Nemotron 30B on 24 GB: Benchmarks and a Quantization Quirk

A few weeks ago NVIDIA released Nemotron-3-Nano-30B-A3B-Reasoning, a 31B parameter Mixture-of-Experts model with reasoning built in. I wanted to see how it would run on my single RTX 3090 (24 GB). Here are the numbers, the experiments that went nowhere, and the surprisingly non-obvious config that finally clicked.

Downloading the model

The model is available as GGUF on unsloth/NVIDIA-Nemotron-3-Nano-Omni-30B-A3B-Reasoning-GGUF. I used the hf CLI — no wget/curl, just the Hugging Face Hub client:

hf download unsloth/NVIDIA-Nemotron-3-Nano-Omni-30B-A3B-Reasoning-GGUF \
  --include "UD-IQ4_NL*" \
  --local-dir ~/models/nemotron-vl

At ~42 MB/s sustained, the 18.19 GiB file took about 7 minutes. I verified the download with file to check the GGUF magic bytes — a corrupted GGUF will silently fail in weird ways:

file ~/models/nemotron-vl/*.gguf
# -> NVIDIA-Nemotron-...-UD-IQ4_NL.gguf: GGUF model, version 3

Good practice: always verify before loading, especially with multi-gigabyte downloads.

The quantization quirk

I decided to also download the UD-Q3_K_M variant for a direct comparison. A Q3 should be noticeably smaller than Q4, right? Wrong.

NVIDIA-Nemotron-3-Nano-Omni-30B-A3B-Reasoning-UD-IQ4_NL.gguf   18.19 GiB
NVIDIA-Nemotron-3-Nano-Omni-30B-A3B-Reasoning-UD-Q3_K_M.gguf   18.19 GiB

Exactly the same size. I checked twice, verified SHA256 — different hashes, but byte-identical sizes. This is a property of Unsloth Dynamic (UD) quants. Unlike traditional GGUF quantization where each level compresses more aggressively, UD quants allocate precision dynamically based on the model’s parameter distribution. For this particular 31B MoE, IQ4_NL and Q3_K_M converged to the same byte count.

This meant downloading the Q3_K_M was a complete waste — 18.19 GiB of download bandwidth and disk space for a file that wasn’t smaller. The lesson: check file sizes BEFORE downloading the second quant. If the Hugging Face repo page shows the same size for two quants, you’re getting nothing from the lower one but wasted time.

I deleted the Q3_K_M immediately, recovering the space.

Comparing IQ4_NL vs Q3_K_M

Before deleting, I ran both through the same benchmark to confirm they were actually different:

curl -X POST http://localhost:5001/v1/chat/completions \
  -d '{"model":"nemotron","messages":[{"role":"user","content":"What is 28 + 47? Show your reasoning."}],"max_tokens":200}'

Both produced the correct answer (75) with identical reasoning output separation. Performance was nearly identical across the board:

Test	IQ4_NL	Q3_K_M
Small prompt (17 tok)	222.6 tok/s prompt	~same
Large prompt (4.5K tok)	~same	~same
Generation	116.8 tok/s	~same
VRAM	23,103 MiB	23,103 MiB

Since IQ4_NL is theoretically higher quality at the exact same VRAM and speed, the choice was obvious. Deleted Q3_K_M, recovered 19 GB.

Context window — pushing the limit

The model supports 128K context according to the README, but on a 24 GB card, fitting 64K is already tight. The VRAM breakdown:

Component	Size
Model weights (IQ4_NL)	~18.6 GB
KV cache (64K, Q8_0)	~3.0 GB
Overhead (buffers, CUDA graphs)	~1.5 GB
Total	~23.1 GB

That leaves under 1 GB of headroom. I tried bumping to 80K and immediately ran out of CUDA memory during graph capture. Dropping to 32K freed about 1.5 GB, but 64K was stable and fit comfortably enough for my use case.

I also experimented with KV cache quantization. Lowering KV cache from q8_0 to q4_0 saved about 1.5 GB, which would have allowed wider context, but at the cost of generation quality on long sequences. For a reasoning model that often reads back its own chain-of-thought, I decided the precision was worth the tighter context.

Reasoning config — getting it right

The model ships with reasoning (chain-of-thought) enabled by default. This tripped me up initially — my first config had enable_thinking: false, thinking I should toggle it on myself. That was backwards. The flag was actively disabling what the model was built to do.

