Datasets:
Benchmark Report: NVIDIA-Nemotron-3.5-Lightning-30B-A3B (Q4_K_M)
Date: 2026-08-12
Author: WITCHEER
Platform: NVIDIA GeForce RTX 5090 Benchmark Rig (capsule)
Model
| Field | Value |
|---|---|
| Model | NVIDIA-Nemotron-3.5-Lightning-30B-A3B |
| Parameters | 31.58 B total / 3 B active (nemotron_h hybrid MoE) |
| Quantization | Q4_K_M (source: lmstudio-community/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF) |
| File size | 22.83 GiB |
| Engine | llama.cpp (CUDA 12.8 (patched)) |
Hardware
| Component | Spec |
|---|---|
| GPU | NVIDIA GeForce RTX 5090 |
| CPU | AMD Ryzen 5 9600 |
| RAM | 64GB DDR5-5600 |
| OS | Ubuntu 26.04 LTS |
| CUDA | 12.8 (patched) |
Run context. GGUF leg: llama.cpp master worktree at build b10371 (5d16e81dd), built with the rig's pinned toolchain (gcc-14, nvcc 12.8), all layers resident (-ngl 99), no expert offload. Model released 2026-08-11; benched the same day. The NVFP4 leg below ran on the identical harness the following hours, against the official nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4 build via vLLM 0.25.1 on the native cutlass sm_120 FP4 path.
Quality Benchmarks
All benchmarks use generative evaluation via llama-server chat completions. Multiple-choice tasks (MMLU, ARC, HellaSwag) use letter extraction instead of loglikelihood scoring -- results are internally consistent for model comparison but absolute scores may differ from logprob-based evaluations by 5-15%.
Summary
| Benchmark | Score | Metric |
|---|---|---|
| MMLU | 77.93% | accuracy |
| ARC-Challenge | 92.15% | accuracy |
| HellaSwag | 80.64% | accuracy |
| HumanEval | 82.32% | pass@1 |
| GSM8K | 85.60% | exact_match |
MMLU Breakdown by Category
| Category | Score | Correct / Total |
|---|---|---|
| Stem | 74.78% | 1,127 / 1,507 |
| Humanities | 78.19% | 1,237 / 1,582 |
| Social Sciences | 86.45% | 1,429 / 1,653 |
| Other | 73.63% | 1,670 / 2,268 |
Sampled at 50% (seed 42)
Speed Benchmarks
Measured with llama-bench. All layers GPU-offloaded (-ngl 99).
Prompt Processing (tokens/s)
| Context Length | Speed | +/-sigma |
|---|---|---|
| 128 | 4,559 | 24.3 |
| 512 | 11,698 | 39.7 |
| 2048 | 11,693 | 69.3 |
| 4096 | 11,571 | 37.6 |
| 8192 | 11,440 | 13.3 |
| 16384 | 11,198 | 21.7 |
Generation (tokens/s)
| Metric | Speed | +/-sigma |
|---|---|---|
| tg128 | 377.4 | 1.7 |
NVFP4 second data point (official build, vLLM 0.25.1)
Same model, same card, same five-task harness (50% sample, seed 42, think-off): the official NVFP4 release (nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4, 21.6 GB weights) served through vLLM 0.25.1 on the native cutlass sm_120 FP4 path, versus this Q4_K_M GGUF through llama.cpp.
| Q4_K_M (llama.cpp) | NVFP4 (vLLM 0.25.1) | |
|---|---|---|
| MMLU | 77.93 | 77.72 |
| ARC-Challenge | 92.15 | 92.58 |
| HellaSwag | 80.64 | 81.40 |
| GSM8K | 85.60 | 86.96 |
| HumanEval | 82.32 | 81.71 |
| q_avg | 83.7 | 84.1 |
| decode (see note) | 377.4 tok/s (tg128) | 410.5 tok/s chat-server median (462.2 at 2k-token prompt) |
| weights on disk | 22.83 GiB | 21.6 GB |
Speed convention caveat: the two decode figures are not the same measurement — llama-bench tg128 vs chat-server completion throughput (completion tokens / (total − TTFT), 3-run medians). They support "same speed class, NVFP4 ahead on the native path", not a precise multiplier. Quality numbers ARE directly comparable (identical harness both legs).
