Falcon Mamba 7B
By TII · UAE
Updated 2026-07-13
Overview
TII's first serious pure Mamba SSM at scale — 7B with constant memory per token, sidestepping transformer attention costs entirely.
When to pick this model
- Streaming workloads needing constant memory per token
- Research on state-space models versus transformers
- Throughput-bound inference where attention is the bottleneck
- Long-running generation where context grows unboundedly
- Edge inference on memory-constrained devices
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 5 GB |
| Q5_K_M | 6 GB |
| Q8_0 | 9 GB |
| FP16 (no quantization) | 14 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 Mamba 7B fits an 8 GB consumer card at Q4_K_M (5 GB). Stepping up to Q8_0 nearly doubles the footprint to 9 GB, and unquantized FP16 weights take 14 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, Falcon Mamba 7B needs roughly 8 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 15 tokens/sec on entry-level GPUs, on the order of 40 tokens/sec on a mid-range card, and up to 100 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches Falcon Mamba 7B 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 Mamba 7B |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Q5_K_M (6 GB used) |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Q8_0 (9 GB used) |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | FP16 (14 GB used) |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | FP16 (14 GB used) |
| 32 GB | RTX 5090 | FP16 (14 GB used) |
Which GPU should you buy to run Falcon Mamba 7B?
To run Falcon Mamba 7B locally at Q4, you need ~5 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 | 62 |
Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.
To put Falcon Mamba 7B in context: its MMLU score of 62 ranks #30 of the 34 catalog models with a published MMLU result (catalog median 73.4). Scores are author-published and measured under different harnesses, so treat gaps of a few points as noise rather than a verdict.
Strengths
- O(1) memory per token at inference
- No practical context limit imposed by attention
- Apache 2.0 license
- Demonstrates Mamba viability at production scale
Limitations
- Weaker in-context learning than transformers of equal size
- No vision or multimodal support
- Trained context is only 8k despite architectural headroom
Typical workloads
In our catalog grid, Falcon Mamba 7B is filed under Linear Inference, Long Stream — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: everyday chat, drafting and summarization.
Note the 8k-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: Mamba architecture (SSM) · 7B · no Transformer · O(1) inference
Training: TII UAE — 5.5T tokens corpus. Pure State Space Model architecture.
The benchmark pure-Mamba 7B — pick it to study SSMs or to serve streaming workloads where attention costs hurt most.
Quick start
ollama run falcon-mamba:7bOr 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 Mamba 7B need?
At the recommended Q4_K_M quantization, Falcon Mamba 7B needs about 5 GB of VRAM. Q8_0 takes 9 GB, and unquantized FP16 weights take 14 GB.
Can Falcon Mamba 7B run without a GPU?
Yes — with roughly 8 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 Mamba 7B support?
Falcon Mamba 7B supports a 8k-token context window (8,192 tokens).
Can I use Falcon Mamba 7B commercially?
Falcon Mamba 7B 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 Mamba 7B on consumer hardware?
Our compatibility engine estimates on the order of 40 tokens/sec on a mid-range GPU and up to 100 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of Falcon Mamba 7B should I download first?
Start with Q4_K_M (5 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.