What GPU do you need to run NVIDIA Nemotron 3.5 Super VL 120B-A12B?
Weights-only fit: a new Mac Studio M5 Ultra ($5,499). No priced GPU card holds NVIDIA Nemotron 3.5 Super VL 120B-A12B (about 71.6 GB at Q4); this system holds it (weights-only fit, context unknown: no KV cache data; 0.4 GB left beyond the file and headroom), at about 87 tok/s estimate. The weights fit 8 of the 157 GPUs and systems we track; with no KV cache data, context is unknown on all of them.
124.45B parameters, 12B active (mixture-of-experts) (Hugging Face, accessed 2026-10-16). Q4_K_M GGUF: 70.1 GB (source). No context estimate: llm_config omits num_hidden_layers and the hybrid layer pattern (details).
Vision-language model; the parameter count includes the C-RADIO vision encoder. Image input needs the separate 1.73 GB mmproj file, which is not in the size.
Other places to get it
Checked Nov 14, 2026. We list only copies we opened and checked. Check file hashes for copies from other uploaders.
- ModelScope: Other uploaderNVIDIA community account; we found no NVIDIA page linking to it
- Ollama: Runs on your machine
ollama pull nemotron-3.5-super:120b-a12bListed on Ollama as nemotron-3.5-super
Weights-only fits, context unknown. We have no KV cache data for NVIDIA Nemotron 3.5 Super VL 120B-A12B, so every fit on this page only checks that the Q4_K_M file and headroom fit. How much context fits beside the weights is unknown, so none of these is a proven fit for any context length.
Single GPUs the weights fit, cheapest first
| Single GPU that fits | VRAM | Speed estimate | Max context estimate | Price |
|---|---|---|---|---|
| Apple Mac Studio M5 Ultra (64-core GPU, 96 GB) | 96 GB (~72 usable) | ~87 tok/s | Unknown (no KV data) | low $5,499 |
| Framework Desktop, AMD Ryzen AI Max+ PRO 495 (Strix Halo), 192 GB | 192 GB | ~29 tok/s | Unknown (no KV data) | low $6,799 |
| NVIDIA DGX Spark (128GB) | 128 GB | ~34 tok/s | Unknown (no KV data) | low $6,949.99 |
| NVIDIA RTX PRO 5000 Blackwell 72GB | 72 GB | ~135 tok/s | Unknown (no KV data) | No price yet |
| NVIDIA A100 80GB PCIe | 80 GB | ~176 tok/s | Unknown (no KV data) | No price yet |
| NVIDIA RTX PRO 6000 Blackwell Max-Q | 96 GB | ~167 tok/s | Unknown (no KV data) | No price yet |
| NVIDIA RTX PRO 6000 Blackwell Server Edition | 96 GB | ~155 tok/s | Unknown (no KV data) | No price yet |
| NVIDIA RTX PRO 6000 Blackwell Workstation Edition | 96 GB | ~167 tok/s | Unknown (no KV data) | No price yet |
Every device that runs it
Graphics cards, Macs and mini PCs from the wider hardware list behind My rig, smallest usable memory first. Not sure about yours? Check your exact rig.