Run integrity note: the NVFP4 quality pass crashed mid-GSM8K once with CUDA error: misaligned address (vLLM engine death); the three MC tasks completed before the crash and GSM8K + HumanEval were re-run clean on a fresh server with the identical recipe. Recorded as-is in quality_nvfp4.json.
Two sm_120 traps for reproducers: vLLM 0.25.1's flashinfer JIT fails with nvcc fatal: Unsupported gpu architecture 'compute_120f' unless CUDA_HOME points at CUDA 13 (the 12.8 nvcc doesn't know 120f); and the misaligned-address engine death happened once across the whole board run and did not recur on the rerun — treat it as an intermittent hazard on this stack, not a reproducible bug we can pin.
Speculative-decode leg: both shipped drafters measure negative
NVIDIA ships two speculative-decode drafter modules with this release (DFlash 1.18GB and an MTP head; community GGUF conversions in bartowski/...-GGUF as mtp-*.gguf). llama.cpp merged draft-dflash support for nemotron-3.5 the same day (PR #26905). Measured on this rig: base vs MTP (draft lengths 2/4/8) vs DFlash, chat-server conditions, four workloads (8 prompts x 256 tokens each, temperature 0), llama.cpp b10371.
| leg | prose | code | repetitive | chat |
|---|---|---|---|---|
| plain decoding | 328.6 | 335.4 | 335.1 | 335.3 |
| MTP n=2 | 239.0 (0.73x) | 256.0 (0.76x) | 260.1 (0.78x) | 248.7 (0.74x) |
| MTP n=4 | 230.6 (0.70x) | 252.9 (0.75x) | 247.8 (0.74x) | 235.1 (0.70x) |
| MTP n=8 | 186.9 (0.57x) | 201.9 (0.60x) | 211.9 (0.63x) | 195.4 (0.58x) |
| DFlash | 175.5 (0.53x) | 206.3 (0.62x) | 203.3 (0.61x) | 186.5 (0.56x) |
Every drafter configuration is slower than plain decoding on every workload, and more drafting makes it worse. The mechanism is visible in the acceptance logs: MTP n=2 acceptance is healthy (68-84%, mean accepted length ~2.5) — the drafts are good, they just cannot pay for themselves. At ~335 tok/s the target model produces a token every ~3ms, and the drafter's own forward passes plus verification overhead exceed what accepted tokens save. Longer drafts (n=8: acceptance falls to ~38-45%) and DFlash (36-44%, mean len ~2.1) add more rejected work on top.
Speculative decoding is a rescue for expensive decoders; a 3B-active MoE that already decodes at 335 tok/s on this card has nothing to rescue. Caveat: this is llama.cpp's day-1 dflash/nemotron implementation — NVIDIA's own TRT-LLM stack may show different economics, and these numbers say nothing about the drafters on slower cards, where the break-even moves. Raw data: results/<slug>/spec_decode.json. Conversion note for reproducers: converting the official DFlash safetensors to GGUF requires --target-model-dir pointing at a directory with the target model's tokenizer files, or convert_hf_to_gguf.py refuses.
Methodology
Evaluation Framework
Custom generative evaluators built for this rig. All benchmarks run through llama-server's /v1/chat/completions endpoint.
- Scoring: Generative evaluation (not loglikelihood)
- Thinking: disabled
- MCQ scoring: First valid letter extracted from response (A/B/C/D)
- Sampling: 50% of dataset used
- Temperature: 0 (deterministic)
- Max tokens: 2,048
- GPU offload: All layers (
-ngl 99)
Benchmarked by WITCHEER on the RTX 5090 Benchmark Rig. Source: github.com/notwitcheer/llm-bench-rig/blob/main/reports/nvidia-nemotron-3-5-lightning-30b-a3b-q4-k-m.md. Dataset: huggingface.co/datasets/witcheer/rtx-5090-benchmarks/blob/main/reports/nvidia-nemotron-3-5-lightning-30b-a3b-q4-k-m.md.