Falcon 3 10B Instruct
By TII · UAE
Updated 2026-07-13
Overview
TII's depth-upscaled 10B successor to Falcon 3 7B, hitting MMLU 73.1 and GSM8K 83.1 — state-of-the-art under 13B at release.
When to pick this model
- General chat where 7B is too weak and 13B too costly
- Multilingual production deploys across five EU languages
- Math-leaning tasks needing GSM8K 83+ at small scale
- Replacing Llama 3 8B with stronger benchmark numbers
- Workloads benefiting from 32k context
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 6 GB |
| Q5_K_M | 8 GB |
| Q8_0 | 12 GB |
| FP16 (no quantization) | 20 GB |
VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.
In practice, Falcon 3 10B Instruct fits an 8 GB consumer card at Q4_K_M (6 GB). Stepping up to Q8_0 nearly doubles the footprint to 12 GB, and unquantized FP16 weights take 20 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, Falcon 3 10B Instruct needs roughly 12 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 9 tokens/sec on entry-level GPUs, on the order of 28 tokens/sec on a mid-range card, and up to 70 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches Falcon 3 10B Instruct to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.
| GPU memory | Example cards | Best fit for Falcon 3 10B Instruct |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Q5_K_M (8 GB used) |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Q8_0 (12 GB used) |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | Q8_0 (12 GB used) |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | FP16 (20 GB used) |
| 32 GB | RTX 5090 | FP16 (20 GB used) |
Which GPU should you buy to run Falcon 3 10B Instruct?
To run Falcon 3 10B Instruct locally at Q4, you need ~6 GB of VRAM. The best value for this is a RTX 5060 (8 GB VRAM).
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Published benchmark scores
| Benchmark | Score |
|---|---|
| MMLU | 73.1 |
| GSM8K | 83.1 |
Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.
To put Falcon 3 10B Instruct in context: its MMLU score of 73.1 ranks #18 of the 34 catalog models with a published MMLU result (catalog median 73.4); its GSM8K score of 83.1 ranks #6 of the 9 catalog models with a published GSM8K result (catalog median 83.1). Scores are author-published and measured under different harnesses, so treat gaps of a few points as noise rather than a verdict.
Strengths
- SOTA among sub-13B models at release
- MMLU 73.1 with strong knowledge breadth
- Efficient depth-upscaled design from the 7B base
- Five-language coverage with permissive licensing
- Strong GSM8K performance for the size class
Limitations
- TII Falcon-LLM 2.0 license, not Apache 2.0
- Limited fine-tune ecosystem versus Llama derivatives
- No multimodal version available
Typical workloads
In our catalog grid, Falcon 3 10B Instruct is filed under Dense 10B Chat, Multilingual — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: multilingual workloads.
Note the 31k-token context window — fine for short interactions, limiting for long documents or big retrieval contexts. It ships under the TII Falcon-LLM License 2.0 license — commercial use is generally possible but read the specific terms before embedding it in a product.
Architecture & training
Architecture: Dense 10B · depth-upscaled from 7B
Training: Successor to the 7B.
The strongest sub-13B Falcon to date — a solid mid-size pick when you need multilingual quality without the Llama license.
Quick start
ollama run falcon3:10bOr use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
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Frequently asked questions
How much VRAM does Falcon 3 10B Instruct need?
At the recommended Q4_K_M quantization, Falcon 3 10B Instruct needs about 6 GB of VRAM. Q8_0 takes 12 GB, and unquantized FP16 weights take 20 GB.
Can Falcon 3 10B Instruct run without a GPU?
Yes — with roughly 12 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.
What context window does Falcon 3 10B Instruct support?
Falcon 3 10B Instruct supports a 31k-token context window (32,000 tokens).
Can I use Falcon 3 10B Instruct commercially?
Falcon 3 10B Instruct ships under the TII Falcon-LLM License 2.0 license. Commercial use is generally permitted subject to its terms — review the license text before shipping a product.
How fast is Falcon 3 10B Instruct on consumer hardware?
Our compatibility engine estimates on the order of 28 tokens/sec on a mid-range GPU and up to 70 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of Falcon 3 10B Instruct should I download first?
Start with Q4_K_M (6 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. It already fits an 8 GB card at Q5_K_M.