on Baize chat data for conversation and instruction-following. It uses FlashAttention and multiquery attention to keep inference reasonably fast for its size. Apache 2.0 licensed, so commercial use is unrestricted.
Strengths
- Apache 2.0 license — no commercial restrictions
- FlashAttention + multiquery attention reduce inference overhead at 40B scale
- Built on Falcon-40B, which ranked competitively on the OpenLLM Leaderboard at release
- From TII, a UAE-based research institute — regional provenance
Weaknesses
- Arabic support is weak — training data is primarily English and French
- 2048-token context window is short by current standards
- Requires roughly 85–100GB of memory, meaning multi-GPU or high-end hardware is mandatory
- Newer open models at similar or smaller sizes have since outperformed it on most benchmarks
Quantization variants
Each quantization trades model quality for file size and VRAM. Q4_K_M is the most popular starting point.
| Quantization | File size | VRAM required |
|---|---|---|
| Q4_K_M | 22.0 GB | 28 GB |
Get the model
HuggingFace
Original weights
Source repository — direct quantization required.
Hardware that runs this
Cards with enough VRAM for at least one quantization of Falcon 40B Instruct.
Models worth comparing
Same parameter band, plus what's one tier above and below — so you can decide what actually fits your hardware.
Frequently asked
What's the minimum VRAM to run Falcon 40B Instruct?
Can I use Falcon 40B Instruct commercially?
What's the context length of Falcon 40B Instruct?
Source: huggingface.co/tiiuae/falcon-40b-instruct
Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify model claims.
Related — keep moving
Verify Falcon 40B Instruct runs on your specific hardware before committing money.