Can Nemotron Nano 8B run on NVIDIA T4 16GB?

YES — Runs Great

S88Excellent

Estimated from fit model

Nemotron Nano 8B needs ~9.6 GB VRAM. NVIDIA T4 16GB has 16.0 GB. With Q4_K_M quantization, expect ~46 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: 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

Q4_K_M (Medium quality) — 9.6 GB, 45.8 tok/s, Runs well

9.6 GB required16.0 GB available

60% VRAM used

Fit status

Runs well

Decode

45.8 tok/s

TTFT

4225 ms

Safe context

68K

Memory

9.6 GB / 16.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsNemotron Nano 8B on NVIDIA T4 16GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort:

def quicksort(arr):
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

# Example usage
numbers = [3, 6, 8, 10, 1, 2, 1]
print(quicksort(numbers))  # [1, 1, 2, 3, 6, 8, 10]

The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]:
    """Sort a list using the quicksort algorithm.

    Args:
        arr: List of integers to sort.

    Returns:
        A new sorted list.
    """
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 45.8 tok/s decode · 4.2s TTFT (warm) · 115 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs well	45.8 tok/s	2305 ms	68K
Coding	S	Runs well	45.8 tok/s	4225 ms	68K
Agentic Coding	S	Runs well	45.8 tok/s	6146 ms	68K
Reasoning	S	Runs well	45.8 tok/s	4993 ms	68K
RAG	S	Runs well	45.8 tok/s	7682 ms	68K

Inference speed

Nemotron Nano 8B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Nemotron Nano 8B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~112 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	112.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	112.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	112.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	112.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	112.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	112.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	102.2	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	96.9	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	89.2	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	89.2	Fits
RTX 4070 12GB	12 GB	Q4_K_M	83.3	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	52.9	Fits
RTX 3060 12GB	12 GB	Q4_K_M	52.3	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	48.5	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	46.0	Fits
RTX 4060 8GB	8 GB	Q4_K_M	26.6	Heavy offload

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Nemotron Nano 8B (8B params) fits at each quantization level on NVIDIA T4 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A82
Q3_K_S	3	3.9 GB	Low	A83
NVFP4	4	4.5 GB	Medium	A83
Q4_K_M	4	4.9 GB	Medium	A84
Q5_K_M	5	5.8 GB	High	A85
Q6_K	6	6.6 GB	High	S85
Q8_0Best for your GPU	8	8.6 GB	Very High	S86
F16	16	16.4 GB	Maximum	F0

Get started

Copy-paste commands to run Nemotron Nano 8B on your machine.

Run

lms load Llama-3.1-Nemotron-Nano-8B-v1 && lms server start

Your hardware

More models your NVIDIA T4 16GB can run

Model	Params	Grade	Decode
Qwen 3.5 9B	9B	S	40.7 tok/s
Qwen 3 14B	14B	S	26.3 tok/s
Phi-4-reasoning-plus 14B	14.7B	S	24.9 tok/s
GPT-OSS 20B	21B	A	22.3 tok/s

Frequently asked questions

Can NVIDIA T4 16GB run Nemotron Nano 8B?

Yes, NVIDIA T4 16GB can run Nemotron Nano 8B with a S grade (Runs well). Expected decode speed: 45.8 tok/s.

How much VRAM does Nemotron Nano 8B need?

Nemotron Nano 8B (8B parameters) requires approximately 9.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Nemotron Nano 8B?

The recommended quantization for Nemotron Nano 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Nemotron Nano 8B run at on NVIDIA T4 16GB?

On NVIDIA T4 16GB, Nemotron Nano 8B achieves approximately 45.8 tokens per second decode speed with a time-to-first-token of 4225ms using Q4_K_M quantization.

Can NVIDIA T4 16GB run Nemotron Nano 8B for coding?

For coding workloads, Nemotron Nano 8B on NVIDIA T4 16GB receives a S grade with 45.8 tok/s and 68K context.

What context window can Nemotron Nano 8B use on NVIDIA T4 16GB?

On NVIDIA T4 16GB, Nemotron Nano 8B can safely use up to 68K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for NVIDIA T4 16GB See all hardware for Nemotron Nano 8B

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

45.8 tok/s

2305 ms

68K

Coding

Runs well

45.8 tok/s

4225 ms

68K

Agentic Coding

Runs well

45.8 tok/s

6146 ms

68K

Reasoning

Runs well

45.8 tok/s

4993 ms

68K

RAG

Runs well

45.8 tok/s

7682 ms

68K

Nemotron Nano 8B inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

RTX 5090 32GB

32 GB

Q4_K_M

112.0

Fits

RTX 4090 24GB

24 GB

Q4_K_M

112.0

Fits

RTX 4080 Super 16GB

16 GB

Q4_K_M

112.0

Fits

RTX 3090 24GB

24 GB

Q4_K_M

112.0

Fits

RX 7900 XTX 24GB

24 GB

Q4_K_M

112.0

Fits

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

112.0

Fits

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

102.2

Fits

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

96.9

Fits

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

89.2

Fits

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

89.2

Fits

RTX 4070 12GB

12 GB

Q4_K_M

83.3

Fits

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

52.9

Fits

RTX 3060 12GB

12 GB

Q4_K_M

52.3

Fits

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

48.5

Fits

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

46.0

Fits

RTX 4060 8GB

8 GB

Q4_K_M

26.6

Heavy offload

Quantization options

How Nemotron Nano 8B (8B params) fits at each quantization level on NVIDIA T4 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A82
Q3_K_S	3	3.9 GB	Low	A83
NVFP4	4	4.5 GB	Medium	A83
Q4_K_M	4	4.9 GB	Medium	A84
Q5_K_M	5	5.8 GB	High	A85
Q6_K	6	6.6 GB	High	S85
Q8_0Best for your GPU	8	8.6 GB	Very High	S86
F16	16	16.4 GB	Maximum	F0

Model

Params

Grade

Decode

Capabilities

Qwen 3.5 9B

40.7 tok/s

Qwen 3 14B

14B

26.3 tok/s

Phi-4-reasoning-plus 14B

14.7B

24.9 tok/s

GPT-OSS 20B

21B

22.3 tok/s

Frequently asked questions

Can NVIDIA T4 16GB run Nemotron Nano 8B?

Yes, NVIDIA T4 16GB can run Nemotron Nano 8B with a S grade (Runs well). Expected decode speed: 45.8 tok/s.

How much VRAM does Nemotron Nano 8B need?

Nemotron Nano 8B (8B parameters) requires approximately 9.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Nemotron Nano 8B?

The recommended quantization for Nemotron Nano 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Nemotron Nano 8B run at on NVIDIA T4 16GB?

On NVIDIA T4 16GB, Nemotron Nano 8B achieves approximately 45.8 tokens per second decode speed with a time-to-first-token of 4225ms using Q4_K_M quantization.

Can NVIDIA T4 16GB run Nemotron Nano 8B for coding?

For coding workloads, Nemotron Nano 8B on NVIDIA T4 16GB receives a S grade with 45.8 tok/s and 68K context.

What context window can Nemotron Nano 8B use on NVIDIA T4 16GB?

On NVIDIA T4 16GB, Nemotron Nano 8B can safely use up to 68K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.