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Can the NVIDIA RTX A500 Laptop GPU run RecurrentGemma 9B IT?

23Basic quality (D) · 23 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 46/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs well (offloads to RAM)

Runs well (offloads to RAM). At Q2_K it needs about 4.8 GB of 4.0 GB usable, with an estimated speed of 20.7 tok/s.

Best quant
Q2_K
Generation speed
20.7 tok/s
Prompt processing
451 tok/s
First token (1K prompt)
2.3 s
Largest safe context
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Download RecurrentGemma 9B IT

Original weights · Hugging Face

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

huggingface.co/google/recurrentgemma-9b-it (opens in a new tab)

Free Hugging Face account needed: request access on the model page, and Google approves it (usually within minutes to a few hours).

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

How it’s rated

Quality 23Basic quality (D) · 23 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 46/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q2_K, 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 Runs welloffloads to RAM
How well it runs on the NVIDIA RTX A500 Laptop GPU: 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.10.7 / 45
Memory fitFull marks when everything sits in GPU memory; fewer when part spills to system RAM.10.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.10.8 / 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 score46.5 / 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 NVIDIA RTX A500 Laptop GPU

Quant Memory needed Available Speed Runs Quality Download
Q2_K 4.8 GB 4.0 GB 20.7 tok/s Runs welloffloads to RAM 23Basic quality (D) · 23 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 46/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q3_K_M 5.6 GB 4.0 GB 14.9 tok/s Slowheavy RAM offload 28Basic quality (D) · 28 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 41/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q4_K_M 6.8 GB 4.0 GB 10.8 tok/s Slowheavy RAM offload 32Basic quality (D) · 32 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 41/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q5_K_M 7.7 GB 4.0 GB 8.6 tok/s Slowheavy RAM offload 32Basic quality (D) · 32 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 40/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q6_K 8.8 GB 4.0 GB 7.0 tok/s Slowheavy RAM offload 33Basic quality (D) · 33 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 40/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q8_0 11.1 GB 4.0 GB 4.9 tok/s Slowheavy RAM offload 34Basic quality (D) · 34 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 39/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
F16 20.2 GB 4.0 GB — Won't run 34Basic quality (D) · 34 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 RecurrentGemma 9B IT need on the NVIDIA RTX A500 Laptop GPU 4GB?

At Q2_K it needs about 4.8 GB. The NVIDIA RTX A500 Laptop GPU 4GB has 4.0 GB usable.

How fast is RecurrentGemma 9B IT on the NVIDIA RTX A500 Laptop GPU 4GB?

About 20.7 tok/s at Q2_K, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of RecurrentGemma 9B IT should I use on the NVIDIA RTX A500 Laptop GPU 4GB?

Q2_K. 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.

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