Can Nemotron Mini 4B run on RTX 4070 Laptop 8GB?

YES — Runs Great

B56Good

Estimated from fit model

Nemotron Mini 4B needs ~6.1 GB VRAM. RTX 4070 Laptop 8GB has 8.0 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) — 6.1 GB, 64.0 tok/s, Runs well

6.1 GB required8.0 GB available

76% VRAM used

Fit status

Runs well

Decode

64.0 tok/s

TTFT

3025 ms

Safe context

Memory

6.1 GB / 8.0 GB

Memory breakdown

Weights2.4 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsNemotron Mini 4B on RTX 4070 Laptop 8GB
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: 64.0 tok/s decode · 3.0s TTFT (warm) · 160 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	B	Runs well	64.0 tok/s	1650 ms	4K
Coding	B	Runs well	56.0 tok/s	3457 ms	4K
Agentic Coding	C	Runs with offload (needs ~0 GB host RAM)	58.4 tok/s	4819 ms	4K
Reasoning	B	Runs well	64.0 tok/s	3575 ms	4K
RAG	C	Runs with offload (needs ~0 GB host RAM)	58.4 tok/s	6024 ms	4K

Quantization options

How Nemotron Mini 4B (4B params) fits at each quantization level on RTX 4070 Laptop 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.6 GB	Low	C52
Q3_K_S	3	2.0 GB	Low	C53
NVFP4	4	2.2 GB	Medium	C53
Q4_K_M	4	2.4 GB	Medium	C53
Q5_K_M	5	2.9 GB	High	C54
Q6_K	6	3.3 GB	High	C54
Q8_0Best for your GPU	8	4.3 GB	Very High	C54
F16	16	8.2 GB	Maximum	F0

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

Frequently asked questions

Can RTX 4070 Laptop 8GB run Nemotron Mini 4B?

Yes, RTX 4070 Laptop 8GB can run Nemotron Mini 4B with a B 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 6.1 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 RTX 4070 Laptop 8GB?

On RTX 4070 Laptop 8GB, 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 RTX 4070 Laptop 8GB run Nemotron Mini 4B for coding?

For coding workloads, Nemotron Mini 4B on RTX 4070 Laptop 8GB receives a B grade with 56.0 tok/s and 4K context.

What context window can Nemotron Mini 4B use on RTX 4070 Laptop 8GB?

On RTX 4070 Laptop 8GB, 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 RTX 4070 Laptop 8GB See all hardware for Nemotron Mini 4B

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