Will It Run AI

Can Nemotron 70B run on RTX 6000 Ada 48GB?

BARELY — Tight on Memory

B59Good
Estimated from fit model

Nemotron 70B needs ~53.3 GB VRAM. RTX 6000 Ada 48GB has 48.0 GB. With Q4_K_M quantization, expect ~11 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: HighStack: StandardBottleneck: Host offload
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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) — 53.3 GB, 12.1 tok/s, Very compromised (needs ~4.2 GB host RAM)
53.3 GB required48.0 GB available
111% VRAM needed

5.3 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~4.2 GB host RAM)

Decode

12.1 tok/s

TTFT

16040 ms

Safe context

4K

Memory

53.3 GB / 48.0 GB

Offload

10%

Memory breakdown

Weights42.7 GB
KV Cache4.9 GB
Runtime0.9 GB
Headroom4.8 GB

See how fast it feels

See how fast it feelsNemotron 70B on RTX 6000 Ada 48GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step. Given information: - Speed = 60 mph (miles per hour) - Time = 2.5 hours Formula: Distance = Speed x Time Calculation: Distance = 60 mph x 2.5 h = 150 miles The train travels 150 miles. Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time. Breakdown: - First half: 1.25 hours at 60 mph = 75 miles - Stop: 20 minutes (no distance) - Second half: 1.25 hours at 60 mph = 75 miles Total distance: 150 miles (same) Total time: 2.5 h + 0.33 h = 2.83 hours Average speed: 150 / 2.83 = 53 mph The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 12.1 tok/s decode · 16.0s TTFT (warm) · 30 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly {ram} GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatBRuns with offload12.3 tok/s8620 ms4K
CodingBVery compromised11.1 tok/s17444 ms4K
Agentic CodingFToo heavy9.2 tok/s30516 ms4K
ReasoningBVery compromised11.1 tok/s20615 ms4K
RAGFToo heavy9.2 tok/s38145 ms4K

Quantization options

How Nemotron 70B (70B params) fits at each quantization level on RTX 6000 Ada 48GB (48.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
27.3 GB
LowB70
Q3_K_SBest for your GPU
3
34.3 GB
LowB69
NVFP4
4
39.2 GB
MediumF0
Q4_K_M
4
42.7 GB
MediumF0
Q5_K_M
5
50.4 GB
HighF0
Q6_K
6
57.4 GB
HighF0
Q8_0
8
74.9 GB
Very HighF0
F16
16
143.5 GB
MaximumF0

Get started

Copy-paste commands to run Nemotron 70B on your machine.

Run

ollama run nemotron

Opciones de mejora

Hardware que ejecuta bien Nemotron 70B

NVIDIANVIDIA A16 64GBOpción económica
64 GB VRAM (+16)
B
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.11.9 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$6,500 MSRP

NVIDIARTX PRO 6000 Blackwell Workstation Edition 96GBMejor relación calidad-precio
96 GB VRAM (+48)1792 GB/s (+832)
A
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.38.3 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Sube la velocidad estimada de decodificación alrededor de un 217%.

~$9,999 MSRP

NVIDIARTX PRO 6000 Blackwell Server Edition 96GBMejora NVIDIA
96 GB VRAM (+48)1597 GB/s (+637)
A
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.34.2 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Sube la velocidad estimada de decodificación alrededor de un 183%.

~$9,999 MSRP

Frequently asked questions

Can RTX 6000 Ada 48GB run Nemotron 70B?

Yes, RTX 6000 Ada 48GB can run Nemotron 70B with a B grade (Very compromised). Expected decode speed: 11.1 tok/s.

How much VRAM does Nemotron 70B need?

Nemotron 70B (70B parameters) requires approximately 53.3 GB of memory with Q4_K_M quantization.

What is the best quantization for Nemotron 70B?

The recommended quantization for Nemotron 70B is Q4_K_M, which balances quality and memory efficiency.

What speed will Nemotron 70B run at on RTX 6000 Ada 48GB?

On RTX 6000 Ada 48GB, Nemotron 70B achieves approximately 11.1 tokens per second decode speed with a time-to-first-token of 17444ms using Q4_K_M quantization.

Can RTX 6000 Ada 48GB run Nemotron 70B for coding?

For coding workloads, Nemotron 70B on RTX 6000 Ada 48GB receives a B grade with 11.1 tok/s and 4K context.

What context window can Nemotron 70B use on RTX 6000 Ada 48GB?

On RTX 6000 Ada 48GB, Nemotron 70B can safely use up to 4K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Nemotron 70B feels slow on RTX 6000 Ada 48GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

See all results for RTX 6000 Ada 48GBSee all hardware for Nemotron 70B
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