Can Nous Dolphin 13B run on NVIDIA A100 40GB?

YES — Runs Great

A77Great

Estimated from fit model

Nous Dolphin 13B needs ~26.8 GB VRAM. NVIDIA A100 40GB has 40.0 GB. With Q5_K_M quantization, expect ~142 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q5_K_M (High quality) — 26.8 GB, 142.3 tok/s, Runs well

26.8 GB required40.0 GB available

67% VRAM used

Fit status

Runs well

Decode

142.3 tok/s

TTFT

1360 ms

Safe context

16K

Memory

26.8 GB / 40.0 GB

Memory breakdown

Weights9.4 GB

KV Cache12.2 GB

Runtime1.2 GB

Headroom4.0 GB

See how fast it feels

See how fast it feelsNous Dolphin 13B on NVIDIA A100 40GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
  - Full privacy: your prompts never leave your machine
  - No per-token cost or rate limits
  - Works offline once the model is downloaded
  - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

  Model size     Q4 (4-bit)    Q8 (8-bit)    FP16
  7B params      ~4.3 GB       ~7.5 GB       ~14 GB
  13B params     ~7.9 GB       ~13.9 GB      ~26 GB
  70B params     ~42.7 GB      ~74.9 GB      ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 142.3 tok/s decode · 1.4s TTFT (warm) · 356 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	142.3 tok/s	742 ms	16K
Coding	A	Runs well	142.3 tok/s	1360 ms	16K
Agentic Coding	A	Runs with offload	142.3 tok/s	1978 ms	16K
Reasoning	A	Runs well	142.3 tok/s	1607 ms	16K
RAG	A	Runs with offload	142.3 tok/s	2473 ms	16K

Quantization options

How Nous Dolphin 13B (13B params) fits at each quantization level on NVIDIA A100 40GB (40.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	B63
Q3_K_S	3	6.4 GB	Low	B64
NVFP4	4	7.3 GB	Medium	B64
Q4_K_M	4	7.9 GB	Medium	B64
Q5_K_M	5	9.4 GB	High	B64
Q6_K	6	10.7 GB	High	B65
Q8_0	8	13.9 GB	Very High	B66
F16Best for your GPU	16	26.7 GB	Maximum	B69

Get started

Copy-paste commands to run Nous Dolphin 13B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "nousresearch/Nous-Dolphin-13B" \
  --hf-file "Nous-Dolphin-13B-Q5_K_M.gguf" \
  -c 4096 -ngl 99

Your hardware

More models your NVIDIA A100 40GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	197.5 tok/s
Qwen 3.5 27B	27B	S	85.7 tok/s
Qwen 3.6 27B	27B	S	85.9 tok/s
Qwen 3.6 35B A3B	35B	S	166 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	204.3 tok/s

Frequently asked questions

Can NVIDIA A100 40GB run Nous Dolphin 13B?

Yes, NVIDIA A100 40GB can run Nous Dolphin 13B with a A grade (Runs well). Expected decode speed: 142.3 tok/s.

How much VRAM does Nous Dolphin 13B need?

Nous Dolphin 13B (13B parameters) requires approximately 26.8 GB of memory with Q5_K_M quantization.

What is the best quantization for Nous Dolphin 13B?

The recommended quantization for Nous Dolphin 13B is Q5_K_M, which balances quality and memory efficiency.

What speed will Nous Dolphin 13B run at on NVIDIA A100 40GB?

On NVIDIA A100 40GB, Nous Dolphin 13B achieves approximately 142.3 tokens per second decode speed with a time-to-first-token of 1360ms using Q5_K_M quantization.

Can NVIDIA A100 40GB run Nous Dolphin 13B for coding?

For coding workloads, Nous Dolphin 13B on NVIDIA A100 40GB receives a A grade with 142.3 tok/s and 16K context.

What context window can Nous Dolphin 13B use on NVIDIA A100 40GB?

On NVIDIA A100 40GB, Nous Dolphin 13B can safely use up to 16K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.

See all results for NVIDIA A100 40GB See all hardware for Nous Dolphin 13B

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