on

Can the Marvell ARMADA 8040 NAS (8 GB) run Falcon Mamba 7B Instruct?

25Basic quality (D) · 25 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 52/100 (quality, fit, speed and memory use) sets the ranking.

Barely: it runs very slowly (tight fit)

Very slow (tight fit). At Q3_K_M it needs about 4.6 GB of 5.2 GB usable, with an estimated speed of 1.0 tok/s.

Best quant
Q3_K_M
Generation speed
1.0 tok/s
Prompt processing
1.7 tok/s
First token (1K prompt)
595.6 s
Largest safe context
128K

Best app to run it on the Marvell ARMADA 8040 NAS (8 GB)

On Linux on ARM boards

llama.cpp

Command line + local API · runs on llama.cpp

  • Usually the first to support new model architectures.
llama-server -hf QuantFactory/falcon-mamba-7b-instruct-GGUF:Q3_K_M
  • Ollama Runs the Hugging Face GGUF with ollama run hf.co/…

How we choose: the files published for this model, which apps run in a container on a NAS without a graphics card (models run on its processor), and whether the model fits fully in the memory models can use. Details.

Download Falcon Mamba 7B Instruct

Recommended for the Marvell ARMADA 8040 NAS (8 GB): Q3_K_M. Every other quantization has its own download link in the table below.

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

Download the Q3_K_M file (opens in a new tab)

huggingface.co/QuantFactory/falcon-mamba-7b-instruct-GGUF (opens in a new tab)

ollama run hf.co/QuantFactory/falcon-mamba-7b-instruct-GGUF:Q3_K_M

Original weights · Hugging Face

Full-precision files from TII, for fine-tuning, vLLM, Transformers.

huggingface.co/tiiuae/falcon-mamba-7b-instruct (opens in a new tab)

No sign-in needed.

Links checked 2 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 25Basic quality (D) · 25 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 52/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q3_K_M, from its editorial quality tier scaled by the quantization or precision. It’s the same on any hardware that runs this variant. Scale: A+ 85+ · A 70+ · B 55+ · C 40+ · D under 40.
Runs Very slowtight fit
How well it runs on the Marvell ARMADA 8040 NAS (8 GB): speed, capped by how well it fits in memory. Runs great: 30+ tokens/s (or near the image, video or decision speed target) · Runs well: 10+ tokens/s · Slow: 3+ tokens/s · Very slow: under 3 · Won’t run: doesn’t fit even with offload. A tight fit or RAM offload caps it at Runs well; heavy offload caps it at Slow.

Match score

To order lists, each result also gets a match score out of 100 from four parts. It favors the most capable model that still runs well.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.11.3 / 45
Memory fitFull marks when the model fits in the share of the NAS’s memory that models can use (the rest stays with the NAS system and its storage services); fewer when it goes past that limit.24.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.2.3 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.15.0 / 15
Match score52.7 / 100

The match score orders results; it is not shown as a grade. Quality and Runs are the two ratings in the tables.

Every quantization on the Marvell ARMADA 8040 NAS (8 GB)

Quant Memory needed Available Speed Runs Quality Download
Q2_K 4.0 GB 5.2 GB 1.2 tok/s Very slow 21Basic quality (D) · 21 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 52/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q3_K_M 4.6 GB 5.2 GB 1.0 tok/s Very slowtight fit 25Basic quality (D) · 25 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 52/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q4_K_M 5.5 GB 5.2 GB 0.6 tok/s Very slowover memory limit 28Basic quality (D) · 28 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 39/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 6.2 GB 5.2 GB 0.5 tok/s Very slowfar over memory limit 29Basic quality (D) · 29 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 33/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 7.0 GB 5.2 GB — Won't run 29Basic quality (D) · 29 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 4/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q8_0 8.8 GB 5.2 GB — Won't run 30Basic quality (D) · 30 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 4/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
F16 15.6 GB 5.2 GB — Won't run 30Basic quality (D) · 30 of 100: how capable the model is at F16, whatever it runs on. Match score 4/100 (quality, fit, speed and memory use) sets the ranking. Original weights (opens in a new tab)

Memory figures assume 8K of context. Longer conversations or documents grow the KV cache.

Other hardware that runs it

Quick answers

How much memory does Falcon Mamba 7B Instruct need on the Marvell ARMADA 8040 NAS 8GB?

At Q3_K_M it needs about 4.6 GB. The Marvell ARMADA 8040 NAS 8GB has 5.2 GB usable.

How fast is Falcon Mamba 7B Instruct on the Marvell ARMADA 8040 NAS 8GB?

About 1.0 tok/s at Q3_K_M, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Falcon Mamba 7B Instruct should I use on the Marvell ARMADA 8040 NAS 8GB?

Q3_K_M. Of the versions that fit, it is the one that runs best; when two run equally well, the check prefers the higher-rated one.

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.

Was this page helpful?