Can Nemotron Nano 9B v2 run on Radeon Pro W7900 48GB?

YES — Runs Great

A77Great

Estimated from fit model

Nemotron Nano 9B v2 needs ~13.6 GB VRAM. Radeon Pro W7900 48GB has 48.0 GB. With Q4_K_M quantization, expect ~100 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 13.6 GB, 99.8 tok/s, Runs well

13.6 GB required48.0 GB available

28% VRAM used

Fit status

Runs well

Decode

99.8 tok/s

TTFT

1940 ms

Safe context

131K

Memory

13.6 GB / 48.0 GB

Memory breakdown

Weights5.5 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsNemotron Nano 9B v2 on Radeon Pro W7900 48GB
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: 99.8 tok/s decode · 1.9s TTFT (warm) · 250 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	99.8 tok/s	1058 ms	131K
Coding	A	Runs well	99.8 tok/s	1940 ms	131K
Agentic Coding	A	Runs well	99.8 tok/s	2821 ms	131K
Reasoning	A	Runs well	99.8 tok/s	2292 ms	131K
RAG	A	Runs well	99.8 tok/s	3526 ms	131K

Inference speed

Nemotron Nano 9B v2 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Nemotron Nano 9B v2 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~126 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	126.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	126.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	126.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	126.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	119.6	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	109.1	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	90.9	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	86.2	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	79.3	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	79.3	Fits
RTX 4070 12GB	12 GB	Q4_K_M	74.0	Tight
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	47.0	Fits
RTX 3060 12GB	12 GB	Q4_K_M	46.5	Tight
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	43.1	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	40.9	Fits
RTX 4060 8GB	8 GB	Q4_K_M	18.5	Too big

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 9B v2 (9B params) fits at each quantization level on Radeon Pro W7900 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	A71
Q3_K_S	3	4.4 GB	Low	A71
NVFP4	4	5.0 GB	Medium	A71
Q4_K_M	4	5.5 GB	Medium	A71
Q5_K_M	5	6.5 GB	High	A71
Q6_K	6	7.4 GB	High	A72
Q8_0	8	9.6 GB	Very High	A72
F16Best for your GPU	16	18.5 GB	Maximum	A75

Get started

Copy-paste commands to run Nemotron Nano 9B v2 on your machine.

Run

ollama run nemotron-nano:9b-v2

Your hardware

More models your Radeon Pro W7900 48GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	77.1 tok/s
Qwen 3.5 27B	27B	S	33.4 tok/s
Qwen 3.6 27B	27B	S	23.9 tok/s
Qwen 3.6 35B A3B	35B	S	64.8 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	79.7 tok/s

Frequently asked questions

Can Radeon Pro W7900 48GB run Nemotron Nano 9B v2?

Yes, Radeon Pro W7900 48GB can run Nemotron Nano 9B v2 with a A grade (Runs well). Expected decode speed: 99.8 tok/s.

How much VRAM does Nemotron Nano 9B v2 need?

Nemotron Nano 9B v2 (9B parameters) requires approximately 13.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Nemotron Nano 9B v2?

The recommended quantization for Nemotron Nano 9B v2 is Q4_K_M, which balances quality and memory efficiency.

What speed will Nemotron Nano 9B v2 run at on Radeon Pro W7900 48GB?

On Radeon Pro W7900 48GB, Nemotron Nano 9B v2 achieves approximately 99.8 tokens per second decode speed with a time-to-first-token of 1940ms using Q4_K_M quantization.

Can Radeon Pro W7900 48GB run Nemotron Nano 9B v2 for coding?

For coding workloads, Nemotron Nano 9B v2 on Radeon Pro W7900 48GB receives a A grade with 99.8 tok/s and 131K context.

What context window can Nemotron Nano 9B v2 use on Radeon Pro W7900 48GB?

On Radeon Pro W7900 48GB, Nemotron Nano 9B v2 can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for Radeon Pro W7900 48GB See all hardware for Nemotron Nano 9B v2

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/nemotron-nano-9b-v2-on-radeon-pro-w7900-48gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: