Can Nemotron Mini 4B run on RX 9060 XT 16GB?

YES — Runs Great

C50Usable

Estimated from fit model

Nemotron Mini 4B needs ~6.9 GB VRAM. RX 9060 XT 16GB has 16.0 GB. With Q4_K_M quantization, expect ~56 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: 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

Q4_K_M (Medium quality) — 6.9 GB, 56.0 tok/s, Runs well

6.9 GB required16.0 GB available

43% VRAM used

Fit status

Runs well

Decode

56.0 tok/s

TTFT

3457 ms

Safe context

Memory

6.9 GB / 16.0 GB

Memory breakdown

Weights2.4 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsNemotron Mini 4B on RX 9060 XT 16GB
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: 56.0 tok/s decode · 3.5s TTFT (warm) · 140 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	C	Runs well	56.0 tok/s	1886 ms	4K
Coding	C	Runs well	56.0 tok/s	3457 ms	4K
Agentic Coding	C	Runs well	56.0 tok/s	5029 ms	4K
Reasoning	C	Runs well	56.0 tok/s	4086 ms	4K
RAG	C	Runs well	56.0 tok/s	6286 ms	4K

Inference speed

Nemotron Mini 4B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Nemotron Mini 4B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~76 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	76.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	64.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	64.0

Quantization options

How Nemotron Mini 4B (4B params) fits at each quantization level on RX 9060 XT 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.6 GB	Low	C47
Q3_K_S	3	2.0 GB	Low	C47
NVFP4	4

Get started

Copy-paste commands to run Nemotron Mini 4B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "nvidia/Nemotron-Mini-4B-Instruct" \
  --hf-file "Nemotron-Mini-4B-Instruct-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Upgrade options

Hardware that runs Nemotron Mini 4B well

MacBook Pro M4 Pro 24GBBudget pick

24 GB Unified (+8)

This setup is broadly balanced for this model.56 tok/s decode

~$1,999 MSRP

Frequently asked questions

Can RX 9060 XT 16GB run Nemotron Mini 4B?

Yes, RX 9060 XT 16GB can run Nemotron Mini 4B with a C grade (Runs well). Expected decode speed: 56.0 tok/s.

How much VRAM does Nemotron Mini 4B need?

Nemotron Mini 4B (4B parameters) requires approximately 6.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Nemotron Mini 4B?

The recommended quantization for Nemotron Mini 4B is Q4_K_M, which balances quality and memory efficiency.

What speed will Nemotron Mini 4B run at on RX 9060 XT 16GB?

On RX 9060 XT 16GB, Nemotron Mini 4B achieves approximately 56.0 tokens per second decode speed with a time-to-first-token of 3457ms using Q4_K_M quantization.

Can RX 9060 XT 16GB run Nemotron Mini 4B for coding?

For coding workloads, Nemotron Mini 4B on RX 9060 XT 16GB receives a C grade with 56.0 tok/s and 4K context.

What context window can Nemotron Mini 4B use on RX 9060 XT 16GB?

On RX 9060 XT 16GB, Nemotron Mini 4B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.

See all results for RX 9060 XT 16GB See all hardware for Nemotron Mini 4B

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