Nemotron 3 Super 120B-A12B on Ryzen AI Max+ 395 128GB (128GB) — VRAM, KV Cache & Throughput

Nemotron 3 Super 120B-A12B (123.61B total, 12B active, Hybrid Linear/Mamba + MoE) uses 78.02 GiB of 4-bit weights (UD-Q4_K_XL), requiring 78.81 GiB total at 8K context and 78.99 GiB at 32K on Ryzen AI Max+ 395 128GB (~17.1 tok/s decode).

Interactive Sizing Workbench

Full Compatibility Report →
128 GB · 256 GB/s
Fits with Safe Headroom
Measured GGUF shard size HF config.json ↗
Memory Allocation Breakdown (GiB) 78.81 GiB (84.62 GB) / 128 GiB (62%)
Model Weights: 78.02 GiB KV Cache & State: 0.23 GiB Runtime & Activation Buffers: 0.56 GiB System RAM Offload: 0 GiB
Required GPU Memory
78.81 GiB
84.62 GB (decimal)
Usable Hardware Memory
99.8 GiB
128 GB (physical)
Safe Headroom
+20.99 GiB
62% utilization
Est. Decode Throughput
~17.1 tok/s
12B active params
Est. Prefill Throughput
~1032 tok/s
4K Prompt TTFT: ~3967 ms
Est. Cost / 1M Tokens
$27.18
Hybrid Linear/Mamba + MoE · NVIDIA Open

Recommended Hardware for Current Configuration

Best Single GPU
RTX PRO 6000 Blackwell (96GB)
~$8,500
Best Value / Used GPU
2x A100 80GB SXM
160GB · ~$24,000
Apple Unified Memory
Ryzen AI Max+ 395 128GB
~$1,999
Cloud From
Vast.ai · $1.67 / hr
1x RTX PRO 6000 Blackwell

Production Launch Commands

llama.cpp (GGUF)
llama-server -hf unsloth/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF:UD-Q4_K_XL \
  --ctx-size 8192 -ngl 999 --flash-attn
vLLM
vllm serve nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16 \
  --max-model-len 8192 --gpu-memory-utilization 0.92
Live Token Speed Simulator (TTFT ~3967ms · ~17.1 tok/s)
[Nemotron 3 Super 120B-A12B · 78.81 GiB] allocated on target GPU. Prefill completed in TTFT window; streaming autoregressive tokens at memory-bandwidth-bound rate...

Cloud GPU Comparison & Rent-vs-Buy TCO

Verified public rates across RunPod, Vast.ai, Vultr, and DigitalOcean.

Verified: 2026-11-04
Purchase break-even
~44.1 months
Est. local power: $12.77 / mo
Provider Recommended Instance Total VRAM Hourly Rate Monthly at Selected Hours Billing Mode Deploy
RunPod 1x RTX PRO 6000 Blackwell (Community Cloud) 96 GB $1.69 / hr $205.50 / mo Per-second Deploy GPU ↗
Vast.ai 1x RTX PRO 6000 Blackwell (Verified Host) 96 GB $1.67 / hr $203.44 / mo Spot / On-demand Deploy GPU ↗
Vultr ($300 Free Credit*) 2x L40S 48GB (Dedicated Instance · $300 Credit*) 96 GB $3.34 / hr $406.39 / mo Hourly Deploy GPU ↗
Vultr Cloud GPU 2x L40S 48GB (Dedicated Instance) 96 GB $3.34 / hr $406.39 / mo Hourly Deploy GPU ↗
DigitalOcean 1x Instinct MI300X 192GB (Dedicated Instance) 192 GB $2.59 / hr $314.94 / mo Per-second Deploy GPU ↗

Rates reflect public listings on the verification date and fluctuate with regional inventory.

Fine-Tuning VRAM Requirements (QLoRA / LoRA / Full) — Nemotron 3 Super 120B-A12B

QLoRA (4b) Fits with Safe Headroom
80.78 GiB
Model Weights: 78.02 GiB · Gradients + Optimizer: 1.68 GiB · Act: 1.08 GiB
Recommended GPU: RTX PRO 6000 Blackwell (96GB)
LoRA (16b) Exceeds Available GPU Memory
234.31 GiB
Model Weights: 230.24 GiB · Gradients + Optimizer: 2.64 GiB · Act: 1.43 GiB
Recommended GPU: Instinct MI325X 256GB (256GB)
Full BF16 Exceeds Available GPU Memory
946.5 GiB
Model Weights: 230.24 GiB · Gradients + Optimizer: 713.74 GiB · Act: 2.52 GiB
Recommended GPU: 8x H200 141GB SXM (1128GB)

Engineering Sizing Notes & Optimization Playbook

Quick Hardware Verdict

Ryzen AI Max+ 395 128GB (128GB physical, 99.8 GiB usable) runs 4-bit (UD-Q4_K_XL) Nemotron 3 Super 120B-A12B at 8K context using 78.81 GiB with 20.99 GiB of safe headroom (~17.1 tok/s single-stream decode).

