Can NVIDIA DGX Spark (GB10) · 128 GB run Qwen3-Coder 30B-A3B Instruct?
128 GB unified · 273 GB/s memory bandwidth · Qwen3-Coder 30B-A3B Instruct has 30.5B parameters (3.3B active per token).
Short answer
Yes, excellent for chat and comfortable or better for every workload we test.
Best quantization for chat: Q8_0 (30 GB of weights) on Ollama. Generation: ≈55 tok/s, leaving about 88 GB free for your other apps.
How it rates for each workload
Same model and machine, different jobs. Agents and long documents send far bigger prompts than chat, so they need much more speed and memory.
| Workload | Verdict | Quant | Generation | First reply | Context | |
|---|---|---|---|---|---|---|
| Chatwith Open WebUI | Excellent | Q8_0 | ≈55 tok/s | under 1 s | 8K | Details |
| Coding questionswith Continue | Excellent | Q8_0 | ≈50 tok/s | ≈2 s | 16K | Details |
| Coding in a repositorywith Aider | Comfortable | Q8_0 | ≈40 tok/s | ≈6 s | 32K | Details |
| Agentic codingwith OpenCode | Comfortable | Q8_0 | ≈35 tok/s | ≈9 s | 32K | Details |
| Long documents (64K)with Open WebUI | Excellent | Q8_0 | ≈25 tok/s | ≈30 s | 64K | Details |
Speeds come from measured benchmarks for this chip. “First reply” is the time to read the workload’s opening prompt. How we calculate
Get it running
Copy-paste commands for the Q8_0 build with a 64K context. Ollama and llama.cpp download the file straight from Hugging Face.
ollama run hf.co/unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q8_0
Downloads 32.48 GB (Q8_0). Ollama's default context is small: set OLLAMA_CONTEXT_LENGTH=65536 before `ollama serve` for long prompts.
Other models for NVIDIA DGX Spark (GB10) · 128 GB
The best-rated models for chat on this machine.
- gpt-oss-120b≈55 tok/sExcellent
- Qwen3.6 35B-A3B60–80 tok/sExcellent
- Qwen3-Next 80B-A3B Instruct60–80 tok/sExcellent
- Gemma 4 26B-A4B45–60 tok/sExcellent
- Qwen3.5 35B-A3B60–80 tok/sExcellent
- LFM2.5 24B-A2B120–160 tok/sExcellent
Cheapest machines for Qwen3-Coder 30B-A3B Instruct
Hardware rated comfortable or better for chat, lowest price first.
- Laptop · Ryzen AI 9 HX 370 (Radeon 890M) · 32 GB$1,300 · 20–35 tok/sComfortable
- Laptop · Core Ultra 7 258V (Arc 140V) · 32 GB$1,300 · 17–30 tok/sComfortable
- Mac mini M4 32 GB$1,399 · 25–35 tok/sExcellent
- Desktop PC · Ryzen 9 9950X (no GPU) · 64 GB DDR5$1,400 · 11–18 tok/sComfortable
- Desktop PC · Intel Arc Pro B60 24GB · 32 GB RAM$1,500 · 60–95 tok/sExcellent
- Ryzen AI Max+ 395 mini PC / Framework Desktop · 64 GB$1,600 · 50–65 tok/sExcellent
Same chip, other memory: 64 GB
Different tool, context size or workload?
These pages use typical settings. The full check models your exact setup.