Nemotron 3 Super 120B-A12B on Kunlunxin P800 64GB (昆仑芯) (64GB) — 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 Kunlunxin P800 64GB (昆仑芯) (~25 tok/s decode).
Recommended Hardware for Current Configuration
Production Launch Commands
llama-server -hf unsloth/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF:UD-Q4_K_XL \ --ctx-size 8192 -ngl 999 --flash-attn
vllm serve nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16 \ --max-model-len 8192 --gpu-memory-utilization 0.92
Cloud GPU Comparison & Rent-vs-Buy TCO
Verified public rates across RunPod, Vast.ai, Vultr, and DigitalOcean.
| 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
Engineering Sizing Notes & Optimization Playbook
Quick Hardware Verdict
A single Kunlunxin P800 64GB (昆仑芯) (64GB) cannot hold 4-bit Nemotron 3 Super 120B-A12B (78.81 GiB at 8K) entirely in VRAM. Use multi-GPU tensor parallelism, offload layers to system RAM, or deploy on a larger cloud GPU.
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)
Quantization Precision & Memory Matrix — Nemotron 3 Super 120B-A12B @ 8K
| Quantization Format | Weights (GiB) | KV Cache | Total VRAM (GiB) | Decode Speed | Compatibility (Kunlunxin P800 64GB (昆仑芯)) | Recommended GPU | Data Source |
|---|---|---|---|---|---|---|---|
| Native Weights (BF16 / FP8 / MXFP4 — Lossless) (BF16) | 230.24 GiB | 0.23 GiB | 231.03 GiB (248.07 GB) | ~3 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) | ~8.3 tok/s | Exceeds Available GPU Memory | 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) | ~10.4 tok/s | Exceeds Available GPU Memory | 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) | ~12 tok/s | Exceeds Available GPU Memory | 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) | ~25 tok/s | Exceeds Available GPU Memory | 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) | ~101.9 tok/s | Tight Fit (<8% 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) | ~105.4 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) | ~115.5 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 Kunlunxin P800 64GB (昆仑芯) (64GB)?
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 60.8 GiB usable on Kunlunxin P800 64GB (昆仑芯), delivering ~25 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).