SmolLM2 135M Instruct on NVIDIA RTX A1000

Can the NVIDIA RTX A1000 run SmolLM2 135M Instruct?

Yes, it runs great

Runs great. At F16 it needs about 1.4 GB of 8.0 GB usable, with an estimated speed of 425 tok/s.

Best quant: F16
Generation speed: 425 tok/s
Prompt processing: 37,108 tok/s
First token (1K prompt): 0.3 s
Largest safe context: 8K

Best app to run it on the NVIDIA RTX A1000

On Windows

Ollama

Command line + local API · runs on llama.cpp (ggml), MLX (Apple silicon, preview)

One command with Ollama’s own build of this model.
Serves an OpenAI-compatible API that other apps and coding tools can use.

ollama run smollm2:135m-instruct-fp16

LM Studio Point-and-click: search, download and chat in one window.
llama.cpp Usually the first to support new model architectures.

On Linux

Ollama

Command line + local API · runs on llama.cpp (ggml), MLX (Apple silicon, preview)

One command with Ollama’s own build of this model.
Serves an OpenAI-compatible API that other apps and coding tools can use.

ollama run smollm2:135m-instruct-fp16

LM Studio Point-and-click: search, download and chat in one window.
llama.cpp Usually the first to support new model architectures.

How we choose: the files published for this model, your operating system and GPU, whether it fits fully in memory, and published speed tests (MLX vs llama.cpp on Apple silicon, vLLM vs llama.cpp vs Ollama on NVIDIA). Details.

Download SmolLM2 135M Instruct

Recommended for the NVIDIA RTX A1000: F16. Every other quantization has its own download link in the table below.

Quickest · Ollama

One command downloads a ready-to-run version and starts a chat.

Get Ollama at ollama.com/download (opens in a new tab) · model page (opens in a new tab)

ollama run smollm2:135m-instruct-fp16

Quantized files · GGUF

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

Download the F16 file (opens in a new tab)

huggingface.co/unsloth/SmolLM2-135M-Instruct-GGUF (opens in a new tab)

ollama run hf.co/unsloth/SmolLM2-135M-Instruct-GGUF:F16

Original weights · Hugging Face

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

huggingface.co/HuggingFaceTB/SmolLM2-135M-Instruct (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from Hugging Face. The weights themselves download from Hugging Face.

github.com/huggingface/smollm (opens in a new tab)

git clone https://github.com/huggingface/smollm

Links checked 3 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 4Basic quality (D) · 4 of 100: how capable the model is at F16, whatever it runs on. Match score 45/100 (quality, fit, speed and memory use) sets the ranking.: How capable the model itself is at F16, 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 great: How well it runs on the NVIDIA RTX A1000: 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.	1.8 / 45
Memory fitFull marks when everything sits in GPU memory; fewer when part spills to system RAM.	25.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.	15.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.	4.2 / 15
Match score	46.0 / 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 A1000

Quant	Memory needed	Available	Speed	Runs	Quality	Download
`Q2_K`	1.2 GB	8.0 GB	2,977 tok/s	Runs great	2Basic quality (D) · 2 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 44/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q3_K_M`	1.3 GB	8.0 GB	2,344 tok/s	Runs great	3Basic quality (D) · 3 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 45/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q4_K_M`	1.3 GB	8.0 GB	1,813 tok/s	Runs great	3Basic quality (D) · 3 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 45/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q5_K_M`	1.3 GB	8.0 GB	1,498 tok/s	Runs great	3Basic quality (D) · 3 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 45/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q6_K`	1.3 GB	8.0 GB	1,258 tok/s	Runs great	3Basic quality (D) · 3 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 45/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q8_0`	1.3 GB	8.0 GB	923 tok/s	Runs great	4Basic quality (D) · 4 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 45/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`F16`	1.4 GB	8.0 GB	425 tok/s	Runs great	4Basic quality (D) · 4 of 100: how capable the model is at F16, whatever it runs on. Match score 45/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab) 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

4Basic quality (D) · 4 of 100: how capable the model is at F16, whatever it runs on. Match score 45/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4060 F16 · 603 tok/s · Runs great
4Basic quality (D) · 4 of 100: how capable the model is at F16, whatever it runs on. Match score 45/100 (quality, fit, speed and memory use) sets the ranking. Apple M1 (16 GB) F16 · 130 tok/s · Runs great
4Basic quality (D) · 4 of 100: how capable the model is at F16, whatever it runs on. Match score 45/100 (quality, fit, speed and memory use) sets the ranking. Apple M4 (16 GB) F16 · 229 tok/s · Runs great
4Basic quality (D) · 4 of 100: how capable the model is at F16, whatever it runs on. Match score 45/100 (quality, fit, speed and memory use) sets the ranking. Apple M4 (24 GB) F16 · 229 tok/s · Runs great
4Basic quality (D) · 4 of 100: how capable the model is at F16, whatever it runs on. Match score 45/100 (quality, fit, speed and memory use) sets the ranking. Apple M4 Pro (24 GB) F16 · 521 tok/s · Runs great
4Basic quality (D) · 4 of 100: how capable the model is at F16, whatever it runs on. Match score 44/100 (quality, fit, speed and memory use) sets the ranking. Intel Arc B580 F16 · 772 tok/s · Runs great

Quick answers

How much memory does SmolLM2 135M Instruct need on the NVIDIA RTX A1000 8GB?

At F16 it needs about 1.4 GB. The NVIDIA RTX A1000 8GB has 8.0 GB usable.

How fast is SmolLM2 135M Instruct on the NVIDIA RTX A1000 8GB?

About 425 tok/s at F16, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of SmolLM2 135M Instruct should I use on the NVIDIA RTX A1000 8GB?

F16. 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.