What quantization should I use for Qwen3-32B on a NVIDIA DGX Spark?

Q8_0 is the highest-quality quant that grades B or better on this hardware with llama.cpp at 8K context; the per-quant table on this page grades all 4 available quants.

Can an NVIDIA DGX Spark run Qwen3-32B?

Yes. Qwen3-32B needs 37.5GB at Q8_0 with llama.cpp and 8K context; the NVIDIA DGX Spark has 128GB of unified memory (~90% GPU-addressable) (114.7GB usable) — grade A, with decode speed in the painful tier.

NVIDIA DGX Spark · Qwen3-32B · Q8_0

✅ Fits — grade A

PainfulbetaNew hardware class — tier not yet calibrated

weights 34.8 GBKV cache 2.15 GBoverhead 0.52 GBpool 114.7 GBneeds 37.5 GB

assumes llama.cpp · batch 1 · 8K context · fp16 KV cache

Also runs with: Ollama (one-line install) · LM Studio (GUI) · KoboldCpp (GUI)

Rent a GPU that fits

It fits, but only in the Painful tier — usable for a batch job, rough for anything interactive. If you need it responsive, renting a larger GPU by the hour is worth pricing against an upgrade.

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Adjust assumptions — runtime, context, GPU count →

Every quant, graded

NVIDIA DGX Spark × Qwen3-32B, llama.cpp, 8K context.

Quant	File size	Fit	Speed tier
Q8_0	34.8 GB	Fits · A	painful · beta
Q6_K	26.9 GB	Fits · A	painful · beta
Q5_K_M	23.2 GB	Fits · A	painful · beta
Q4_K_M	19.8 GB	Fits · A	usable · beta

Qwen3-32B on similar hardware

More models on the NVIDIA DGX Spark

Verdict computed from published specs and measured GGUF file sizes — see how the math works.