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Can the NVIDIA A800 40GB Active run StarCoder2 15B Instruct?

40Fair quality (C) · 40 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 69/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs great

Runs great. At Q8_0 it needs about 20.2 GB of 40.0 GB usable, with an estimated speed of 60.9 tok/s.

Best quant
Q8_0
Generation speed
60.9 tok/s
Prompt processing
880 tok/s
First token (1K prompt)
1.2 s
Largest safe context
16K

Best app to run it on the NVIDIA A800 40GB Active

On Linux

vLLM

Inference server · runs on vLLM

  • The full model fits in GPU memory at 8-bit, so it can batch many requests: best for serving a team or an app.
vllm serve bigcode/starcoder2-15b-instruct-v0.1
  • Ollama One command with Ollama’s own build of this model.
  • LM Studio Point-and-click: search, download and chat in one window.

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 starcoder2:15b-instruct-v0.1-q8_0
  • LM Studio Point-and-click: search, download and chat in one window.
  • llama.cpp Usually the first to support new model architectures.

Want an agent? StarCoder2 15B Instruct supports tool use, so you can pair a llama.cpp or MLX LM server with one of these. Apps that run on this computer come first.

Coding agents

Personal assistants

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 StarCoder2 15B Instruct

Recommended for the NVIDIA A800 40GB Active: Q8_0. 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 starcoder2:15b-instruct-v0.1-q8_0

Quantized files · GGUF

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

Download the Q8_0 file (opens in a new tab)

huggingface.co/lmstudio-community/starcoder2-15b-instruct-v0.1-GGUF (opens in a new tab)

ollama run hf.co/lmstudio-community/starcoder2-15b-instruct-v0.1-GGUF:Q8_0

Original weights · Hugging Face

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

huggingface.co/bigcode/starcoder2-15b-instruct-v0.1 (opens in a new tab)

No sign-in needed.

Official repo · GitHub

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

github.com/bigcode-project/starcoder2 (opens in a new tab)

git clone https://github.com/bigcode-project/starcoder2

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

How it’s rated

Quality 40Fair quality (C) · 40 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 69/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q8_0, 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 A800 40GB Active: 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.18.0 / 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.11.6 / 15
Match score69.6 / 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 A800 40GB Active

Quant Memory needed Available Speed Runs Quality Download
Q2_K 9.7 GB 40.0 GB 196 tok/s Runs great 28Basic quality (D) · 28 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 58/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q3_K_M 11.1 GB 40.0 GB 155 tok/s Runs great 33Basic quality (D) · 33 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 61/100 (quality, fit, speed and memory use) sets the ranking.
Q4_K_M 13.0 GB 40.0 GB 120 tok/s Runs great 38Basic quality (D) · 38 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 64/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 14.6 GB 40.0 GB 98.8 tok/s Runs great 38Basic quality (D) · 38 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 65/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 16.4 GB 40.0 GB 83.0 tok/s Runs great 39Basic quality (D) · 39 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 67/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q8_0 20.2 GB 40.0 GB 60.9 tok/s Runs great 40Fair quality (C) · 40 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 69/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
F16 35.2 GB 40.0 GB 28.1 tok/s Runs welltight fit 40Fair quality (C) · 40 of 100: how capable the model is at F16, whatever it runs on. Match score 69/100 (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 StarCoder2 15B Instruct need on the NVIDIA A800 40GB Active?

At Q8_0 it needs about 20.2 GB. The NVIDIA A800 40GB Active has 40.0 GB usable.

How fast is StarCoder2 15B Instruct on the NVIDIA A800 40GB Active?

About 60.9 tok/s at Q8_0, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of StarCoder2 15B Instruct should I use on the NVIDIA A800 40GB Active?

Q8_0. 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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