Nemotron 3.5 Lightning 30B-A3B
30B-A3BNVIDIA Nemotron
The small member of NVIDIA's Nemotron 3.5 family: a 30B text-only mixture-of-experts model with about 3B parameters active per token. It generates at roughly the speed of a 3B model. Only 6 of its 52 layers keep a growing cache, which makes long context almost free: at Q4_0 (ggml-org's 18.9 GB file) it needs about 19.8 GB at 4K, 20.6 GB at 128K and 21.4 GB at its full 256K window. That is comfortable on a 24 GB card up to 128K and tight only at the full window; a 16 GB card cannot hold it. Q8_0 (about 35 GB) wants a 48 GB card or a 64 GB Mac. An optional 1.2 GB draft file for speculative decoding ships separately. Sizes come from the published GGUF files; NVIDIA's benchmark claims are its own and no per-level quality loss has been published.
262K
Max Context
2
Quant Variants
GGUF Q8_0
Best Quality
—
Accuracy Retained
Quantization Variants
Per-quant VRAM, quality loss, and inference speed on RTX 4090
Measured = run on this site · Estimated = not run here: calculated, or a figure this site has not verified · Community = public reports
Similar models
Compare with Nemotron 3.5Nemotron 3.5 Bolt 74B-A9B
NVIDIA Nemotron
NVIDIA's Nemotron 3.5 Bolt is a 74B mixture-of-experts model with about 9B parameters active per token, built to fit a 24 GB card. It is compressed from Nemotron 3.5 Super VL and ships in one build, NVIDIA's own native 2-bit file of 22.0 GB. Only 8 of its 88 layers keep a growing cache, so context costs little: about 23.1 GB at 4K, 24.1 GB at 128K and 25.2 GB at its full 256K window. That is the 24 GB NVIDIA designed for, but over this site's comfortable line, so a 24 GB card reads as tight; a 32 GB card or a 48 GB Mac runs it comfortably. The image encoder is a separate 1.7 GB file. NVIDIA's benchmark claims are its own, and no quality loss for the 2-bit build has been published.
Nemotron 3.5 Super VL 120B-A12B
NVIDIA Nemotron
NVIDIA's Nemotron 3.5 Super VL is a 120B mixture-of-experts vision-language model with about 12B parameters active per token. It is built for coding, tool use and document work, with a context window of up to 1M tokens. Only 8 of its 88 layers keep a growing cache, so even the full window is cheap: at Q4_0 (ggml-org's 71.8 GB file) it needs about 75 GB at 4K and 84 GB at 1M. That runs comfortably on a 128 GB Mac or a DGX Spark 128G, and tightly on one 80 GB card. Q8_0 (128 GB) wants a 256 GB Mac. The vision encoder is a separate 1–1.7 GB file. Sizes come from the published GGUF files; NVIDIA's benchmark claims are its own and no per-level quality loss has been published.
Seed-OSS 36B Instruct
ByteDance Seed
Dense 36B with a native 512K context. Q4 weights are ~22GB, which lands on a 24GB card or a 2×16GB split — but the context is the real cost: 512K of KV cache is roughly 128GB on its own, so budget context first and weights second.
Qwen3.8 27B
Alibaba Qwen3.8
Hybrid attention: only 16 of its 64 layers keep a KV cache, which is why a 27B model holds a 262K context in about 16GB of cache rather than 64GB. Sizing it as a conventional stack overstates the cache fourfold — the estimates here count the 16 and match published measurements at 8K, 32K and 262K. Quality loss per level has not been published.
Running Nemotron 3.5 Lightning 30B-A3B locally
At Q4_0 and 4K of context, Nemotron 3.5 Lightning 30B-A3B needs about 19.8 GB — 18.0 GB of weights, 0.02 GB of KV cache and a 1.8 GB activation buffer. The smallest cards in this index that clear that comfortably are 24 GB ones — 4 of them, from the RTX 3090 up — and 31 of the 78 cards here do. These are calculated figures, not measurements: the estimate stops counting a card as comfortable at 88% of its VRAM, which is roughly the room a desktop session needs.
What longer context costs
Going from 4K to 32K adds about 0.17 GB, taking the total to 20.0 GB. That is gentler than the parameter count suggests: only 6 of 52 layers keep a KV cache that grows with context, so the cache scales at a fraction of the usual rate. The model's native window is 256K; holding all of it at Q4_0 would need about 21 GB.
Which build to download
This index only tracks Nemotron 3.5 Lightning 30B-A3B in GGUF, across 2 levels (Q4_0, Q8_0). No per-level perplexity sweep has been published for this model, so the quality column is empty rather than estimated — within one model, more bits per weight is the only ordering the data supports. No throughput has been measured for this model on this site's hardware. GGUF runs on llama.cpp and Ollama across NVIDIA, AMD and Apple silicon; AWQ and GPTQ target vLLM on CUDA and ROCm; EXL2 is ExLlamaV2 — CUDA only, and now archived, so local use rather than serving.
Common questions
- How much VRAM does Nemotron 3.5 Lightning 30B-A3B need?
- About 19.8 GB at Q4_0 with 4K of context and batch 1, rising to roughly 20.0 GB at 32K. That figure is weights plus KV cache plus a 10% activation buffer, calculated from the model's architecture rather than measured on a card.
- Will Nemotron 3.5 Lightning 30B-A3B run on a 24GB GPU?
- Yes — at Q4_0 and 4K context it needs about 19.8 GB, which leaves 4.2 GB spare on a RTX 3090, or on any of the other 24 GB cards here (4 in all). That is the smallest size in this index that clears it comfortably; 31 of 78 do.
- Which quantization of Nemotron 3.5 Lightning 30B-A3B should I use?
- No published quality comparison exists for this model, so pick by footprint: Q4_0 is the 4-bit build this page is sized at, and a higher bits-per-weight level is more faithful. All 2 levels in the index are Q4_0, Q8_0. Note this is a mixture-of-experts model — all parameters must be resident even though only a fraction are active per token, so the memory cost follows the total, not the active count.
Where this model fits
Sized at Q4_0 with a 4K context window, smallest card first. Comfortable means the estimate uses at most 88% of the memory.
- Runs comfortably on
- RTX 309024GB · +4.2Radeon RX 7900 XTX24GB · +4.2RTX 409024GB · +4.2Tesla P40 24G24GB · +4.2Mac M5 32G32GB · +4.2
- Tight but possible
- Radeon RX 7900 XT20GB
- Ships in
- GGUF
- Try it yourself
- Size it in the calculatorGenerate the run command
How to actually run this
Deployment guides for this model and this class of hardware.
This page's figures change when the model or the runtime does.Last updated 2026-10-18 RSS → /feed.xml