Show all 24 devices that run it
| Device | Type | Memory | Speed estimate | Max context estimate | Price |
|---|---|---|---|---|---|
| Apple M2 Max (38-core GPU) 96GB | Mac | 96 GB (~72 usable) | ~42 tok/s | Unknown Tight | No price yet |
| Apple M3 Max (30-core GPU) 96GB | Mac | 96 GB (~72 usable) | ~33 tok/s | Unknown Tight | No price yet |
| Apple M3 Ultra 96GB | Mac | 96 GB (~72 usable) | ~70 tok/s | Unknown Tight | No price yet |
| Apple Mac Studio M5 Ultra (64-core GPU, 96 GB) | Mac | 96 GB (~72 usable) | ~87 tok/s | Unknown Tight | low $5,499 |
| NVIDIA RTX PRO 5000 Blackwell 72GB | Workstation GPU | 72 GB | ~135 tok/s | Unknown Tight | No price yet |
| NVIDIA A100 80GB PCIe | Data center GPU | 80 GB | ~176 tok/s | Unknown | No price yet |
| Apple M1 Ultra 128GB | Mac | 128 GB (~96 usable) | ~69 tok/s | Unknown | No price yet |
| Apple M2 Ultra 128GB | Mac | 128 GB (~96 usable) | ~69 tok/s | Unknown | No price yet |
| Apple M3 Max (40-core GPU) 128GB | Mac | 128 GB (~96 usable) | ~42 tok/s | Unknown | No price yet |
| Apple M4 Max (40-core GPU) 128GB | Mac | 128 GB (~96 usable) | ~53 tok/s | Unknown | No price yet |
| Apple M5 Max (40-core GPU) 128GB | Mac | 128 GB (~96 usable) | ~57 tok/s | Unknown | No price yet |
| NVIDIA RTX PRO 6000 Blackwell Max-Q | Workstation GPU | 96 GB | ~167 tok/s | Unknown | No price yet |
| NVIDIA RTX PRO 6000 Blackwell Server Edition | Data center GPU | 96 GB | ~155 tok/s | Unknown | No price yet |
| NVIDIA RTX PRO 6000 Blackwell Workstation Edition | Workstation GPU | 96 GB | ~167 tok/s | Unknown | No price yet |
| AMD Ryzen AI Max 385 (128GB) | Mini PC | 128 GB | ~27 tok/s | Unknown | No price yet |
| AMD Ryzen AI Max 390 (128GB) | Mini PC | 128 GB | ~27 tok/s | Unknown | No price yet |
| AMD Ryzen AI Max+ 395 (128GB) | Mini PC | 128 GB | ~27 tok/s | Unknown | low $3,499.99 |
| NVIDIA DGX Spark (128GB) | Mini PC | 128 GB | ~34 tok/s | Unknown | low $6,949.99 |
| NVIDIA RTX Spark N1X laptop (6144-core GPU, 128GB) | Laptop | 128 GB | ~37 tok/s | Unknown | No price yet |
| Apple M2 Ultra 192GB | Mac | 192 GB (~144 usable) | ~69 tok/s | Unknown | No price yet |
| Apple M3 Ultra 256GB | Mac | 256 GB (~192 usable) | ~70 tok/s | Unknown | No price yet |
| Apple M5 Ultra 256GB | Mac | 256 GB (~192 usable) | ~87 tok/s | Unknown | No price yet |
| Framework Desktop, AMD Ryzen AI Max+ PRO 495 (Strix Halo), 192 GB | Mini PC | 192 GB | ~29 tok/s | Unknown | low $6,799 |
| Apple M5 Ultra 512GB | Mac | 512 GB (~384 usable) | ~87 tok/s | Unknown | No price yet |
Multi-GPU: cards that need two or more
| Cards | Total VRAM | Speed estimate | Max context estimate |
|---|---|---|---|
| 3 × AMD Radeon RX 7900 XTX | 72 GB | ~87 tok/s | Unknown Tight |
| 3 × NVIDIA GeForce RTX 3090 | 72 GB | ~101 tok/s | Unknown Tight |
| 3 × NVIDIA GeForce RTX 4090 | 72 GB | ~107 tok/s | Unknown Tight |
| 3 × NVIDIA GeForce RTX 5090 | 96 GB | ~167 tok/s | Unknown |
| 2 × NVIDIA RTX A6000 | 96 GB | ~85 tok/s | Unknown |
Speed calculator
estimateEstimate = bandwidth ÷ GB read per token (the model's listed GGUF file: Q4_K_M for most models, otherwise the quant named in the list; scaled by active/total parameters for MoE), scaled by a per-platform efficiency and fixed per-token costs calibrated to 45 measured llama.cpp benchmarks (2025–26). Fit = file size + 1.5 GB headroom ≤ total VRAM (on Macs, the part of unified memory macOS lets the GPU use by default), and room for at least 4K tokens of context with an 8-bit KV cache. For a model with no KV cache data, only the file and headroom are checked, so a fit says nothing about context. Multi-GPU: VRAM adds, speed stays one card's (a layer split runs the cards in turn); max context uses the total. Speed is for short context. Max context = what is left after the file and a 1 GB runtime reserve ÷ KV cache bytes per token, default FP16 and 8-bit (q8_0) cache. How speed works · how context works