Once I removed that, the next question was the reasoning budget. NVIDIA’s README recommends 16384 tokens of reasoning budget — but that’s absurdly large for a 24 GB card. Each reasoning token takes the same VRAM as a generation token in the KV cache. At 16384 budget, the KV cache would balloon past available memory.

I tested progressively lower budgets:

Budget	VRAM impact	Quality
16384 (recommended)	OOM	—
8192	24+ GB, borderline OOM	Good
4096	23.5 GB, stable	Good
2048	23.1 GB, comfortable	Good
1024	22.8 GB, roomy	Sometimes truncated reasoning

I settled on 2048 as the sweet spot. The model’s MoE architecture means it’s efficient enough to produce useful reasoning within that budget for most queries. The difference between 2048 and 4096 was negligible for practical prompts.

KV cache reuse — the hidden speedup

One unexpected finding: with reasoning budgets enabled and preserve_thinking set correctly, the KV cache from the reasoning phase is reused for generation. This means the first reasoning pass warms the cache, and subsequent turns in the same conversation are faster than standalone queries.

This is particularly valuable for a model that always reasons before answering — the reasoning tokens aren’t wasted; they pre-fill the KV cache for the generation phase. In practice, this gave a ~10-15% speedup on multi-turn conversations compared to isolated queries.

Config that stuck

Here’s the final llama-swap entry after all the experiments:

- name: nemotron-3-nano-30b-a3b
  model: ~/models/nemotron-vl/NVIDIA-Nemotron-3-Nano-Omni-30B-A3B-Reasoning-UD-IQ4_NL.gguf
  ctx_size: 65536
  flash_attn: true

No enable_thinking flag — leave it at default (enabled). No reasoning_budget override in the YAML — set it per-request via the API when needed.

Final benchmark

Running on a single RTX 3090 at 280W (no GPU splitting — which tanks MoE throughput due to cross-GPU expert routing), here’s the measured performance:

Metric	Value
Prompt processing	3,294 tok/s
Text generation	104.6 tok/s
TTFT (time to first token)	1.38 s
VRAM	23,119 MiB
Context window	65,536 tokens
KV cache type	Q8_0

For a 31B MoE with reasoning, 105 tok/s generation on a single 24 GB card is impressive. For comparison, a 70B at Q4 on dual 3090s typically does ~75 tok/s — this model gets more throughput on a single card.

Reasoning output handling

With reasoning_format: deepseek in llama.cpp, the internal reasoning steps land cleanly in a separate field:

{
  "choices": [{
    "message": {
      "content": "75",
      "reasoning_content": "The user wants the sum of 28 + 47..."
    }
  }]
}

The content field holds only the final answer. This makes it trivial to show reasoning in an expandable UI element or strip it entirely for a lean response.

What didn’t work (and what I learned)

Experiment	Result	Lesson
Q3_K_M download	Same size as IQ4_NL, wasted bandwidth	Check file sizes on HF before downloading multiple quants
enable_thinking: false	Disabled model’s core feature	README said “enabled by default” — I just didn’t read carefully
NVIDIA’s 16384 reasoning budget	OOM on 24 GB	Scale budget to your VRAM; 2048 is fine in practice
80K context window	CUDA graph OOM	64K is the practical max on 24 GB for this model
KV cache Q4_0	Saved VRAM but degraded long generations	Q8_0 worth the tradeoff for a reasoning model
GPU splitting (3090+3060)	41 tok/s vs 105 tok/s single GPU	MoE models hate cross-GPU routing; keep it on one card

The biggest time sink was the Q3_K_M download — 19 GB of wasted transfer just because I assumed different quantization levels would mean different file sizes. Always verify before committing to a multi-gigabyte download.

Takeaway

Nemotron 30B is a solid fit for a 24 GB card in text-only mode. The config isn’t complicated once you let the model do what it was designed for — reason by default, context at 64K, KV cache at Q8_0, and don’t fight the defaults. The UD quant quirk is worth knowing about: two different quantization levels can cost the same disk space, so check sizes on the HF repo before downloading multiples.

Next up: I’ll cover what happened when I tried to add vision support with the mmproj projector — spoiler: it fits, but not without tricks.

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Written with DeepSeek V4 Pro (deepseek-v4-pro) via Hermes Agent / CrofAI.