KV Cache & Runtime Tuning

Nemotron 3 Super 120B-A12B uses Hybrid Linear/Mamba + MoE (123.61B total, 12B active per token). Moving from 8K to 32K context expands FP16 KV cache from 0.23 GiB to 0.41 GiB; enabling 8-bit KV cache (llama.cpp -ctk q8_0 -ctv q8_0 or vLLM --kv-cache-dtype fp8) saves 0.12 GiB at 32K (reducing total VRAM to 78.87 GiB).

VRAM Scaling Across Context Windows (4K to 128K, 4-bit Weights)

Nemotron 3 Super 120B-A12B VRAM scaling from 4K to 128K context (4K: 78.78 GiB, 8K: 78.81 GiB, 32K: 78.99 GiB, 128K: 79.74 GiB) 4K 78.78 GiB (KV 0.2 GiB) 8K 78.81 GiB (KV 0.23 GiB) 32K 78.99 GiB (KV 0.41 GiB) 128K 79.74 GiB (KV 1.16 GiB) Ryzen AI Max+ 395 128GB (128GB)
Model weights remain constant while attention KV cache grows with context length and batch size.

Quantization Precision & Memory Matrix — Nemotron 3 Super 120B-A12B @ 8K

Quantization Format Weights (GiB) KV Cache Total VRAM (GiB) Decode Speed Compatibility (Ryzen AI Max+ 395 128GB) Recommended GPU Data Source
Native Weights (BF16 / FP8 / MXFP4 — Lossless) (BF16) 230.24 GiB 0.23 GiB 231.03 GiB (248.07 GB) ~3.4 tok/s Exceeds Available GPU Memory Instinct MI325X 256GB (256GB) Official checkpoint size
FP8 / GGUF Q8_0 (8-bit — Near-Lossless) (Q8_0) 119.65 GiB 0.23 GiB 120.44 GiB (129.32 GB) ~11.3 tok/s Tight Fit (<8% Headroom) Instinct MI300X 192GB (192GB) Measured GGUF shard size
GGUF Q6_K (6-bit — High Precision) (Q6_K) 106.87 GiB 0.23 GiB 107.66 GiB (115.6 GB) ~12.7 tok/s Tight Fit (<8% Headroom) Instinct MI300X 192GB (192GB) Measured GGUF shard size
GGUF Q5_K_M (5-bit — Balanced) (Q5_K_M) 99.96 GiB 0.23 GiB 100.75 GiB (108.18 GB) ~13.5 tok/s Tight Fit (<8% Headroom) Instinct MI300X 192GB (192GB) Measured GGUF shard size
Q4_K_M / UD-Q4_K_XL / AWQ (4-bit — Recommended) (UD-Q4_K_XL) 78.02 GiB 0.23 GiB 78.81 GiB (84.62 GB) ~17.1 tok/s Fits with Safe Headroom RTX PRO 6000 Blackwell (96GB) Measured GGUF shard size
GGUF IQ4_XS (4.25-bit — Compact) (IQ4_XS) 60.06 GiB 0.23 GiB 60.85 GiB (65.34 GB) ~21.9 tok/s Fits with Safe Headroom RTX PRO 6000 Blackwell (96GB) Measured GGUF shard size
GGUF Q3_K_M (3-bit — Low VRAM) (Q3_K_M) 57.47 GiB 0.23 GiB 58.26 GiB (62.56 GB) ~22.9 tok/s Fits with Safe Headroom RTX PRO 6000 Blackwell (96GB) Measured GGUF shard size
GGUF UD-Q2_K_XL (2-bit — Extreme MoE) (UD-Q2_K_XL) 50.9 GiB 0.23 GiB 51.69 GiB (55.5 GB) ~25.6 tok/s Fits with Safe Headroom RTX PRO 6000 Blackwell (96GB) Measured GGUF shard size

Technical Specifications & FAQ

Does Nemotron 3 Super 120B-A12B fit on Ryzen AI Max+ 395 128GB (128GB)?

In 4-bit (UD-Q4_K_XL) at 8,192 context, Nemotron 3 Super 120B-A12B requires 78.81 GiB total GPU memory (78.02 GiB weights + 0.23 GiB KV cache + 0.56 GiB runtime buffers) against 99.8 GiB usable on Ryzen AI Max+ 395 128GB, delivering ~17.1 tok/s decode.

How much VRAM does Nemotron 3 Super 120B-A12B need at 32K context?

At 32,768 context tokens, Nemotron 3 Super 120B-A12B (Hybrid Linear/Mamba + MoE) allocates 0.41 GiB for KV cache and state, bringing total 4-bit VRAM to 78.99 GiB (84.81 GB decimal).

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